Network Support

src/modelskill/__init__.py

@@ -38,13 +38,10 @@
     TrackModelResult,
     GridModelResult,
     DfsuModelResult,
+    NetworkModelResult,
     DummyModelResult,
 )
-from .obs import (
-    observation,
-    PointObservation,
-    TrackObservation,
-)
+from .obs import observation, PointObservation, TrackObservation, NodeObservation
 from .matching import from_matched, match
 from .configuration import from_config
 from .settings import options, get_option, set_option, reset_option, load_style
@@ -94,9 +91,11 @@ def load(filename: Union[str, Path]) -> Comparer | ComparerCollection:
     "GridModelResult",
     "DfsuModelResult",
     "DummyModelResult",
+    "NetworkModelResult",
     "observation",
     "PointObservation",
     "TrackObservation",
+    "NodeObservation",
     "TimeSeries",
     "match",
     "from_matched",

src/modelskill/comparison/_collection.py

@@ -10,7 +10,6 @@
     Iterator,
     List,
     Union,
-    Optional,
     Mapping,
     Iterable,
     overload,
@@ -286,13 +285,13 @@ def merge(
 
     def sel(
         self,
-        model: Optional[IdxOrNameTypes] = None,
-        observation: Optional[IdxOrNameTypes] = None,
-        quantity: Optional[IdxOrNameTypes] = None,
-        start: Optional[TimeTypes] = None,
-        end: Optional[TimeTypes] = None,
-        time: Optional[TimeTypes] = None,
-        area: Optional[List[float]] = None,
+        model: IdxOrNameTypes | None = None,
+        observation: IdxOrNameTypes | None = None,
+        quantity: IdxOrNameTypes | None = None,
+        start: TimeTypes | None = None,
+        end: TimeTypes | None = None,
+        time: TimeTypes | None = None,
+        area: List[float] | None = None,
         **kwargs: Any,
     ) -> "ComparerCollection":
         """Select data based on model, time and/or area.
@@ -572,7 +571,7 @@ def gridded_skill(
         binsize: float | None = None,
         by: str | Iterable[str] | None = None,
         metrics: Iterable[str] | Iterable[Callable] | str | Callable | None = None,
-        n_min: Optional[int] = None,
+        n_min: int | None = None,
         **kwargs: Any,
     ) -> SkillGrid:
         """Skill assessment of model(s) on a regular spatial grid.

src/modelskill/comparison/_collection_plotter.py

@@ -6,7 +6,6 @@
     List,
     Literal,
     Mapping,
-    Optional,
     Sequence,
     Tuple,
     Union,
@@ -60,19 +59,19 @@ def scatter(
         quantiles: int | Sequence[float] | None = None,
         fit_to_quantiles: bool = False,
         show_points: bool | int | float | None = None,
-        show_hist: Optional[bool] = None,
-        show_density: Optional[bool] = None,
-        norm: Optional[colors.Normalize] = None,
+        show_hist: bool | None = None,
+        show_density: bool | None = None,
+        norm: colors.Normalize | None = None,
         backend: Literal["matplotlib", "plotly"] = "matplotlib",
         figsize: Tuple[float, float] = (8, 8),
-        xlim: Optional[Tuple[float, float]] = None,
-        ylim: Optional[Tuple[float, float]] = None,
+        xlim: Tuple[float, float] | None = None,
+        ylim: Tuple[float, float] | None = None,
         reg_method: str | bool = "ols",
-        title: Optional[str] = None,
-        xlabel: Optional[str] = None,
-        ylabel: Optional[str] = None,
-        skill_table: Optional[Union[str, List[str], Mapping[str, str], bool]] = None,
-        ax: Optional[Axes] = None,
+        title: str | None = None,
+        xlabel: str | None = None,
+        ylabel: str | None = None,
+        skill_table: Union[str, List[str], Mapping[str, str], bool] | None = None,
+        ax: Axes | None = None,
         **kwargs,
     ) -> Axes | list[Axes]:
         """Scatter plot tailored for comparing model output with observations.
@@ -190,17 +189,17 @@ def _scatter_one_model(
         quantiles: int | Sequence[float] | None,
         fit_to_quantiles: bool,
         show_points: bool | int | float | None,
-        show_hist: Optional[bool],
-        show_density: Optional[bool],
+        show_hist: bool | None,
+        show_density: bool | None,
         backend: Literal["matplotlib", "plotly"],
         figsize: Tuple[float, float],
-        xlim: Optional[Tuple[float, float]],
-        ylim: Optional[Tuple[float, float]],
+        xlim: Tuple[float, float] | None,
+        ylim: Tuple[float, float] | None,
         reg_method: str | bool,
-        title: Optional[str],
-        xlabel: Optional[str],
-        ylabel: Optional[str],
-        skill_table: Optional[Union[str, List[str], Mapping[str, str], bool]],
+        title: str | None,
+        xlabel: str | None,
+        ylabel: str | None,
+        skill_table: Union[str, List[str], Mapping[str, str], bool] | None,
         ax,
         **kwargs,
     ):
@@ -344,11 +343,11 @@ def hist(
         bins: int | Sequence = 100,
         *,
         model: str | int | None = None,
-        title: Optional[str] = None,
+        title: str | None = None,
         density: bool = True,
         alpha: float = 0.5,
         ax=None,
-        figsize: Optional[Tuple[float, float]] = None,
+        figsize: Tuple[float, float] | None = None,
         **kwargs,
     ):
         """Plot histogram of specific model and all observations.
@@ -409,11 +408,11 @@ def _hist_one_model(
         *,
         mod_name: str,
         bins: int | Sequence,
-        title: Optional[str],
+        title: str | None,
         density: bool,
         alpha: float,
         ax,
-        figsize: Optional[Tuple[float, float]],
+        figsize: Tuple[float, float] | None,
         **kwargs,
     ):
         from ._comparison import MOD_COLORS
@@ -464,7 +463,7 @@ def taylor(
         marker: str = "o",
         marker_size: float = 6.0,
         title: str = "Taylor diagram",
-    ) -> Optional[Figure]:
+    ) -> Figure | None:
         """Taylor diagram for model skill comparison.
 
