diff --git a/docs/source/notebooks/mlm_x_sampling.ipynb b/docs/source/notebooks/mlm_x_sampling.ipynb new file mode 100644 index 00000000..f7724235 --- /dev/null +++ b/docs/source/notebooks/mlm_x_sampling.ipynb @@ -0,0 +1,3133 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0", + "metadata": { + "tags": [ + "differt-install-preamble", + "remove-cell", + "skip-execution" + ] + }, + "outputs": [], + "source": [ + "# Run this cell to install DiffeRT and its dependencies, e.g., on Google Colab\n", + "\n", + "try:\n", + " import differt # noqa: F401\n", + "except ImportError:\n", + " import sys # noqa: F401\n", + "\n", + " !{sys.executable} -m pip install differt[all] e3x jax_tqdm" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Radio Coverage Map from Generative Sampling-Assisted Ray Tracing\n", + "\n", + ":::{warning}\n", + "The content of this notebook is not yet ready to be published.\n", + ":::\n", + "\n", + "This notebook aims at being a tutorial to reproduce the results presented in the paper\n", + "*Comparing Differentiable and Dynamic Ray\n", + "Tracing: Introducing the Multipath Lifetime Map* {cite}`mlm-eucap2025`,\n", + "and assumes you are familiar with its content.\n", + "\n", + "**You can run it locally or with Google Colab** by cliking on the rocket\n", + "at the top of this page!\n", + "\n", + ":::{tip}\n", + "On Google Colab, make sure to select a GPU or TPU runtime for a faster experience.\n", + ":::\n", + "\n", + "If you find this tutorial useful and plan on using this tool for your publications,\n", + "please cite our work, see {ref}`citing`.\n", + "\n", + "## Summary\n", + "\n", + "In our work, we present the Multipath Lifetime Map (MLM), a visual tool, along with two metrics,\n", + "to help determining the scope of application of the Dynamic Ray Tracing (RT) method. For further details, please refer to the paper.\n", + "\n", + ":::{important}\n", + "The below implementation is far from being the most efficient, as it first aims at providing a nice visual output,\n", + "and it is tailored for users moving on a 2D grid.\n", + "\n", + "While our method extends to any number and kind of dynamic objects,\n", + "it may become extremily hard to provide a visual representation of the MLM,\n", + "especially for higher dimensions.\n", + ":::\n", + "\n", + "## Imports\n", + "\n", + "As our code includes some non-trivial plots, we need to import quite a few Python modules.\n", + "All of them should be installed with `differt[all]`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-17 17:38:31.995931: W external/xla/xla/service/gpu/nvptx_compiler.cc:930] The NVIDIA driver's CUDA version is 12.2 which is older than the PTX compiler version 12.6.85. Because the driver is older than the PTX compiler version, XLA is disabling parallel compilation, which may slow down compilation. You should update your NVIDIA driver or use the NVIDIA-provided CUDA forward compatibility packages.\n" + ] + } + ], + "source": [ + "import urllib.request\n", + "from collections.abc import Iterator\n", + "from functools import partial\n", + "from typing import Any\n", + "\n", + "import e3x\n", + "import equinox as eqx\n", + "import flax.linen as nn\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import numpy as np\n", + "import plotly.graph_objects as go\n", + "from beartype import beartype as typechecker\n", + "from jax_tqdm import scan_tqdm\n", + "from jaxtyping import Array, Bool, Float, Int, PRNGKeyArray, PyTree, jaxtyped\n", + "from plotly.colors import convert_to_RGB_255\n", + "from plotly.subplots import make_subplots\n", + "from tqdm.notebook import tqdm\n", + "\n", + "from differt.em import (\n", + " Dipole,\n", + " materials,\n", + " pointing_vector,\n", + " reflection_coefficients,\n", + " sp_directions,\n", + ")\n", + "from differt.geometry import (\n", + " Paths,\n", + " TriangleMesh,\n", + " normalize,\n", + ")\n", + "from differt.plotting import draw_image, draw_markers, reuse, set_defaults\n", + "from differt.scene import (\n", + " TriangleScene,\n", + " download_sionna_scenes,\n", + " get_sionna_scene,\n", + ")\n", + "from differt.utils import dot, sample_points_in_bounding_box" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "## Simple Urban Street Canyon Scenario\n", + "\n", + "Street canyons are probably one of the most common types of scenarios studied in RT,\n", + "due to their simplicity to model, but also important presence in big cities.\n", + "\n", + "As we provide a compatibility layer with Sionna scenes {cite}`sionna`,\n", + "we will simply load the `'simple_street_canyon'` scene.\n", + "\n", + "You can download all the scenes from the Sionna repository with\n", + "{func}`download_sionna_scenes`.\n", + "By default, they will be placed in a subfolder of the `differt` Python module." