Minor updates

Author: ErnstRoell

.github/workflows/docs.yml

@@ -3,9 +3,8 @@ name: website
 # build the documentation whenever there are new commits on main
 on:
   push:
-    # Alternative: only build for tags.
-    tags:
-      - '*'
+    branches:
+      - main
 
 # security: restrict permissions for CI jobs.
 permissions:
@@ -57,4 +56,4 @@ jobs:
       url: ${{ steps.deployment.outputs.page_url }}
     steps:
       - id: deployment
-        uses: actions/deploy-pages@v4
+        uses: actions/deploy-pages@v4

Makefile

@@ -18,6 +18,7 @@
 from typing import Callable, TypeAlias
 
 import torch
+
 from dect.ect_fn import indicator
 
 Tensor: TypeAlias = torch.Tensor

dect/ect.py

@@ -0,0 +1,96 @@
+from typing import Literal
+
+import geotorch
+import torch
+from torch import nn
+
+def compute_wecc(
+    nh: torch.FloatTensor,
+    index: torch.LongTensor,
+    lin: torch.FloatTensor,
+    weight: torch.FloatTensor,
+    scale: float = 500,
+):
+    """Computes the Weighted Euler Characteristic Curve.
+
+    Parameters
+    ----------
+    nh : torch.FloatTensor
+        The node heights, computed as the inner product of the node coordinates
+        x and the direction vector v.
+    index: torch.LongTensor
+        The index that indicates to which pointcloud a node height belongs. For
+        the node heights it is the same as the batch index, for the higher order
+        simplices it will have to be recomputed.
+    lin: torch.FloatTensor
+        The discretization of the interval [-1,1] each node height falls in this
+        range due to rescaling in normalizing the data.
+    weight: torch.FloatTensor
+        The weight of the node, edge or face. It is the maximum of the node
+        weights for the edges and faces.
+    scale: torch.FloatTensor
+        A single number that scales the sigmoid function by multiplying the
+        sigmoid with the scale. With high (100>) values, the ect will resemble a
+        discrete ECT and with lower values it will smooth the ECT.
+    """
+    ecc = torch.nn.functional.sigmoid(scale * torch.sub(lin, nh)) * weight.view(
+        1, -1, 1
+    )
+    ecc = ecc.movedim(0, 2).movedim(0, 1)
+    return segment_add_coo(ecc, index)
+
+
+def compute_wect(
+    batch: Batch,
+    v: torch.FloatTensor,
+    lin: torch.FloatTensor,
+    wect_type: Literal["points"] | Literal["edges"] | Literal["faces"],
+):
+    """
+    Computes the Weighted Euler Characteristic Transform of a batch of point
+    clouds.
+
+    Parameters
+    ----------
+    batch : Batch
+        A batch of data containing the node coordinates, batch index,
+        edge_index, face, and node weights.
+    v: torch.FloatTensor
+        The direction vector that contains the directions.
+    lin: torch.FloatTensor
+        The discretization of the interval [-1,1] each node height falls in this
+        range due to rescaling in normalizing the data.
+    wect_type: str
+        The type of WECT to compute. Can be "points", "edges", or "faces".
+    """
+
+    nh = batch.x @ v
+    if wect_type in ["edges", "faces"]:
+        edge_weights, _ = batch.node_weights[batch.edge_index].max(axis=0)
+        eh, _ = nh[batch.edge_index].min(dim=0)
+    if wect_type == "faces":
+        face_weights, _ = batch.node_weights[batch.face].max(axis=0)
+        fh, _ = nh[batch.face].min(dim=0)
+
+    if wect_type == "points":
+        return compute_wecc(nh, batch.batch, lin, batch.node_weights)
+    if wect_type == "edges":
+        # noinspection PyUnboundLocalVariable
+        return compute_wecc(
+            nh, batch.batch, lin, batch.node_weights
+        ) - compute_wecc(
+            eh, batch.batch[batch.edge_index[0]], lin, edge_weights
+        )
+    if wect_type == "faces":
+        # noinspection PyUnboundLocalVariable
+        return (
+            compute_wecc(nh, batch.batch, lin, batch.node_weights)
+            - compute_wecc(
+                eh, batch.batch[batch.edge_index[0]], lin, edge_weights
+            )
+            + compute_wecc(fh, batch.batch[batch.face[0]], lin, face_weights)
+        )
+    raise ValueError(f"Invalid wect_type: {wect_type}")
+
+
+

dect/wect.py

@@ -30,6 +30,7 @@ ipykernel = "^6.29.5"
 pytest-cov = "^6.0.0"
 pytest = "^8.3.4"
 
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"