         Taylor diagram showing model std and correlation to observation
@@ -783,8 +782,8 @@ def _residual_hist_one_model(
     def spatial_overview(
         self,
         ax=None,
-        figsize: Optional[Tuple] = None,
-        title: Optional[str] = None,
+        figsize: Tuple | None = None,
+        title: str | None = None,
     ) -> Axes:
         """Plot observation points on a map showing the model domain
 

src/modelskill/comparison/_comparer_plotter.py

@@ -3,7 +3,6 @@
     Literal,
     Union,
     List,
-    Optional,
     Tuple,
     Sequence,
     TYPE_CHECKING,
@@ -464,19 +463,19 @@ def scatter(
         quantiles: int | Sequence[float] | None = None,
         fit_to_quantiles: bool = False,
         show_points: bool | int | float | None = None,
-        show_hist: Optional[bool] = None,
-        show_density: Optional[bool] = None,
-        norm: Optional[colors.Normalize] = None,
+        show_hist: bool | None = None,
+        show_density: bool | None = None,
+        norm: colors.Normalize | None = None,
         backend: Literal["matplotlib", "plotly"] = "matplotlib",
         figsize: Tuple[float, float] = (8, 8),
-        xlim: Optional[Tuple[float, float]] = None,
-        ylim: Optional[Tuple[float, float]] = None,
+        xlim: Tuple[float, float] | None = None,
+        ylim: Tuple[float, float] | None = None,
         reg_method: str | bool = "ols",
-        title: Optional[str] = None,
-        xlabel: Optional[str] = None,
-        ylabel: Optional[str] = None,
-        skill_table: Optional[Union[str, List[str], Mapping[str, str], bool]] = None,
-        ax: Optional[matplotlib.axes.Axes] = None,
+        title: str | None = None,
+        xlabel: str | None = None,
+        ylabel: str | None = None,
+        skill_table: Union[str, List[str], Mapping[str, str], bool] | None = None,
+        ax: matplotlib.axes.Axes | None = None,
         **kwargs,
     ) -> matplotlib.axes.Axes | list[matplotlib.axes.Axes]:
         """Scatter plot tailored for model-observation comparison.
@@ -594,18 +593,18 @@ def _scatter_one_model(
         quantiles: int | Sequence[float] | None,
         fit_to_quantiles: bool,
         show_points: bool | int | float | None,
-        show_hist: Optional[bool],
-        show_density: Optional[bool],
-        norm: Optional[colors.Normalize],
+        show_hist: bool | None,
+        show_density: bool | None,
+        norm: colors.Normalize | None,
         backend: Literal["matplotlib", "plotly"],
         figsize: Tuple[float, float],
-        xlim: Optional[Tuple[float, float]],
-        ylim: Optional[Tuple[float, float]],
+        xlim: Tuple[float, float] | None,
+        ylim: Tuple[float, float] | None,
         reg_method: str | bool,
-        title: Optional[str],
-        xlabel: Optional[str],
-        ylabel: Optional[str],
-        skill_table: Optional[Union[str, List[str], Mapping[str, str], bool]],
+        title: str | None,
+        xlabel: str | None,
+        ylabel: str | None,
+        skill_table: Union[str, List[str], Mapping[str, str], bool] | None,
         **kwargs,
     ):
         """Scatter plot for one model only"""
@@ -746,7 +745,14 @@ def taylor(
         df = df.rename(columns={"_std_obs": "obs_std", "_std_mod": "std"})
 
         pts = [
-            TaylorPoint(name=r.model, obs_std=r.obs_std, std=r.std, cc=r.cc, marker=marker, marker_size=marker_size)
+            TaylorPoint(
+                name=r.model,
+                obs_std=r.obs_std,
+                std=r.std,
+                cc=r.cc,
+                marker=marker,
+                marker_size=marker_size,
+            )
             for r in df.itertuples()
         ]