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "download_sionna_scenes() # Let's download Sionna scenes (from the main branch)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "facecolor": [ + [ + 0.21222999691963196, + 0.5647109746932983, + 0.7991030216217041 + ], + [ + 0.21222999691963196, + 0.5647109746932983, + 0.7991030216217041 + ], + [ + 0.21222999691963196, + 0.5647109746932983, + 0.7991030216217041 + ], + [ + 0.21222999691963196, + 0.5647109746932983, + 0.7991030216217041 + ], + [ + 0.21222999691963196, + 0.5647109746932983, + 0.7991030216217041 + ], + [ + 0.21222999691963196, + 0.5647109746932983, + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "set_defaults(\n", + " \"plotly\"\n", + ") # Our scene is simple, and Plotly is the best backend for online interactive plots :-)\n", + "\n", + "use_box = False # Set this to 'True' to work on an even simpler scene\n", + "\n", + "if use_box:\n", + " mesh = TriangleMesh.box(length=10.0, width=4.0, height=3.0)\n", + " base_scene = TriangleScene(mesh=mesh)\n", + "else:\n", + " file = get_sionna_scene(\"simple_street_canyon\")\n", + " base_scene = TriangleScene.load_xml(file)\n", + "\n", + "sample_quads = (\n", + " False # You can set this to 'True' to sample quadrilaterals faces instead\n", + ")\n", + "# Otherwise, triangular faces are sampled\n", + "base_scene = base_scene.set_assume_quads(sample_quads)\n", + "base_scene.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "In the cell below (hidden by default), we define quite a few utility functions to\n", + "get nice plots, but this code is not needed if you are only interested in the metrics,\n", + "and not the visual output." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "def draw_mesh_2d(mesh: TriangleMesh, figure: go.Figure) -> None:\n", + " assert mesh.object_bounds is not None\n", + "\n", + " for i, j in mesh.object_bounds:\n", + " sub_mesh = mesh[i:j]\n", + "\n", + " (xs, ys, (_, z_max)) = sub_mesh.bounding_box.T\n", + "\n", + " layer = \"below\" if z_max < 1e-6 else None\n", + "\n", + " assert sub_mesh.face_colors is not None\n", + " color = convert_to_RGB_255(sub_mesh.face_colors[0, :])\n", + "\n", + " figure.add_shape(\n", + " type=\"rect\",\n", + " x0=xs[0],\n", + " y0=ys[0],\n", + " x1=xs[1],\n", + " y1=ys[1],\n", + " fillcolor=f\"rgb{color!s}\",\n", + " layer=layer,\n", + " )\n", + "\n", + "\n", + "@eqx.filter_jit\n", + "@jaxtyped(typechecker=typechecker)\n", + "def random_tx_rx(\n", + " base_scene: TriangleScene, *, key: PRNGKeyArray\n", + ") -> TriangleScene:\n", + " \"\"\"\n", + " Return a random scene with one TX and one RX, at random positions.\n", + "\n", + " TX is placed in the \"upper\" part of the scene, and RX is the \"lower\" part.\n", + " This is just to increase the chances of having possible ray paths in between,\n", + " while maintaining relatively realistic positions.\n", + "\n", + " Args:\n", + " base_scene: The base scene from which the random scene\n", + " is derived.\n", + " key: The random key to be used.\n", + "\n", + " Returns:\n", + " A new scene.\n", + " \"\"\"\n", + " scene = base_scene\n", + " bounding_box = scene.mesh.bounding_box\n", + " min_z, max_z = bounding_box[:, 2]\n", + " rx_bounding_box = bounding_box.at[:, 2].set([min_z + 0.5, min_z + 2.0])\n", + " rx_bounding_box = rx_bounding_box.at[:, 1].set([-5.0, +5.0])\n", + " tx_bounding_box = bounding_box.at[:, 2].set([max_z + 5.0, max_z + -5.0])\n", + " tx_bounding_box = tx_bounding_box.at[:, 1].set([-5.0, +5.0])\n", + " key_tx, key_rx = jax.random.split(key, 2)\n", + " scene = eqx.tree_at(\n", + " lambda s: s.transmitters,\n", + " scene,\n", + " sample_points_in_bounding_box(tx_bounding_box, key=key_tx),\n", + " )\n", + " return eqx.tree_at(\n", + " lambda s: s.receivers,\n", + " scene,\n", + " sample_points_in_bounding_box(rx_bounding_box, key=key_rx),\n", + " )\n", + "\n", + "\n", + "@jaxtyped(typechecker=typechecker)\n", + "def random_scene(\n", + " base_scene: TriangleScene, *, key: PRNGKeyArray\n", + ") -> TriangleScene:\n", + " \"\"\"\n", + " Return a random scene with one TX and one RX, at random positions, and a random number of objects.\n", + "\n", + " The number of objects can be anywhere between 'num_objects // 2' and 'num_objects',\n", + " where 'num_objects' is the total number of objects in the scene. Again, we avoid\n", + " sampling scene with very few objects, as they usually contain no valid paths.\n", + "\n", + " Args:\n", + " base_scene: The base scene from which the random scene\n", + " is derived.\n", + " key: The random key to be used.\n", + "\n", + " Returns:\n", + " A new scene.\n", + " \"\"\"\n", + " scene = base_scene\n", + " key_tx_rx, key_num_objects, key_sample_triangles = jax.random.split(key, 3)\n", + " scene = random_tx_rx(scene, key=key_tx_rx)\n", + " num_objects = scene.mesh.num_objects\n", + " num_objects = jax.random.randint(\n", + " key_num_objects, (), num_objects // 2, num_objects + 1\n", + " )\n", + " return eqx.tree_at(\n", + " lambda s: s.mesh,\n", + " scene,\n", + " scene.mesh.sample(int(num_objects), key=key_sample_triangles),\n", + " )\n", + "\n", + "\n", + "@jaxtyped(typechecker=typechecker)\n", + "def train_dataloader(\n", + " base_scene: TriangleScene, num_reuse: int = 10, *, key: PRNGKeyArray\n", + ") -> Iterator[TriangleScene]:\n", + " \"\"\"\n", + " Return an (infinite) iterator over random scenes for training the model.\n", + "\n", + " Args:\n", + " base_scene: The base scene from which random scenes\n", + " are derived.