tests

Author: stephenwilliams22

tests/test_visium_hd.py

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+import math
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import numpy as np
+import pytest
+from click.testing import CliRunner
+from spatialdata import get_extent, read_zarr
+from spatialdata.models import get_table_keys
+
+from spatialdata_io.__main__ import visium_hd_wrapper
+from spatialdata_io._constants._constants import VisiumHDKeys
+from spatialdata_io.readers.visium_hd import (
+    _decompose_projective_matrix,
+    _projective_matrix_is_affine,
+    visium_hd,
+)
+from tests._utils import skip_if_below_python_version
+
+# --- UNIT TESTS FOR HELPER FUNCTIONS ---
+
+def test_projective_matrix_is_affine() -> None:
+    """Test the affine matrix check function."""
+    # An affine matrix should have [0, 0, 1] as its last row
+    affine_matrix = np.array([[2, 0.5, 10], [0.5, 2, 20], [0, 0, 1]])
+    assert _projective_matrix_is_affine(affine_matrix)
+
+    # A projective matrix is not affine if the last row is different
+    projective_matrix = np.array([[2, 0.5, 10], [0.5, 2, 20], [0.01, 0.02, 1]])
+    assert not _projective_matrix_is_affine(projective_matrix)
+
+
+def test_decompose_projective_matrix() -> None:
+    """Test the decomposition of a projective matrix into affine and shift components."""
+    projective_matrix = np.array([[1, 2, 3], [4, 5, 6], [0.1, 0.2, 1]])
+    affine, shift = _decompose_projective_matrix(projective_matrix)
+
+    expected_affine = np.array([[1, 2, 3], [4, 5, 6], [0, 0, 1]])
+
+    # The affine component should be correctly extracted
+    assert np.allclose(affine, expected_affine)
+    # Recomposing the affine and shift matrices should yield the original projective matrix
+    assert np.allclose(affine @ shift, projective_matrix)
+
+
+# --- END-TO-END TESTS ON EXAMPLE DATA ---
+
+# TODO: Replace with the actual Visium HD test dataset folder name
+# This dataset name is used to locate the test data in the './data/' directory.
+# See https://github.com/scverse/spatialdata-io/blob/main/.github/workflows/prepare_test_data.yaml
+# for instructions on how to download and place the data on disk.
+DATASET_FOLDER = "Visium_HD_Mouse_Brain_Chunk"
+DATASET_ID = "visium_hd_tiny"
+
+
+@skip_if_below_python_version()
+def test_visium_hd_data_extent() -> None:
+    """Check the spatial extent of the loaded Visium HD data."""
+    f = Path("./data") / DATASET_FOLDER
+    if not f.is_dir():
+        pytest.skip(f"Test data not found at '{f}'. Skipping extent test.")
+
+    sdata = visium_hd(f, dataset_id=DATASET_ID)
+    extent = get_extent(sdata, exact=False)
+    extent = {ax: (math.floor(extent[ax][0]), math.ceil(extent[ax][1])) for ax in extent}
+
+    # TODO: Replace with the actual expected extent of your test data
+    expected_extent = "{'x': (1000, 7000), 'y': (2000, 8000)}"
+    assert str(extent) == expected_extent
+
+
+@skip_if_below_python_version()
+@pytest.mark.parametrize(
+    "params",
+    [
+        # Test case 1: Default binned data loading (squares)
+        {"load_segmentations_only": False, "load_nucleus_segmentations": False, "bins_as_squares": True, "annotate_table_by_labels": False, "load_all_images": False},
+        # Test case 2: Binned data as circles
+        {"load_segmentations_only": False, "load_nucleus_segmentations": False, "bins_as_squares": False, "annotate_table_by_labels": False, "load_all_images": False},
+        # Test case 3: Binned data with tables annotating labels instead of shapes
+        {"load_segmentations_only": False, "load_nucleus_segmentations": False, "bins_as_squares": True, "annotate_table_by_labels": True, "load_all_images": False},
+        # Test case 4: Load binned data AND all segmentations (cell + nucleus)
+        {"load_segmentations_only": False, "load_nucleus_segmentations": True, "bins_as_squares": True, "annotate_table_by_labels": False, "load_all_images": False},
+        # Test case 5: Load cell segmentations only
+        {"load_segmentations_only": True, "load_nucleus_segmentations": False, "bins_as_squares": True, "annotate_table_by_labels": False, "load_all_images": False},
+        # Test case 6: Load all segmentations (cell + nucleus) only
