Add basic tests

Author: Marius1311

src/cellmapper/check.py

@@ -57,7 +57,7 @@ def check(self) -> None:
 
 
 INSTALL_HINTS = types.SimpleNamespace(
-    rapids="To speed up k-NN search on GPU, you may install RAPIDS following the guide from "
+    rapids="To speed up k-NN search on GPU, you may install rapids following the guide from "
     "https://docs.rapids.ai/install/. Note that you will only need cuML.",
     faiss="To speed up k-NN search on GPU, you may install faiss following the guide from "
     "https://github.com/facebookresearch/faiss/blob/main/INSTALL.md",

src/cellmapper/knn.py

@@ -191,7 +191,7 @@ def compute_neighbors(
             logger.info("Using %s to compute %d neighbors.", method, n_neighbors)
 
             if method == "rapids":
-                check_deps("rapids_singlecell")
+                check_deps("rapids")
                 import cuml as cm
                 import cupy as cp
 

tests/conftest.py

@@ -1,11 +1,14 @@
-import anndata as ad
 import numpy as np
 import pytest
 
 
 @pytest.fixture
-def adata():
-    adata = ad.AnnData(X=np.array([[1.2, 2.3], [3.4, 4.5], [5.6, 6.7]]).astype(np.float32))
-    adata.layers["scaled"] = np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]]).astype(np.float32)
+def sample_distances():
+    # 3 samples, 2 neighbors each
+    return np.array([[0.0, 1.0], [0.0, 2.0], [0.0, 3.0]], dtype=np.float64)
 
-    return adata
+
+@pytest.fixture
+def sample_indices():
+    # 3 samples, 2 neighbors each
+    return np.array([[0, 1], [1, 2], [2, 0]], dtype=np.int64)

tests/test_basic.py

@@ -1,40 +1,5 @@
-import pytest
-
 import cellmapper
 
 
 def test_package_has_version():
     assert cellmapper.__version__ is not None
-
-
-@pytest.mark.skip(reason="This decorator should be removed when test passes.")
-def test_example():
-    assert 1 == 0  # This test is designed to fail.
-
-
-@pytest.mark.skip(reason="This decorator should be removed when test passes.")
-@pytest.mark.parametrize(
-    "transform,layer_key,max_items,expected_len,expected_substring",
-    [
-        # Test default parameters
-        (lambda vals: f"mean={vals.mean():.2f}", None, 100, 1, "mean="),
-        # Test with layer_key
-        (lambda vals: f"mean={vals.mean():.2f}", "scaled", 100, 1, "mean=0."),
-        # Test with max_items limit (won't affect single item)
-        (lambda vals: f"max={vals.max():.2f}", None, 1, 1, "max=6.70"),
-    ],
-)
-def test_elaborate_example_adata_only_simple(
-    adata,  # this tests uses the adata object from the fixture in the conftest.py
-    transform,
-    layer_key,
-    max_items,
-    expected_len,
-    expected_substring,
-):
-    result = cellmapper.pp.elaborate_example(
-        items=[adata], transform=transform, layer_key=layer_key, max_items=max_items
-    )
-
-    assert len(result) == expected_len
-    assert expected_substring in result[0]

tests/test_neighbors_results.py

@@ -0,0 +1,47 @@
+import numpy as np
+import pytest
+from scipy.sparse import csr_matrix
+
+from cellmapper.knn import NeighborsResults
+
+
+def test_neighborsresults_init_shape(sample_distances, sample_indices):
+    # Should not raise
+    nr = NeighborsResults(distances=sample_distances, indices=sample_indices)
+    assert nr.n_samples == 3
+    assert nr.n_neighbors == 2
+    assert nr.shape == (3, 3)
+
+
+def test_neighborsresults_invalid_shape(sample_distances):
+    # indices shape mismatch should raise
+    bad_indices = np.array([[0, 1, 2], [1, 2, 0], [2, 0, 1]])
+    with pytest.raises(ValueError):
+        NeighborsResults(distances=sample_distances, indices=bad_indices)
+
+
+def test_knn_graph_distances(sample_distances, sample_indices):
+    nr = NeighborsResults(distances=sample_distances, indices=sample_indices)
+    mat = nr.knn_graph_distances
+    assert isinstance(mat, csr_matrix)
+    assert mat.shape == (3, 3)
+    # Check that diagonal is zero (self-distance)
+    assert np.allclose(mat.diagonal(), 0)
+
+
+def test_knn_graph_connectivities_gaussian(sample_distances, sample_indices):
+    nr = NeighborsResults(distances=sample_distances, indices=sample_indices)
+    mat = nr.knn_graph_connectivities(kernel="gaussian")
+    assert isinstance(mat, csr_matrix)
+    assert mat.shape == (3, 3)
+    # All values should be in (0, 1]
+    assert np.all((mat.data > 0) & (mat.data <= 1))
+
+
+def test_boolean_adjacency(sample_distances, sample_indices):
+    nr = NeighborsResults(distances=sample_distances, indices=sample_indices)
+    mat = nr.boolean_adjacency()
+    assert isinstance(mat, csr_matrix)
+    assert mat.shape == (3, 3)
+    # All nonzero values should be 1
+    assert np.all(mat.data == 1)