\n", + " num_reuse: The number of times a random scene is reused.\n", + "\n", + " When a scene is reused, only TX / RX positions are changed.\n", + " key: The random key to be used.\n", + "\n", + " Yields:\n", + " An infinite number of random scenes.\n", + " \"\"\"\n", + "\n", + " while True:\n", + " key, key_to_use = jax.random.split(key, 2)\n", + " scene = random_scene(base_scene, key=key_to_use)\n", + "\n", + " for _ in range(num_reuse):\n", + " key, key_to_use = jax.random.split(key, 2)\n", + " yield random_tx_rx(base_scene, key=key_to_use)\n", + "\n", + " del scene\n", + "\n", + "\n", + "class FlaxE3MLP(nn.Module):\n", + " \"\"\"Convert scene objects into feature vectors with some equivariance properties.\"\"\"\n", + "\n", + " num_features: int\n", + " max_degree: int = 2\n", + "\n", + " @nn.compact\n", + " def __call__(\n", + " self,\n", + " tx: Float[Array, \"num_tx 3\"],\n", + " rx: Float[Array, \"num_rx 3\"],\n", + " objects: Float[Array, \"num_objects num_vertices_per_object 3\"],\n", + " ) -> tuple[\n", + " Float[Array, \" num_features\"], Float[Array, \"num_objects num_features\"]\n", + " ]:\n", + " \"\"\"\n", + " Call this model to generate feature vectors from scene objects.\n", + "\n", + " Args:\n", + " tx: The vector of TX positions.\n", + " rx: The vector of RX positions.\n", + " objects: The array of objects vertices.\n", + "\n", + " Returns:\n", + " The scene feature vector and the objects feature vectors.\n", + " \"\"\"\n", + " num_tx = tx.shape[0]\n", + " num_rx = rx.shape[0]\n", + " num_objects = objects.shape[0]\n", + "\n", + " # [num_vertices=num_tx+num_rx+num_objects*num_vertices_per_object 3]\n", + " xyz = jnp.concatenate((tx, rx, objects.reshape(-1, 3)), axis=0)\n", + " # Invariance to translation\n", + " xyz -= jnp.mean(xyz, axis=0, keepdims=True)\n", + " # Invariance to scaling\n", + " xyz /= jnp.std(xyz, axis=0, keepdims=True)\n", + "\n", + " # 1 - Scene features\n", + "\n", + " # [num_vertices 1 1 (max_degree+1)**2 num_features]\n", + " x = e3x.nn.basis(\n", + " xyz,\n", + " num=self.num_features,\n", + " max_degree=self.max_degree,\n", + " radial_fn=partial(e3x.nn.triangular_window, limit=2.0),\n", + " )\n", + " # [num_vertices 1 2 (max_degree+1)**2 num_features]\n", + " x = e3x.nn.TensorDense()(x)\n", + " # [num_vertices 1 2 num_features]\n", + " x = e3x.nn.TensorDense(max_degree=0)(x)\n", + " # [num_vertices 2*num_features]\n", + " x = x.reshape(x.shape[0], -1)\n", + " # [num_vertices num_features]\n", + " x = nn.Dense(features=self.num_features)(x)\n", + " # [num_features]\n", + " scene_features = x.sum(axis=0)\n", + "\n", + " # [num_objects 1 1 (max_degree+1)**2 num_features]\n", + " x = e3x.nn.basis(\n", + " xyz[num_tx + num_rx :, ...].reshape(\n", + " num_objects, -1, *xyz.shape[1:]\n", + " ),\n", + " num=self.num_features,\n", + " max_degree=self.max_degree,\n", + " radial_fn=partial(e3x.nn.triangular_window, limit=2.0),\n", + " ).mean(axis=1)\n", + " # [num_objects 1 2 (max_degree+1)**2 num_features]\n", + " x = e3x.nn.TensorDense()(x)\n", + " # [num_objects 1 2 num_features]\n", + " x = e3x.nn.TensorDense(max_degree=0)(x)\n", + " # [num_objects 2*num_features]\n", + " x = x.reshape(x.shape[0], -1)\n", + " # [num_objects num_features]\n", + " object_features = nn.Dense(features=self.num_features)(x)\n", + "\n", + " return scene_features, object_features\n", + "\n", + "\n", + "class E3MLP(eqx.Module):\n", + " \"\"\"Convenient Equinox wrapper to use Flax's Linen modules.\"\"\"\n", + "\n", + " params: PyTree[Array]\n", + " flax_model: FlaxE3MLP = eqx.field(static=True)\n", + "\n", + " def __init__(\n", + " self,\n", + " *args: Any,\n", + " num_vertices_per_object: int,\n", + " key: PRNGKeyArray,\n", + " **kwargs: Any,\n", + " ) -> None:\n", + " self.flax_model = FlaxE3MLP(*args, **kwargs)\n", + " tx = jnp.empty((1, 3))\n", + " rx = jnp.empty((1, 3))\n", + " objects = jnp.empty((1, num_vertices_per_object, 3))\n", + " self.params = self.flax_model.init(key, tx, rx, objects)\n", + "\n", + " @eqx.filter_jit\n", + " @jaxtyped(typechecker=typechecker)\n", + " def __call__(\n", + " self,\n", + " tx: Float[Array, \"num_tx 3\"],\n", + " rx: Float[Array, \"num_rx 3\"],\n", + " objects: Float[Array, \"num_objects num_vertices_per_object 3\"],\n", + " ) -> tuple[\n", + " Float[Array, \" num_features\"], Float[Array, \"num_objects num_features\"]\n", + " ]:\n", + " return self.flax_model.apply(self.params, tx, rx, objects)\n", + "\n", + "\n", + "class FlowModel(eqx.Module):\n", + " \"\"\"Flow model that returns flows between parent and child states.\"\"\"\n", + "\n", + " cell: eqx.nn.GRUCell\n", + " \"\"\"Cell that generate a vector of features from an ordered sequence of feature vectors.\"\"\"\n", + " features_2_flow: eqx.nn.MLP\n", + " \"\"\"MLP that maps each (scene_features, object_features[i, :], state_features) to flow.\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " num_features: int,\n", + " *,\n", + " key: PRNGKeyArray,\n", + " ) -> None:\n", + " \"\"\"\n", + " Construct a GFlowNet model.\n", + "\n", + " Args:\n", + " num_features: The size of the vector that represents each object.