Import from utils

src/scanpy/_utils/compute/is_constant.py

@@ -16,7 +16,8 @@
 
     from numpy.typing import NDArray
 
-    _CSMatrix = sparse.csr_matrix | sparse.csc_matrix
+    from ..._utils import _CSMatrix
+
     _Array = NDArray | DaskArray | _CSMatrix
 
     C = TypeVar("C", bound=Callable)

src/scanpy/get/get.py

@@ -9,7 +9,8 @@
 from anndata import AnnData
 from numpy.typing import NDArray
 from packaging.version import Version
-from scipy.sparse import csc_matrix, csr_matrix
+
+from .._utils import _CSMatrix
 
 if TYPE_CHECKING:
     from collections.abc import Collection, Iterable
@@ -21,8 +22,6 @@
     from .._compat import DaskArray
 
 
-_CSMatrix = csr_matrix | csc_matrix
-
 # --------------------------------------------------------------------------------
 # Plotting data helpers
 # --------------------------------------------------------------------------------

src/scanpy/preprocessing/_deprecated/highly_variable_genes.py

@@ -16,9 +16,7 @@
 if TYPE_CHECKING:
     from typing import Literal
 
-    from scipy.sparse import csc_matrix, csr_matrix
-
-    _CSMatrix = csr_matrix | csc_matrix
+    from .._utils import _CSMatrix
 
 
 @deprecated("Use sc.pp.highly_variable_genes instead")

src/scanpy/preprocessing/_deprecated/sampling.py

@@ -9,11 +9,9 @@
     import numpy as np
     from anndata import AnnData
     from numpy.typing import NDArray
-    from scipy.sparse import csc_matrix, csr_matrix
 
     from ..._compat import _LegacyRandom
-
-    _CSMatrix = csr_matrix | csc_matrix
+    from ..._utils import _CSMatrix
 
 
 @old_positionals("n_obs", "random_state", "copy")

src/scanpy/preprocessing/_pca/__init__.py

@@ -27,12 +27,9 @@
     import dask_ml.decomposition as dmld
     import sklearn.decomposition as skld
     from numpy.typing import DTypeLike, NDArray
-    from scipy import sparse
 
     from ..._compat import _LegacyRandom
-    from ..._utils import Empty
-
-    _CSMatrix = sparse.csr_matrix | sparse.csc_matrix
+    from ..._utils import Empty, _CSMatrix
 
     MethodDaskML = type[dmld.PCA | dmld.IncrementalPCA | dmld.TruncatedSVD]
     MethodSklearn = type[skld.PCA | skld.TruncatedSVD]

src/scanpy/preprocessing/_pca/_compat.py

@@ -15,12 +15,10 @@
     from typing import Literal
 
     from numpy.typing import NDArray
-    from scipy import sparse
     from sklearn.decomposition import PCA
 
     from ..._compat import _LegacyRandom
-
-    _CSMatrix = sparse.csr_matrix | sparse.csc_matrix
+    from .._utils import _CSMatrix
 
 
 def _pca_compat_sparse(

src/scanpy/preprocessing/_pca/_dask_sparse.py

@@ -15,11 +15,9 @@
     from typing import Literal
 
     from numpy.typing import DTypeLike
-    from scipy import sparse
 
     from ..._compat import DaskArray
-
-    _CSMatrix = sparse.csr_matrix | sparse.csc_matrix
+    from .._utils import _CSMatrix
 
 
 @dataclass

src/scanpy/preprocessing/_qc.py

@@ -7,13 +7,13 @@
 import numba
 import numpy as np
 import pandas as pd
-from scipy.sparse import csc_matrix, csr_matrix, issparse, spmatrix
+from scipy.sparse import csr_matrix, issparse, spmatrix
 
 from scanpy.preprocessing._distributed import materialize_as_ndarray
 from scanpy.preprocessing._utils import _get_mean_var
 
 from .._compat import DaskArray, njit
-from .._utils import _doc_params, axis_nnz, axis_sum
+from .._utils import _CSMatrix, _doc_params, axis_nnz, axis_sum
 from ._docs import (
     doc_adata_basic,
     doc_expr_reps,
@@ -28,8 +28,6 @@
 
     from anndata import AnnData
 
-_CSMatrix = csr_matrix | csc_matrix
-
 
 def _choose_mtx_rep(adata, *, use_raw: bool = False, layer: str | None = None):
     is_layer = layer is not None

src/scanpy/preprocessing/_scale.py

@@ -30,9 +30,8 @@
 
 if TYPE_CHECKING:
     from numpy.typing import NDArray
-    from scipy import sparse as sp
 
