(chore): alter tests as well

tests/test_backed_hdf5.py

@@ -10,7 +10,7 @@
 from scipy import sparse
 
 import anndata as ad
-from anndata.compat import SpArray
+from anndata.compat import SpArray, SpMatrix
 from anndata.tests.helpers import (
     GEN_ADATA_DASK_ARGS,
     as_dense_dask_array,
@@ -200,8 +200,8 @@ def test_backed_raw_subset(tmp_path, array_type, subset_func, subset_func2):
     var_idx = subset_func2(mem_adata.var_names)
     if (
         array_type is asarray
-        and isinstance(obs_idx, list | np.ndarray | sparse.spmatrix | SpArray)
-        and isinstance(var_idx, list | np.ndarray | sparse.spmatrix | SpArray)
+        and isinstance(obs_idx, list | np.ndarray | SpMatrix | SpArray)
+        and isinstance(var_idx, list | np.ndarray | SpMatrix | SpArray)
     ):
         pytest.xfail(
             "Fancy indexing does not work with multiple arrays on a h5py.Dataset"

tests/test_backed_sparse.py

@@ -13,7 +13,7 @@
 import anndata as ad
 from anndata._core.anndata import AnnData
 from anndata._core.sparse_dataset import sparse_dataset
-from anndata.compat import CAN_USE_SPARSE_ARRAY, SpArray
+from anndata.compat import CAN_USE_SPARSE_ARRAY, SpArray, SpMatrix
 from anndata.experimental import read_dispatched
 from anndata.tests.helpers import AccessTrackingStore, assert_equal, subset_func
 
@@ -259,8 +259,8 @@ def test_consecutive_bool(
 )
 def test_dataset_append_memory(
     tmp_path: Path,
-    sparse_format: Callable[[ArrayLike], sparse.spmatrix],
-    append_method: Callable[[list[sparse.spmatrix]], sparse.spmatrix],
+    sparse_format: Callable[[ArrayLike], SpMatrix],
+    append_method: Callable[[list[SpMatrix]], SpMatrix],
     diskfmt: Literal["h5ad", "zarr"],
 ):
     path = tmp_path / f"test.{diskfmt.replace('ad', '')}"
@@ -296,7 +296,7 @@ def test_dataset_append_memory(
 )
 def test_read_array(
     tmp_path: Path,
-    sparse_format: Callable[[ArrayLike], sparse.spmatrix],
+    sparse_format: Callable[[ArrayLike], SpMatrix],
     diskfmt: Literal["h5ad", "zarr"],
     subset_func,
     subset_func2,
@@ -316,7 +316,7 @@ def test_read_array(
     ad.settings.use_sparse_array_on_read = True
     assert issubclass(type(diskmtx[obs_idx, var_idx]), SpArray)
     ad.settings.use_sparse_array_on_read = False
-    assert issubclass(type(diskmtx[obs_idx, var_idx]), sparse.spmatrix)
+    assert issubclass(type(diskmtx[obs_idx, var_idx]), SpMatrix)
 
 
 @pytest.mark.parametrize(
@@ -328,8 +328,8 @@ def test_read_array(
 )
 def test_dataset_append_disk(
     tmp_path: Path,
-    sparse_format: Callable[[ArrayLike], sparse.spmatrix],
-    append_method: Callable[[list[sparse.spmatrix]], sparse.spmatrix],
+    sparse_format: Callable[[ArrayLike], SpMatrix],
+    append_method: Callable[[list[SpMatrix]], SpMatrix],
     diskfmt: Literal["h5ad", "zarr"],
 ):
     path = tmp_path / f"test.{diskfmt.replace('ad', '')}"
@@ -356,7 +356,7 @@ def test_dataset_append_disk(
 @pytest.mark.parametrize("sparse_format", [sparse.csr_matrix, sparse.csc_matrix])
 def test_indptr_cache(
     tmp_path: Path,
-    sparse_format: Callable[[ArrayLike], sparse.spmatrix],
+    sparse_format: Callable[[ArrayLike], SpMatrix],
 ):
     path = tmp_path / "test.zarr"
     a = sparse_format(sparse.random(10, 10))
@@ -438,7 +438,7 @@ def width_idx_kinds(
 )
 def test_data_access(
     tmp_path: Path,
-    sparse_format: Callable[[ArrayLike], sparse.spmatrix],
+    sparse_format: Callable[[ArrayLike], SpMatrix],
     idx_maj: Idx,
     idx_min: Idx,
     exp: Sequence[str],

tests/test_concatenate.py

@@ -20,7 +20,7 @@
 from anndata import AnnData, Raw, concat
 from anndata._core import merge
 from anndata._core.index import _subset
-from anndata.compat import AwkArray, CupySparseMatrix, DaskArray, SpArray
+from anndata.compat import AwkArray, CupySparseMatrix, DaskArray, SpArray, SpMatrix
 from anndata.tests import helpers
 from anndata.tests.helpers import (
     BASE_MATRIX_PARAMS,
@@ -61,7 +61,7 @@ def _filled_array(a, fill_value=None):
     return as_dense_dask_array(_filled_array_np(a, fill_value))
 
 
-@filled_like.register(sparse.spmatrix)
+@filled_like.register(SpMatrix)
 def _filled_sparse(a, fill_value=None):
     if fill_value is None:
         return sparse.csr_matrix(a.shape)
@@ -200,7 +200,7 @@ def test_concatenate_roundtrip(join_type, array_type, concat_func, backwards_com
         if isinstance(orig.X, SpArray):
             base_type = SpArray
         else:
-            base_type = sparse.spmatrix
+            base_type = SpMatrix
     if isinstance(orig.X, CupySparseMatrix):
         base_type = CupySparseMatrix
     assert isinstance(result.X, base_type)
@@ -404,7 +404,7 @@ def test_concatenate_obsm_outer(obsm_adatas, fill_val):
         ),
     )
 