\n", + " key: The random key to be used.\n", + " \"\"\"\n", + " key_cell, key_flow = jax.random.split(key, 2)\n", + "\n", + " # 1 - Combine arbitrary many features from an (ordered) state and create a new feature vector\n", + " self.cell = eqx.nn.GRUCell(\n", + " input_size=num_features,\n", + " hidden_size=num_features,\n", + " key=key_cell,\n", + " )\n", + "\n", + " # 2 - Features to flow\n", + " self.features_2_flow = eqx.nn.MLP(\n", + " # Takes scene_features + object_features[i, :] + state_features\n", + " in_size=num_features * 3,\n", + " out_size=\"scalar\",\n", + " width_size=500,\n", + " depth=3,\n", + " final_activation=jnp.exp, # Positive flow only\n", + " key=key_flow,\n", + " )\n", + "\n", + " @eqx.filter_jit\n", + " @jaxtyped(typechecker=typechecker)\n", + " def __call__(\n", + " self,\n", + " scene_features: Float[Array, \" num_features\"],\n", + " object_features: Float[Array, \"num_objects num_features\"],\n", + " state: Bool[Array, \"order num_objects\"],\n", + " ) -> Float[Array, \" num_objects\"]:\n", + " \"\"\"\n", + " Call this model in order to generate a new flow from a given state,\n", + " and an abstract representation of the scene and its objects.\n", + "\n", + " Args:\n", + " scene_features: The vector of scene features.\n", + " object_features: The array of each object's features.\n", + " state: The current state, a one-hot encoding of the path candidate\n", + " in construction. Only one element per column can be 'True'.\n", + "\n", + " Returns:\n", + " The array of flows, one per object.\n", + " \"\"\"\n", + " # [order num_features]\n", + " # note: this is a simple but efficient way to take object features\n", + " # corresponding to object indices encoded in the state\n", + " state_features = jnp.einsum(\"ij,jk->ik\", state, object_features)\n", + "\n", + " # [order num_features]\n", + " # note: we transform 'order' vectors of 'num_features' values into\n", + " # one vector of 'num_features' values.\n", + " # We could have used the 'sum' operation (over the leading axis),\n", + " # but then we loose information about the order in which features\n", + " # appeared. A cell can keep some information about ordering since\n", + " # cell(cell(a, b), c) isn't the same as, e.g., cell(cell(b, a), c).\n", + " state_features = jax.lax.scan(\n", + " lambda state, features: (self.cell(features, state), None),\n", + " jnp.zeros_like(scene_features),\n", + " state_features,\n", + " )[0]\n", + "\n", + " # [num_objects]\n", + " # note: the input (per object) looks as follows\n", + " # # [scene_features, state_features, object_features[i, :]]\n", + " return jax.vmap(\n", + " lambda *features: self.features_2_flow(jnp.concatenate(features)),\n", + " in_axes=(None, 0, None),\n", + " )(\n", + " scene_features,\n", + " object_features,\n", + " state_features,\n", + " )\n", + "\n", + "\n", + "class Model(eqx.Module):\n", + " \"\"\"Generative model that samples a path candidate from flows.\"\"\"\n", + "\n", + " features: E3MLP\n", + " \"\"\"MLP that maps each xyz coordinate to a SE(3)- and scaling-invariant feature vector.\"\"\"\n", + " flow: FlowModel\n", + " \"\"\"Flow model.\"\"\"\n", + " inference: bool\n", + " \"\"\"Whether this model is used for training or inference.\"\"\"\n", + " dropout: eqx.nn.Dropout\n", + " \"\"\"Used during training to improve the learning process.\n", + "\n", + " N.B.: this was also observed to avoid model collapsing for order=2.\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " num_features: int = 100,\n", + " *,\n", + " sample_quads: bool = False,\n", + " inference: bool = False,\n", + " key: PRNGKeyArray,\n", + " ) -> None:\n", + " \"\"\"\n", + " Construct a model.\n", + "\n", + " Args:\n", + " num_features: The size of the vector that will represent each xyz triplet.\n", + " sample_quads: Whether the model should sample quadrilaterals or triangles.\n", + " inference: Whether this model is used for training or inference.\n", + " key: The random key to be used.\n", + " \"\"\"\n", + " self.inference = inference\n", + "\n", + " key_embeds, key_flow = jax.random.split(key, 2)\n", + "\n", + " # Layers\n", + "\n", + " # 1 - World coordinates to features\n", + " self.features = E3MLP(\n", + " num_features=num_features,\n", + " num_vertices_per_object=6 if sample_quads else 3,\n", + " key=key_embeds,\n", + " )\n", + "\n", + " # 2 - Generate flow\n", + " self.flow = FlowModel(\n", + " num_features=num_features,\n", + " key=key_flow,\n", + " )\n", + "\n", + " self.dropout = eqx.nn.Dropout(inference=self.inference)\n", + "\n", + " @eqx.filter_jit\n", + " @jaxtyped(typechecker=typechecker)\n", + " def __call__(\n", + " self,\n", + " scene: TriangleScene,\n", + " *,\n", + " order: int,\n", + " key: PRNGKeyArray,\n", + " ) -> (\n", + " Int[Array, \" {order}\"]\n", + " | tuple[Int[Array, \" {order}\"], Float[Array, \"{order} num_objects\"]]\n", + " ):\n", + " \"\"\"\n", + " Call this model to generate a path candidate of the given order.\n", + "\n", + " Args:\n", + " scene: The triangle scene.\n", + " order: The order of the path candidate.\n", + " key: The random key to be used.\n", + "\n", + " Returns:\n", + " A path candidate.