Add comprehensive unit tests

tests/conftest.py

@@ -0,0 +1,93 @@
+"""Pytest configuration and shared fixtures for pyECT tests."""
+
+import pytest
+import torch
+
+
+@pytest.fixture
+def device():
+    """Return the device to use for tests."""
+    return torch.device("cpu")
+
+
+@pytest.fixture
+def triangle_vertices():
+    """Return vertices for a simple 2D triangle."""
+    return torch.tensor([
+        [-1.0, 0.0],
+        [0.0, 1.0],
+        [1.0, 0.0]
+    ])
+
+
+@pytest.fixture
+def triangle_edges():
+    """Return edge indices for a simple triangle."""
+    return torch.tensor([
+        [0, 1],
+        [1, 2],
+        [2, 0]
+    ])
+
+
+@pytest.fixture
+def triangle_faces():
+    """Return face indices for a simple triangle."""
+    return torch.tensor([[0, 1, 2]])
+
+
+@pytest.fixture
+def tetrahedron_vertices():
+    """Return vertices for a simple 3D tetrahedron."""
+    return torch.tensor([
+        [0.0, 0.0, 0.0],
+        [1.0, 0.0, 0.0],
+        [0.5, 1.0, 0.0],
+        [0.5, 0.5, 1.0]
+    ])
+
+
+@pytest.fixture
+def tetrahedron_edges():
+    """Return edge indices for a tetrahedron."""
+    return torch.tensor([
+        [0, 1], [0, 2], [0, 3],
+        [1, 2], [1, 3], [2, 3]
+    ])
+
+
+@pytest.fixture
+def tetrahedron_faces():
+    """Return face indices for a tetrahedron."""
+    return torch.tensor([
+        [0, 1, 2],
+        [0, 1, 3],
+        [0, 2, 3],
+        [1, 2, 3]
+    ])
+
+
+@pytest.fixture
+def tetrahedron_tets():
+    """Return tetrahedron indices."""
+    return torch.tensor([[0, 1, 2, 3]])
+
+
+def pytest_configure(config):
+    """Configure pytest with custom markers."""
+    config.addinivalue_line(
+        "markers", "slow: marks tests as slow (deselect with '-m \"not slow\"')"
+    )
+    config.addinivalue_line(
+        "markers", "cuda: marks tests that require CUDA"
+    )
+
+
+def has_cuda():
+    """Check if CUDA is available."""
+    return torch.cuda.is_available()
+
+
+def has_mps():
+    """Check if MPS (Apple Silicon) is available."""
+    return hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()