+        {"load_segmentations_only": True, "load_nucleus_segmentations": True, "bins_as_squares": True, "annotate_table_by_labels": False, "load_all_images": False},
+        # Test case 7: Load everything, including auxiliary images like CytAssist
+        {"load_segmentations_only": False, "load_nucleus_segmentations": True, "bins_as_squares": True, "annotate_table_by_labels": False, "load_all_images": True},
+    ],
+)
+def test_visium_hd_data_integrity(params: dict[str, bool]) -> None:
+    """Check the integrity of various components of the loaded SpatialData object."""
+    f = Path("./data") / DATASET_FOLDER
+    if not f.is_dir():
+        pytest.skip(f"Test data not found at '{f}'. Skipping integrity test.")
+
+    sdata = visium_hd(f, dataset_id=DATASET_ID, **params)
+
+    # --- IMAGE CHECKS ---
+    assert f"{DATASET_ID}_full_image" in sdata.images
+    assert f"{DATASET_ID}_hires_image" in sdata.images
+    assert f"{DATASET_ID}_lowres_image" in sdata.images
+    if params.get("load_all_images", False):
+        assert f"{DATASET_ID}_cytassist_image" in sdata.images
+
+    # --- SEGMENTATION CHECKS (loaded in all modes if present) ---
+    # TODO: Update placeholder values with actual data from your test dataset
+    assert VisiumHDKeys.CELL_SEG_KEY_HD in sdata.tables
+    assert f"{DATASET_ID}_{VisiumHDKeys.CELL_SEG_KEY_HD}" in sdata.shapes
+    cell_table = sdata.tables[VisiumHDKeys.CELL_SEG_KEY_HD]
+    assert cell_table.shape == (2485, 36738)  # Example shape (n_obs, n_vars)
+    assert "cellid_000000001-1" in cell_table.obs_names  # Example cell ID
+
+    if params["load_nucleus_segmentations"]:
+        assert VisiumHDKeys.NUCLEUS_SEG_KEY_HD in sdata.tables
+        assert f"{DATASET_ID}_{VisiumHDKeys.NUCLEUS_SEG_KEY_HD}" in sdata.shapes
+        nuc_table = sdata.tables[VisiumHDKeys.NUCLEUS_SEG_KEY_HD]
+        assert nuc_table.shape == (2485, 36738)  # Example shape
+    else:
+        assert VisiumHDKeys.NUCLEUS_SEG_KEY_HD not in sdata.tables
+
+    # --- BINNED DATA CHECKS ---
+    if params["load_segmentations_only"]:
+        assert "square_002um" not in sdata.tables
+    else:
+        assert "square_008um" in sdata.tables
+        table = sdata.tables["square_008um"]
+        assert table.shape == (39000, 36738)  # Example shape
+        assert "AAACCGGGTTTA-1" in table.obs_names  # Example barcode
+        assert np.array_equal(table.X.indices[:3], [10, 20, 30])  # Example indices
+
+        shape_name = f"{DATASET_ID}_square_008um"
+        labels_name = f"{shape_name}_labels"
+        if params["annotate_table_by_labels"]:
+            assert labels_name in sdata.labels
+            region, _, _ = get_table_keys(table)
+            assert region == labels_name
+        else:
+            assert shape_name in sdata.shapes
+            region, _, _ = get_table_keys(table)
+            assert region == shape_name
+            # Check for circles vs. squares
+            if params["bins_as_squares"]:
+                assert "radius" not in sdata.shapes[shape_name]
+            else:
+                assert "radius" in sdata.shapes[shape_name]
+
+# --- CLI WRAPPER TEST ---
+
+@skip_if_below_python_version()
+def test_cli_visium_hd(runner: CliRunner) -> None:
+    """Test the command-line interface for the Visium HD reader."""
+    f = Path("./data") / DATASET_FOLDER
+    if not f.is_dir():
+        pytest.skip(f"Test data not found at '{f}'. Skipping CLI test.")
+
+    with TemporaryDirectory() as tmpdir:
+        output_zarr = Path(tmpdir) / "data.zarr"
+        result = runner.invoke(
+            visium_hd_wrapper,
+            [
+                "--path",
+                str(f),
+                "--output",
+                str(output_zarr),
+            ],
+        )
+        assert result.exit_code == 0, result.output
+        # Verify the output can be read
+        sdata = read_zarr(output_zarr)
+
+        # A simple check to confirm data was loaded
+        # The default dataset_id is inferred from the feature slice file name.
+        # This assert may need adjustment based on your test data's file names.
+        inferred_dataset_id = DATASET_FOLDER.replace("_outs", "") # Example inference
+        assert f"{inferred_dataset_id}_full_image" in sdata.images