\n", + "\n", + " If training mode, it also returns the array of flows.\n", + " \"\"\"\n", + " # 1 - Extracting data\n", + "\n", + " num_objects = scene.mesh.num_objects\n", + "\n", + " tx = scene.transmitters.reshape(-1, 3)\n", + " rx = scene.receivers.reshape(-1, 3)\n", + " objects = scene.mesh.triangle_vertices.reshape(num_objects, -1, 3)\n", + "\n", + " # 2 - Computing features\n", + "\n", + " scene_features, object_features = self.features(tx, rx, objects)\n", + "\n", + " # 3 - Generating path candidates\n", + "\n", + " ScanR = Int[Array, \" \"]\n", + " Flow = Float[Array, f\" {num_objects}\"]\n", + " State = Bool[Array, f\"{order} {num_objects}\"]\n", + " ScanC = tuple[\n", + " Flow,\n", + " State,\n", + " ]\n", + "\n", + " if not self.inference: # During training, we return the flows\n", + " ScanR = tuple[ScanR, Flow]\n", + "\n", + " @jaxtyped(typechecker=typechecker)\n", + " def scan_fn(\n", + " carry: ScanC,\n", + " key_and_current_order: tuple[PRNGKeyArray, Int[Array, \" \"]],\n", + " ) -> tuple[ScanC, ScanR]:\n", + " parent_edge_flow_prediction, state = carry\n", + " key, current_order = key_and_current_order\n", + "\n", + " key_sample, key_dropout = jax.random.split(key, 2)\n", + "\n", + " # Randomly choose a child state (i.e., an object), based on forward flow\n", + " parent_edge_flow_prediction = self.dropout(\n", + " parent_edge_flow_prediction, key=key_dropout\n", + " )\n", + " p = parent_edge_flow_prediction / parent_edge_flow_prediction.sum()\n", + " object_index = jax.random.categorical(\n", + " key=key_sample, logits=jnp.log(p)\n", + " )\n", + "\n", + " # Update 'state' accordingly\n", + " state = state.at[current_order, object_index].set(True)\n", + "\n", + " # Compute the new flow, and stop any flow to 'object_index'\n", + " # to avoid sampling the sample object twice in a row\n", + " edge_flow_prediction = (\n", + " self.flow(scene_features, object_features, state)\n", + " .at[object_index]\n", + " .set(0.0)\n", + " )\n", + "\n", + " if self.inference:\n", + " return (edge_flow_prediction, state), object_index\n", + " return (edge_flow_prediction, state), (\n", + " object_index,\n", + " edge_flow_prediction,\n", + " )\n", + "\n", + " # Let's initialize the variables\n", + " state = jnp.zeros((order, num_objects), dtype=bool)\n", + " parent_edge_flow_prediction = self.flow(\n", + " scene_features, object_features, state\n", + " )\n", + "\n", + " # We use scan to efficiently loop over multiple states\n", + " init = parent_edge_flow_prediction, state\n", + " _, path_candidate = jax.lax.scan(\n", + " scan_fn,\n", + " init,\n", + " xs=(jax.random.split(key, order), jnp.arange(order)),\n", + " )\n", + "\n", + " if not self.inference:\n", + " path_candidate, flows = path_candidate\n", + " flows = jnp.vstack((parent_edge_flow_prediction, flows[1:, :]))\n", + "\n", + " if scene.mesh.assume_quads:\n", + " path_candidate *= 2\n", + "\n", + " if not self.inference:\n", + " return path_candidate, flows\n", + "\n", + " return path_candidate\n", + "\n", + " @eqx.filter_jit\n", + " @jaxtyped(typechecker=typechecker)\n", + " def sample_path_candidates(\n", + " self,\n", + " *args: Any,\n", + " num_path_candidates: int,\n", + " order: int,\n", + " key: PRNGKeyArray,\n", + " **kwargs: Any,\n", + " ) -> Int[Array, \"{num_path_candidates} {order}\"]:\n", + " \"\"\"\n", + " Sample 'num_path_candidates' in a given scene.\n", + "\n", + " This is a convenient wrapper to sample arbitrary many path candidates,\n", + " and has the same output for both training and inference modes.\n", + "\n", + " Args:\n", + " args: Positional arguments passed to :meth:`__call__`.\n", + " order: The order of the path candidate.\n", + " key: The random key to be used.\n", + " kwargs: Keyword arguments passed to :meth:`__call__`.\n", + "\n", + " Returns:\n", + " An array of path candidate.\n", + " \"\"\"\n", + "\n", + " @jaxtyped(typechecker=typechecker)\n", + " def scan_fun(\n", + " _: None, key: PRNGKeyArray\n", + " ) -> tuple[None, Int[Array, f\" {order}\"]]:\n", + " path_candidate = self(*args, order=order, key=key, **kwargs)\n", + "\n", + " if self.inference:\n", + " return None, path_candidate\n", + "\n", + " return None, path_candidate[0]\n", + "\n", + " return jax.lax.scan(\n", + " scan_fun,\n", + " init=None,\n", + " xs=jax.random.split(key, num_path_candidates),\n", + " )[1]\n", + "\n", + " @classmethod\n", + " def load(cls, url: str, **kwargs: Any) -> \"Self\":\n", + " \"\"\"Load a model from a remote URL.\n", + "\n", + " This is a convenient method to load a model from pre-trained\n", + " weights uploaded on the internet.\n", + "\n", + " Args:\n", + " url: The url to download the weights from.\n", + "\n", + " E.g., provide \"https://github.com/jeertmans/DiffeRT/releases/download/icmlcn2025/model_1.eqx\"\n", + " to download the model weights for first order reflection.\n", + " kwargs: Keyword arguments passed to initialize the model.\n", + "\n", + " Shape arguments must be the same as those used when training,\n", + " other arguments (such as the random key) are not important, and\n", + " dummy values can be provided.