Test or remove all remaining (except graph)

src/scanpy/_utils/__init__.py

@@ -841,7 +841,8 @@ def check_nonnegative_integers(X: _SupportedArray) -> bool | DaskArray:
 
 
 @check_nonnegative_integers.register(np.ndarray)
-@check_nonnegative_integers.register(sparse.spmatrix)
+@check_nonnegative_integers.register(sparse.csr_matrix)
+@check_nonnegative_integers.register(sparse.csc_matrix)
 def _check_nonnegative_integers_in_mem(X: _MemoryArray) -> bool:
     from numbers import Integral
 

src/scanpy/external/tl/_harmony_timeseries.py

@@ -76,9 +76,9 @@ def harmony_timeseries(
 
     **X_harmony** - :class:`~numpy.ndarray` (:attr:`~anndata.AnnData.obsm`, dtype `float`)
         force directed layout
-    **harmony_aff** - :class:`~scipy.sparse.spmatrix` (:attr:`~anndata.AnnData.obsp`, dtype `float`)
+    **harmony_aff** - :class:`~scipy.sparse.csr_matrix` (:attr:`~anndata.AnnData.obsp`, dtype `float`)
         affinity matrix
-    **harmony_aff_aug** - :class:`~scipy.sparse.spmatrix` (:attr:`~anndata.AnnData.obsp`, dtype `float`)
+    **harmony_aff_aug** - :class:`~scipy.sparse.csr_matrix` (:attr:`~anndata.AnnData.obsp`, dtype `float`)
         augmented affinity matrix
     **harmony_timepoint_var** - `str` (:attr:`~anndata.AnnData.uns`)
         The name of the variable passed as `tp`

src/scanpy/external/tl/_palantir.py

@@ -94,7 +94,7 @@ def palantir(
             Array of Diffusion components.
         - palantir_EigenValues - :class:`~numpy.ndarray` (:attr:`~anndata.AnnData.uns`, dtype `float`)
             Array of corresponding eigen values.
-        - palantir_diff_op - :class:`~scipy.sparse.spmatrix` (:attr:`~anndata.AnnData.obsp`, dtype `float`)
+        - palantir_diff_op - :class:`~scipy.sparse.csr_matrix` (:attr:`~anndata.AnnData.obsp`, dtype `float`)
             The diffusion operator matrix.
 
     **Multi scale space results**,

src/scanpy/preprocessing/_simple.py

@@ -46,7 +46,6 @@
 
     import pandas as pd
     from numpy.typing import NDArray
-    from scipy.sparse import spmatrix
 
     from .._compat import _LegacyRandom
     from .._utils import RNGLike, SeedLike
@@ -61,7 +60,7 @@
     "min_counts", "min_genes", "max_counts", "max_genes", "inplace", "copy"
 )
 def filter_cells(
-    data: AnnData | spmatrix | np.ndarray | DaskArray,
+    data: AnnData | _CSMatrix | np.ndarray | DaskArray,
     *,
     min_counts: int | None = None,
     min_genes: int | None = None,
@@ -209,7 +208,7 @@ def filter_cells(
     "min_counts", "min_cells", "max_counts", "max_cells", "inplace", "copy"
 )
 def filter_genes(
-    data: AnnData | spmatrix | np.ndarray | DaskArray,
+    data: AnnData | _CSMatrix | np.ndarray | DaskArray,
     *,
     min_counts: int | None = None,
     min_cells: int | None = None,

tests/test_metrics.py

@@ -14,7 +14,6 @@
 from scipy import sparse
 
 import scanpy as sc
-from scanpy._compat import DaskArray
 from testing.scanpy._helpers.data import pbmc68k_reduced
 from testing.scanpy._pytest.params import ARRAY_TYPES
 
@@ -117,26 +116,33 @@ def test_correctness(metric, size, expected):
     np.testing.assert_equal(metric(adata, vals=connected), expected)
 
 
-@pytest.mark.parametrize("array_type", ARRAY_TYPES)
-def test_graph_metrics_w_constant_values(metric, array_type, threading):
+@pytest.mark.parametrize(
+    "array_type", [*ARRAY_TYPES, pytest.param(sparse.coo_matrix, id="scipy_coo")]
+)
+def test_graph_metrics_w_constant_values(
+    request: pytest.FixtureRequest, metric, array_type, threading
+):
+    if "dask" in array_type.__name__:
+        reason = "DaskArray not yet supported"
+        request.applymarker(pytest.mark.xfail(reason=reason))
+
     # https://github.com/scverse/scanpy/issues/1806
     pbmc = pbmc68k_reduced()
-    XT = array_type(pbmc.raw.X.T.copy())
+    XT = pbmc.raw.X.T.copy()
     g = pbmc.obsp["connectivities"].copy()
     equality_check = partial(np.testing.assert_allclose, atol=1e-11)
 
-    if isinstance(XT, DaskArray):
-        pytest.skip("DaskArray yet not supported")
-
     const_inds = np.random.choice(XT.shape[0], 10, replace=False)
     with warnings.catch_warnings():
         warnings.simplefilter("ignore", sparse.SparseEfficiencyWarning)
         XT_zero_vals = XT.copy()
         XT_zero_vals[const_inds, :] = 0
+        XT_zero_vals = array_type(XT_zero_vals)
         XT_const_vals = XT.copy()
         XT_const_vals[const_inds, :] = 42
+        XT_const_vals = array_type(XT_const_vals)
 
-    results_full = metric(g, XT)
+    results_full = metric(g, array_type(XT))
     # TODO: Check for warnings
     with pytest.warns(
         UserWarning, match=r"10 variables were constant, will return nan for these"

tests/test_qc_metrics.py

@@ -75,10 +75,13 @@ def test_segments_binary():
 
 
 @pytest.mark.parametrize(
-    "cls", [np.asarray, sparse.csr_matrix, sparse.csc_matrix, sparse.coo_matrix]
+    "array_type", [*ARRAY_TYPES, pytest.param(sparse.coo_matrix, id="scipy_coo")]
 )
-def test_top_segments(cls):
-    a = cls(np.ones((300, 100)))
+def test_top_segments(request: pytest.FixtureRequest, array_type):
+    if "dask" in array_type.__name__:
+        reason = "DaskArray not yet supported"
+        request.applymarker(pytest.mark.xfail(reason=reason))
+    a = array_type(np.ones((300, 100)))
     seg = top_segment_proportions(a, [50, 100])
     assert (seg[:, 0] == 0.5).all()
     assert (seg[:, 1] == 1.0).all()

tests/test_utils.py

@@ -8,7 +8,7 @@
 import pytest
 from anndata.tests.helpers import asarray
 from packaging.version import Version
-from scipy.sparse import csr_matrix, issparse
+from scipy.sparse import coo_matrix, csr_matrix, issparse
 
 from scanpy._compat import DaskArray, _legacy_numpy_gen, pkg_version
 from scanpy._utils import (
@@ -151,7 +151,9 @@ def test_elem_mul(array_type):
     np.testing.assert_array_equal(res, expd)
 
 
-@pytest.mark.parametrize("array_type", ARRAY_TYPES)
+@pytest.mark.parametrize(
+    "array_type", [*ARRAY_TYPES, pytest.param(coo_matrix, id="scipy_coo")]
+)
 def test_axis_sum(array_type):
     m1 = array_type(asarray([[0, 1, 1], [1, 0, 1]]))
     expd_0 = np.array([1, 1, 2])