-    assert isinstance(outer.obsm["sparse"], sparse.spmatrix)
+    assert isinstance(outer.obsm["sparse"], SpMatrix)
     np.testing.assert_equal(
         outer.obsm["sparse"].toarray(),
         np.array(
@@ -1496,7 +1496,7 @@ def test_concat_X_dtype(cpu_array_type, sparse_indexer_type):
     if sparse.issparse(result.X):
         # See https://github.com/scipy/scipy/issues/20389 for why this doesn't work with csc
         if sparse_indexer_type == np.int64 and (
-            issubclass(cpu_array_type, sparse.spmatrix) or adata.X.format == "csc"
+            issubclass(cpu_array_type, SpMatrix) or adata.X.format == "csc"
         ):
             pytest.xfail(
                 "Data type int64 is not maintained for sparse matrices or csc array"

tests/test_concatenate_disk.py

@@ -30,7 +30,7 @@
         pd.DataFrame,
     ),
     varm_types=(sparse.csr_matrix, np.ndarray, pd.DataFrame),
-    layers_types=(sparse.spmatrix, np.ndarray, pd.DataFrame),
+    layers_types=(sparse.csr_matrix, np.ndarray, pd.DataFrame),
 )
 
 

tests/test_io_conversion.py

@@ -10,6 +10,7 @@
 from scipy import sparse
 
 import anndata as ad
+from anndata.compat import SpMatrix
 from anndata.tests.helpers import assert_equal, gen_adata
 
 
@@ -99,8 +100,8 @@ def test_dense_to_sparse_memory(tmp_path, spmtx_format, to_convert):
     orig = gen_adata((50, 50), np.array)
     orig.raw = orig.copy()
     orig.write_h5ad(dense_path)
-    assert not isinstance(orig.X, sparse.spmatrix)
-    assert not isinstance(orig.raw.X, sparse.spmatrix)
+    assert not isinstance(orig.X, SpMatrix)
+    assert not isinstance(orig.raw.X, SpMatrix)
 
     curr = ad.read_h5ad(dense_path, as_sparse=to_convert, as_sparse_fmt=spmtx_format)
 

tests/test_io_dispatched.py

@@ -4,10 +4,9 @@
 
 import h5py
 import zarr
-from scipy import sparse
 
 import anndata as ad
-from anndata.compat import SpArray
+from anndata.compat import SpArray, SpMatrix
 from anndata.experimental import read_dispatched, write_dispatched
 from anndata.tests.helpers import assert_equal, gen_adata
 
@@ -96,7 +95,7 @@ def set_copy(d, **kwargs):
         # TODO: Should the passed path be absolute?
         path = "/" + store.path + "/" + k
         if hasattr(elem, "shape") and not isinstance(
-            elem, sparse.spmatrix | SpArray | ad.AnnData
+            elem, SpMatrix | SpArray | ad.AnnData
         ):
             if re.match(r"^/((X)|(layers)).*", path):
                 chunks = (M, N)

tests/test_io_elementwise.py

@@ -22,7 +22,13 @@
     get_spec,
 )
 from anndata._io.specs.registry import IORegistryError
-from anndata.compat import CAN_USE_SPARSE_ARRAY, SpArray, ZarrGroup, _read_attr
+from anndata.compat import (
+    CAN_USE_SPARSE_ARRAY,
+    SpArray,
+    SpMatrix,
+    ZarrGroup,
+    _read_attr,
+)
 from anndata.experimental import read_elem_as_dask
 from anndata.io import read_elem, write_elem
 from anndata.tests.helpers import (
@@ -220,7 +226,7 @@ def test_io_spec(store, value, encoding_type):
 @pytest.mark.parametrize("as_dask", [False, True])
 def test_io_spec_cupy(store, value, encoding_type, as_dask):
     if as_dask:
-        if isinstance(value, sparse.spmatrix):
+        if isinstance(value, SpMatrix):
             value = as_cupy_sparse_dask_array(value, format=encoding_type[:3])
         else:
             value = as_dense_cupy_dask_array(value)

tests/test_readwrite.py

@@ -15,12 +15,11 @@
 import pytest
 import zarr
 from numba.core.errors import NumbaDeprecationWarning
-from scipy import sparse
 from scipy.sparse import csc_array, csc_matrix, csr_array, csr_matrix
 
 import anndata as ad
 from anndata._io.specs.registry import IORegistryError
-from anndata.compat import DaskArray, SpArray, _read_attr
+from anndata.compat import DaskArray, SpArray, SpMatrix, _read_attr
 from anndata.tests.helpers import as_dense_dask_array, assert_equal, gen_adata
 from testing.anndata._helpers import xfail_if_numpy2_loompy
 
@@ -161,7 +160,7 @@ def test_readwrite_kitchensink(tmp_path, storage, typ, backing_h5ad, dataset_kwa
     # since we tested if assigned types and loaded types are DaskArray
     # this would also work if they work
     if isinstance(adata_src.raw.X, SpArray):
-        assert isinstance(adata.raw.X, sparse.spmatrix)
+        assert isinstance(adata.raw.X, SpMatrix)
     else:
         assert isinstance(adata_src.raw.X, type(adata.raw.X) | DaskArray)
     assert isinstance(