\n", + " \"\"\"\n", + " with tqdm(\n", + " unit=\"B\",\n", + " unit_scale=True,\n", + " unit_divisor=1024,\n", + " desc=f\"Retrieving model from: {url}\",\n", + " miniters=1,\n", + " ) as bar:\n", + "\n", + " def reporthook(\n", + " block_count: int, block_size: int, total_size: int\n", + " ) -> None:\n", + " bar.total = total_size\n", + " bar.update(block_count * block_size - bar.n)\n", + "\n", + " model = eqx.filter_eval_shape(cls, **kwargs)\n", + " filename, _ = urllib.request.urlretrieve(url, reporthook=reporthook)\n", + "\n", + " with open(filename, \"rb\") as f:\n", + " return eqx.tree_deserialise_leaves(f, model)" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "## Ray Tracing with a Grid of Receivers\n", + "\n", + "Because we want to detect multipath cells for a moving receiver,\n", + "we will need to perform RT simulations for many receiving antenna location.\n", + "The easiest way to do so is to use the\n", + "{meth}`TriangleScene.with_receivers_grid`\n", + "method and rely on the global support of batched dimensions that this library offers to perform\n", + "many RT simulations at the same time.\n", + "\n", + ":::{warning}\n", + "Beware that large batches or higher order reflection paths could rapidly cause out-of-memory issues!\n", + "\n", + "If you have multiple GPUs or TPUs at your disposal, you can\n", + "also use the parallel feature of {meth}`TriangleScene.compute_paths`.\n", + ":::\n", + "\n", + "In the cell below, we also plot a dummy estimate of the received power, simply as a function of the inversed\n", + "squared of the path length." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f30655df4c164d85b4114abb7e32091a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Retrieving model from: https://github.com/jeertmans/DiffeRT/releases/download/icmlcn2025/model_1.eqx: 0.00B [0…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b88fc24868184936b2507c555be19685", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Retrieving model from: https://github.com/jeertmans/DiffeRT/releases/download/icmlcn2025/model_2.eqx: 0.00B [0…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_1 = Model.load(\n", + " \"https://github.com/jeertmans/DiffeRT/releases/download/icmlcn2025/model_1.eqx\",\n", + " key=jax.random.key(0),\n", + " sample_quads=sample_quads,\n", + ")\n", + "model_2 = Model.load(\n", + " \"https://github.com/jeertmans/DiffeRT/releases/download/icmlcn2025/model_2.eqx\",\n", + " key=jax.random.key(0),\n", + " sample_quads=sample_quads,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e0fd98307326441b8919b03efd286238", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Running for 10,000 iterations: 0%| | 0/10000 [00:00 0:\n", + " # [*batch num_path_candidates order]\n", + " obj_indices = paths.objects[..., 1:-1]\n", + " # [*batch num_path_candidates order]\n", + " mat_indices = jnp.take(scene.mesh.face_materials, obj_indices, axis=0)\n", + " # [*batch num_path_candidates order 3]\n", + " obj_normals = jnp.take(scene.mesh.normals, obj_indices, axis=0)\n", + " # [*batch num_path_candidates order]\n", + " obj_n_r = jnp.take(n_r, mat_indices, axis=0)\n", + " # [*batch num_path_candidates order+1 3]\n", + " path_segments = jnp.diff(paths.vertices, axis=-2)\n", + " # [*batch num_path_candidates order+1 3],\n", + " # [*batch num_path_candidates order+1 1]\n", + " k, s = normalize(path_segments, keepdims=True)\n", + " # [*batch num_path_candidates order 3]\n", + " k_i = k[..., :-1, :]\n", + " k_r = k[..., +1:, :]\n", + " # [*batch num_path_candidates order 3]\n", + " (e_i_s, e_i_p), (e_r_s, e_r_p) = sp_directions(k_i, k_r, obj_normals)\n", + " # [*batch num_path_candidates order 1]\n", + " cos_theta = dot(obj_normals, -k_i, keepdims=True)\n", + " # [*batch num_path_candidates order 1]\n", + " r_s, r_p = reflection_coefficients(obj_n_r[..., None], cos_theta)\n", + " # [*batch num_path_candidates 1]\n", + " r_s = jnp.prod(r_s, axis=-2)\n", + " r_p = jnp.prod(r_p, axis=-2)\n", + " # [*batch num_path_candidates order 3]\n", + " (e_i_s, e_i_p), (e_r_s, e_r_p) = sp_directions(k_i, k_r, obj_normals)\n", + " # [*batch num_path_candidates 1]\n", + " E_i_s = dot(E_i, e_i_s[..., 0, :], keepdims=True)\n", + " E_i_p = dot(E_i, e_i_p[..., 0, :], keepdims=True)\n", + " B_i_s = dot(B_i, e_i_s[..., 0, :], keepdims=True)\n", + " B_i_p = dot(B_i, e_i_p[..., 0, :], keepdims=True)\n", + " # [*batch num_path_candidates 1]\n", + " E_r_s = r_s * E_i_s\n", + " E_r_p = r_p * E_i_p\n", + " B_r_s = r_s * B_i_s\n", + " B_r_p = r_p * B_i_p\n", + " # [*batch num_path_candidates 3]\n", + " E_r = E_r_s * e_r_s[..., -1, :] + E_r_p * e_r_p[..., -1, :]\n", + " B_r = B_r_s * e_r_s[..., -1, :] + B_r_p * e_r_p[..., -1, :]\n", + " # [*batch num_path_candidates 1]\n", + " s_tot = s.sum(axis=-2)\n", + " spreading_factor = s[..., 0, :] / s_tot # Far-field approximation\n", + " phase_shift = jnp.exp(1j * s_tot * ant.wavenumber)\n", + " # [*batch num_path_candidates 3]\n", + " E_r *= spreading_factor * phase_shift\n", + " B_r *= spreading_factor * phase_shift\n", + " else:\n", + " # [*batch num_path_candidates 3]\n", + " E_r = E_i\n", + " B_r = B_i\n", + "\n", + " if