Migrate to uv

Author: ErnstRoell

poetry.lock

@@ -1,38 +1,43 @@
-[tool.poetry]
+[project]
 name = "dect"
 version = "0.1.0"
 description = "A fast package to compute the Euler Characteristic Transform"
 authors = [
-    "Ernst Röell <ernst.roeell@helmholtz-munich.de>",
-    "Bastian Rieck <bastian.grossenbacher@unifr.ch>",
+    { name = "Ernst Röell", email = "ernst.roeell@helmholtz-munich.de" },
+    { name = "Bastian Rieck", email = "bastian.grossenbacher@unifr.ch" },
 ]
-maintainers = ["Ernst Röell <ernst.roeell@helmholtz-munich.de>"]
+requires-python = ">=3.10.0"
 readme = "README.md"
+maintainers = [{ name = "Ernst Röell", email = "ernst.roeell@helmholtz-munich.de" }]
 classifiers = [
     "Development Status :: 4 - Beta",
     "Programming Language :: Python",
 ]
+dependencies = [
+    "torch>2.0.0",
+    "pdoc>=15.0.1,<16",
+    "torch-geometric>=2.6.1,<3",
+    "geotorch>=0.3.0,<0.4",
+]
 
-[tool.poetry.dependencies]
-python = ">=3.10.0"
-torch = ">2.0.0"
-pdoc = "^15.0.1"
-torch-geometric = "^2.6.1"
-geotorch = "^0.3.0"
-
-
-[tool.poetry.group.dev.dependencies]
-pytest = "^8.3.4"
-matplotlib = "^3.10.0"
-pytest-cov = "^6.0.0"
-ipykernel = "^6.29.5"
-
-
-[tool.poetry.group.test.dependencies]
-pytest-cov = "^6.0.0"
-pytest = "^8.3.4"
+[dependency-groups]
+dev = [
+    "pytest>=8.3.4,<9",
+    "matplotlib>=3.10.0,<4",
+    "pytest-cov>=6.0.0,<7",
+    "ipykernel>=6.29.5,<7",
+]
+test = [
+    "pytest-cov>=6.0.0,<7",
+    "pytest>=8.3.4,<9",
+]
 
+[tool.uv]
+default-groups = [
+    "dev",
+    "test",
+]
 
 [build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+requires = ["hatchling"]
+build-backend = "hatchling.build"

pyproject.toml

@@ -0,0 +1,8 @@
+"""
+Tests the core functions for computing the
+ECT over a point cloud.
+"""
+
+
+def test_true():
+    pass

tests/test_ect.py

@@ -0,0 +1,8 @@
+"""
+Tests the core functions for computing the
+ECT over a point cloud.
+"""
+
+
+def test_true():
+    pass

tests/test_ect_edges.py

@@ -0,0 +1,62 @@
+"""
+Tests the core functions for computing the
+ECT over a point cloud. A point cloud is of
+shape [B,N,D] where the first dimension is the
+batch, the second is the number of points and the
+third is the ambient dimension.
+Note that in this format each point in the point
+cloud has to have the same cardinality.
+"""
+
+import torch
+
+from dect.directions import generate_2d_directions, generate_uniform_directions
+from dect.ect import compute_ect_point_cloud
+
+
+def test_compute_ect_point_cloud_case_cpu():
+    """
+    Tests the ECT computation of a point cloud.
+    Differs in that it expects an input shape of
+    size [B,N,D], where B is the batch size,
+    N is the number of points and D is the ambient
+    dimension.
+    """
+    ambient_dimension = 4
+    num_pts = 100
+    batch_size = 8
+    num_thetas = 100
+    seed = 0
+    x = torch.rand(size=(batch_size, num_pts, ambient_dimension))
+    v = generate_uniform_directions(
+        num_thetas=num_thetas, d=ambient_dimension, device="cpu", seed=seed
+    )
+
+    ect = compute_ect_point_cloud(x, v, radius=10, resolution=30, scale=500)
+
+    assert ect[0].max() == num_pts
+    assert ect[0].min() == 0
+
+
+def test_compute_ect_point_cloud_case_cuda():
+    """
+    Tests the ECT computation of a point cloud.
+    Differs in that it expects an input shape of
+    size [B,N,D], where B is the batch size,
+    N is the number of points and D is the ambient
+    dimension.
+    """
+    ambient_dimension = 4
+    num_pts = 100
+    batch_size = 8
+    num_thetas = 100
+    seed = 0
+    x = torch.rand(size=(batch_size, num_pts, ambient_dimension), device="cuda")
+    v = generate_uniform_directions(
+        num_thetas=num_thetas, d=ambient_dimension, device="cuda", seed=seed
+    )
+
+    ect = compute_ect_point_cloud(x, v, radius=10, resolution=30, scale=500)
+
+    assert ect[0].max() == num_pts
+    assert ect[0].min() == 0

tests/test_ect_faces.py

@@ -0,0 +1,60 @@
+"""
+Tests the core functions for computing the
+ECT over a point cloud.
+"""
+
+import torch
+
+from dect.directions import generate_2d_directions, generate_uniform_directions
+from dect.ect import compute_ect_points
+
+"""
+Test the ECT for 
+"""
+
+
+def test_compute_ect_case_points():
+    """
+    Test the `compute_ect` function for point clouds.
+    """
+    device = "cpu"
+    # 2D Case
+    seed = 2024
+    ambient_dimension = 5
+    num_points = 10
+    v = generate_uniform_directions(
+        num_thetas=17, seed=seed, device=device, d=ambient_dimension
+    ).to(device)
+    x = torch.rand(size=(num_points, ambient_dimension))
+    ect = compute_ect_points(x, v=v, radius=1, resolution=13, scale=10)
+
+    assert ect.get_device() == -1
+
+    # Check that min and max are 0 and num_pts
+    torch.testing.assert_close(ect.max(), torch.tensor(num_points, dtype=torch.float32))
+    torch.testing.assert_close(ect.min(), torch.tensor(0.0, dtype=torch.float32))
+
+
+def test_compute_ect_case_points_cuda():
+    """
+    Test the `compute_ect` function for point clouds on the gpu.
+    """
+    if not torch.cuda.is_available():
+        return
+
+    device = "cuda:0"
+    # 2D Case
+    num_points = 10
+    v = generate_2d_directions(num_thetas=17).to(device)
+    x = torch.rand(size=(num_points, 2), device=device)
+    ect = compute_ect_points(x, v=v, radius=1, resolution=13, scale=10)
+
+    assert ect.get_device() == 0  # 0 indicates cuda.
+
+    # Check that min and max are 0 and num_pts
+    torch.testing.assert_close(
+        ect.max(), torch.tensor(num_points, dtype=torch.float32, device=device)
+    )
+    torch.testing.assert_close(
+        ect.min(), torch.tensor(0.0, dtype=torch.float32, device=device)
+    )