remove_duplicates:\n", + " paths = paths.mask_duplicate_objects()\n", + "\n", + " # [*batch 3]\n", + " E = jnp.sum(E_r, axis=-2, where=paths.mask[..., None])\n", + " B = jnp.sum(B_r, axis=-2, where=paths.mask[..., None])\n", + "\n", + " return E, B\n", + "\n", + "\n", + "def power(\n", + " scene: TriangleScene,\n", + " *,\n", + " models: dict[int, Model] | None = None,\n", + " min_order: int = 0,\n", + " max_order: int,\n", + "):\n", + " E_tot = jnp.zeros((*batch, 3), dtype=jnp.complex64)\n", + " B_tot = jnp.zeros_like(E_tot)\n", + "\n", + " for order in range(min_order, max_order + 1):\n", + " if order == 0 or models is None:\n", + " for paths in scene_grid.compute_paths(\n", + " order=order, chunk_size=1_000\n", + " ):\n", + " E, B = fields(paths, scene)\n", + " E_tot += E\n", + " B_tot += B\n", + " else:\n", + "\n", + " @scan_tqdm(batch_size, leave=False)\n", + " def body_fun(carry, index):\n", + " i, j = divmod(index, carry[0].shape[1])\n", + "\n", + " local_scene = eqx.tree_at(\n", + " lambda s: s.receivers, scene, scene.receivers[i, j, :]\n", + " )\n", + "\n", + " path_candidates = models[order].sample_path_candidates(\n", + " local_scene,\n", + " order=order,\n", + " num_path_candidates=20,\n", + " key=jax.random.key(1234),\n", + " )\n", + "\n", + " paths = local_scene.compute_paths(\n", + " path_candidates=path_candidates\n", + " )\n", + "\n", + " E, B = fields(paths, local_scene, remove_duplicates=True)\n", + " carry = (carry[0].at[i, j].add(E), carry[1].at[i, j].add(B))\n", + "\n", + " return carry, None\n", + "\n", + " (E_tot, B_tot), _ = jax.lax.scan(\n", + " body_fun, (E_tot, B_tot), jnp.arange(batch_size)\n", + " )\n", + "\n", + " S = pointing_vector(E_tot, B_tot)\n", + " return A_e * jnp.linalg.norm(S, axis=-1)\n", + "\n", + "\n", + "with reuse() as fig:\n", + " scene.plot()\n", + "\n", + " P_gt = power(scene_grid, max_order=2)\n", + " P_pr = power(scene_grid, models=models, max_order=2)\n", + "\n", + " L_dB = 10 * jnp.log10(P_gt / ant.reference_power)\n", + "\n", + " draw_image(\n", + " L_dB, x=x[0, :], y=y[:, 0], z0=z0, colorbar={\"title\": \"Gain (dB)\"}\n", + " )\n", + "\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "tx_x, tx_y, _ = scene.transmitters.reshape(3, 1)\n", + "\n", + "with reuse() as fig:\n", + " draw_mesh_2d(scene.mesh, fig)\n", + " L_dB = 10 * jnp.log10(P_pr / ant.reference_power)\n", + "\n", + " fig.add_heatmap(\n", + " z=L_dB,\n", + " zmin=-100,\n", + " zmax=-20,\n", + " x=np.asarray(x[0, :]),\n", + " colorbar={\"title\": \"Gain (dB)\"},\n", + " y=np.asarray(y[:, 0]),\n", + " )\n", + " fig.add_scatter(\n", + " x=tx_x,\n", + " y=tx_y,\n", + " mode=\"markers+text\",\n", + " text=[\"tx\"],\n", + " marker={\"color\": \"#EF553B\", \"size\": 15},\n", + " showlegend=False,\n", + " )\n", + "\n", + "fig.update_layout(\n", + " width=400,\n", + " height=300,\n", + " plot_bgcolor=\"rgba(0, 0, 0, 0)\",\n", + " paper_bgcolor=\"rgba(0, 0, 0, 0)\",\n", + ")\n", + "fig.update_layout(\n", + " margin=dict(l=0, r=0, t=20, b=20),\n", + ")\n", + "fig.update_xaxes(showgrid=False, zeroline=False)\n", + "fig.update_yaxes(showgrid=False, zeroline=False)\n", + "fig.write_image(\"pred.png\", scale=2)\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [ + "with reuse() as fig:\n", + " draw_mesh_2d(scene.mesh, fig)\n", + " L_dB = 10 * jnp.log10(P_gt / ant.reference_power)\n", + "\n", + " fig.add_heatmap(\n", + " z=L_dB,\n", + " zmin=-100,\n", + " zmax=-20,\n", + " x=np.asarray(x[0, :]),\n", + " colorbar={\"title\": \"Gain (dB)\"},\n", + " y=np.asarray(y[:, 0]),\n", + " )\n", + " fig.add_scatter(\n", + " x=tx_x,\n", + " y=tx_y,\n", + " mode=\"markers+text\",\n", + " text=[\"tx\"],\n", + " marker={\"color\": \"#EF553B\", \"size\": 15},\n", + " showlegend=False,\n", + " )\n", + "\n", + "fig.update_layout(\n", + " width=400,\n", + " height=300,\n", + " plot_bgcolor=\"rgba(0, 0, 0, 0)\",\n", + " paper_bgcolor=\"rgba(0, 0, 0, 0)\",\n", + ")\n", + "fig.update_layout(\n", + " margin=dict(l=0, r=0, t=20, b=20),\n", + ")\n", + "fig.update_xaxes(showgrid=False, zeroline=False)\n", + "fig.update_yaxes(showgrid=False, zeroline=False)\n", + "fig.write_image(\"gt.png\", scale=2)\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "eps = jnp.finfo(jnp.float32).eps\n", + "Lgt_dB = 10 * jnp.log10(eps + P_gt / ant.reference_power)\n", + "Lpr_dB = 10 * jnp.log10(eps + P_pr / ant.reference_power)\n", + "\n", + "e_dB = jnp.abs(Lgt_dB - Lpr_dB)\n", + "\n", + "with reuse() as fig:\n", + " draw_mesh_2d(scene.mesh, fig)\n", + " fig.add_heatmap(\n", + " z=e_dB,\n", + " # zmin=-100,\n", + " zmin=0,\n", + " zmax=20,\n", + " x=np.asarray(x[0, :]),\n", + " colorbar={\"title\": \"Abs.\"},\n", + " y=np.asarray(y[:, 0]),\n", + " )\n", + " fig.add_scatter(\n", + " x=tx_x,\n", + " y=tx_y,\n", + " mode=\"markers+text\",\n", + " text=[\"tx\"],\n", + " marker={\"color\": \"#EF553B\", \"size\": 15},\n", + " showlegend=False,\n", + " )\n", + "\n", + "fig.update_layout(\n", + " width=400,\n", + " height=300,\n", + " plot_bgcolor=\"rgba(0, 0, 0, 0)\",\n", + " paper_bgcolor=\"rgba(0, 0, 0, 0)\",\n", + ")\n", + "fig.update_layout(\n", + " margin=dict(l=0, r=0, t=20, b=20),\n", + ")\n", + "fig.update_xaxes(showgrid=False, zeroline=False)\n", + "fig.update_yaxes(showgrid=False, zeroline=False)\n", + "fig.write_image(\"delta.png\", scale=2)\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [ + "eps = jnp.finfo(jnp.float32).eps\n", + "er_dB = e_dB / jnp.abs(Lgt_dB)\n", + "\n", + "with reuse() as fig:\n", + " draw_mesh_2d(scene.mesh, fig)\n", + " fig.add_heatmap(\n", + " z=er_dB,\n", + " # zmin=-100,\n", + " zmin=0,\n", + " zmax=1,\n", + " x=np.asarray(x[0, :]),\n", + " colorbar={\"title\": \"Rel.\"},\n", + " y=np.asarray(y[:, 0]),\n", + " )\n", + " fig.add_scatter(\n", + " x=tx_x,\n", + " y=tx_y,\n", + " mode=\"markers+text\",\n", + " text=[\"tx\"],\n", + " marker={\"color\": \"#EF553B\", \"size\": 15},\n", + " showlegend=False,\n", + " )\n", + "\n", + "fig.update_layout(\n", + " width=400,\n", + " height=300,\n", + " plot_bgcolor=\"rgba(0, 0, 0, 0)\",\n", + " paper_bgcolor=\"rgba(0, 0, 0, 0)\",\n", + ")\n", + "fig.update_layout(\n", + " margin=dict(l=0, r=0, t=20, b=20),\n", + ")\n", + "fig.update_xaxes(showgrid=False, zeroline=False)\n", + "fig.update_yaxes(showgrid=False, zeroline=False)\n", + "fig.write_image(\"delta_r.png\", scale=2)\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "fig = make_subplots(\n", + " rows=2,\n", + " cols=1,\n", + " specs=[[{\"type\": \"scene\"}], [{\"type\": \"heatmap\"}]],\n", + ")\n", + "\n", + "powers = []\n", + "\n", + "with reuse(figure=fig) as fig:\n", + " scene.plot(tx_kwargs={\"visible\": False}, row=1, col=1)\n", + " draw_mesh_2d(scene.mesh, fig)\n", + "\n", + " offset = len(fig.data)\n", + "\n", + " x_positions = jnp.linspace(x.min(), x.max())\n", + "\n", + " for x_pos in tqdm(x_positions, leave=False):\n", + " scene_grid = eqx.tree_at(\n", + " lambda s: s.transmitters,\n", + " scene_grid,\n", + " scene_grid.transmitters.at[0].set(x_pos),\n", + " )\n", + " P_pr = power(scene_grid, models=models, max_order=2)\n", + " L_dB = 10 * jnp.log10(P_pr / ant.reference_power)\n", + "\n", + " draw_markers(\n", + " scene_grid.transmitters.reshape(-1, 3),\n", + " labels=[\"tx\"],\n", + " showlegend=False,\n", + " visible=False,\n", + " row=1,\n", + " col=1,\n", + " )\n", + "\n", + " tx_x, tx_y, _ = scene_grid.transmitters.reshape(3, 1)\n", + "\n", + " fig.add_scatter(\n", + " x=tx_x,\n", + " y=tx_y,\n", + " mode=\"markers+text\",\n", + " text=[\"tx\"],\n", + " marker={\"color\": \"#EF553B\", \"size\": 15},\n", + " showlegend=False,\n", + " visible=False,\n", + " row=2,\n", + " col=1,\n", + " )\n", + "\n", + " fig.add_heatmap(\n", + " z=L_dB,\n", + " zmin=-100,\n", + " zmax=-20,\n", + " x=np.asarray(x[0, :]),\n", + " colorbar={\"title\": \"Gain (dB)\"},\n", + " visible=False,\n", + " row=2,\n", + " col=1,\n", + " y=np.asarray(y[:, 0]),\n", + " )\n", + "\n", + " steps = []\n", + "\n", + " for i, _ in enumerate(x_positions):\n", + " step = {\n", + " \"method\": \"update\",\n", + " \"args\": [\n", + " {\"visible\": [False, True] + [False] * len(x_positions) * 3},\n", + " ],\n", + " }\n", + " step[\"args\"][0][\"visible\"][offset + 3 * i + 0] = (\n", + " True # Show TX position on scene\n", + " )\n", + " step[\"args\"][0][\"visible\"][offset + 3 * i + 1] = (\n", + " True # Show TX position on MLM\n", + " )\n", + " step[\"args\"][0][\"visible\"][offset + 3 * i + 2] = True # Show CM\n", + " steps.append(step)\n", + "\n", + " sliders = [\n", + " {\n", + " \"active\": 0,\n", + " \"currentvalue\": {\"prefix\": \"TX index: \"},\n", + " \"pad\": {\"t\": 50},\n", + " \"steps\": steps,\n", + " }\n", + " ]\n", + "\n", + " fig.data[offset + 0].visible = True\n", + " fig.data[offset + 1].visible = True\n", + " fig.data[offset + 2].visible = True\n", + "\n", + " fig.update_layout(\n", + " height=800,\n", + " sliders=sliders,\n", + " xaxis={\"range\": [x.min(), x.max()]},\n", + " yaxis={\n", + " \"range\": [y.min(), y.max()],\n", + " \"scaleanchor\": \"x\",\n", + " \"scaleratio\": 1,\n", + " },\n", + " )\n", + "\n", + "fig" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + }, + "mystnb": { + "execution_mode": "off" + }, + "nbsetup": { + "skip": true + }, + "orphan": true + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 0d47283f..7becfa96 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,6 +6,14 @@ requires-python = ">=3.10" version = "1.0.0" [project.optional-dependencies] +cost-dublin = [ + "differt[all]", + "differt-dev[cuda]", + "e3x>=1.0.2", + "jax-tqdm>=0.3.1", + "kaleido==1.0.0rc0", + "numpy<2", +] cuda = [ "jax[cuda]>=0.4.32", ] diff --git a/uv.lock b/uv.lock index bead1f72..0f9b16a1 100644 --- a/uv.lock +++ b/uv.lock @@ -1,10 +1,10 @@ version = 1 requires-python = ">=3.10" resolution-markers = [ - "python_full_version < '3.11'", - "python_full_version == '3.11.*'", - "python_full_version == '3.12.*'", "python_full_version >= '3.13'", + "python_full_version == '3.12.*'", + "python_full_version == '3.11.*'", + "python_full_version < '3.11'", ] [manifest] @@ -76,17 +76,17 @@ wheels = [ [[package]] name = "anyio" -version = "4.6.2.post1" +version = "4.8.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "exceptiongroup", marker = 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