-    _CSMatrix = sp.csr_matrix | sp.csc_matrix
+    from .._utils import _CSMatrix
 
 
 @njit

src/scanpy/preprocessing/_scrublet/core.py

@@ -23,13 +23,11 @@
 
     from ..._compat import _LegacyRandom
     from ...neighbors import _Metric, _MetricFn
+    from .._utils import _CSMatrix
 
 __all__ = ["Scrublet"]
 
 
-_CSMatrix = sparse.csr_matrix | sparse.csc_matrix
-
-
 @dataclass(kw_only=True)
 class Scrublet:
     """\

src/scanpy/preprocessing/_scrublet/sparse_utils.py

@@ -12,9 +12,8 @@
 if TYPE_CHECKING:
     from numpy.typing import NDArray
 
-    from .._compat import _LegacyRandom
-
-    _CSMatrix = sparse.csr_matrix | sparse.csc_matrix
+    from ..._compat import _LegacyRandom
+    from ..._utils import _CSMatrix
 
 
 def sparse_multiply(

src/scanpy/preprocessing/_simple.py

@@ -22,6 +22,7 @@
 from .._settings import settings as sett
 from .._utils import (
     _check_array_function_arguments,
+    _CSMatrix,
     _resolve_axis,
     axis_sum,
     is_backed_type,
@@ -51,8 +52,6 @@
     from .._utils import RNGLike, SeedLike
 
 
-_CSMatrix = csr_matrix | csc_matrix
-
 A = TypeVar("A", bound=np.ndarray | _CSMatrix | DaskArray)
 
 

src/scanpy/preprocessing/_utils.py

@@ -9,7 +9,7 @@
 from sklearn.random_projection import sample_without_replacement
 
 from .._compat import njit
-from .._utils import axis_sum, elem_mul
+from .._utils import _CSMatrix, axis_sum, elem_mul
 
 if TYPE_CHECKING:
     from typing import Literal
@@ -20,9 +20,6 @@
     from .._utils import _SupportedArray
 
 
-_CSMatrix = sparse.csr_matrix | sparse.csc_matrix
-
-
 @singledispatch
 def axis_mean(X: DaskArray, *, axis: Literal[0, 1], dtype: DTypeLike) -> DaskArray:
     total = axis_sum(X, axis=axis, dtype=dtype)

src/scanpy/tools/_leiden.py

@@ -15,11 +15,10 @@
     from typing import Literal
 
     from anndata import AnnData
-    from scipy import sparse
 
     from .._compat import _LegacyRandom
+    from .._utils import _CSMatrix
 
-    _CSMatrix = sparse.csr_matrix | sparse.csc_matrix
 
 try:
     from leidenalg.VertexPartition import MutableVertexPartition

src/scanpy/tools/_louvain.py

@@ -20,11 +20,9 @@
     from typing import Any, Literal
 
     from anndata import AnnData
-    from scipy.sparse import csc_matrix, csr_matrix
 
     from .._compat import _LegacyRandom
-
-    _CSMatrix = csr_matrix | csc_matrix
+    from .._utils import _CSMatrix
 
 try:
     from louvain.VertexPartition import MutableVertexPartition

src/scanpy/tools/_rank_genes_groups.py

@@ -24,10 +24,11 @@
 
     from anndata import AnnData
     from numpy.typing import NDArray
-    from scipy import sparse
+
+    from .._utils import _CSMatrix
 
     _CorrMethod = Literal["benjamini-hochberg", "bonferroni"]
-    _CSMatrix = sparse.csr_matrix | sparse.csc_matrix
+
 
 # Used with get_literal_vals
 _Method = Literal["logreg", "t-test", "wilcoxon", "t-test_overestim_var"]

src/scanpy/tools/_score_genes.py

@@ -8,10 +8,9 @@
 import pandas as pd
 from scipy.sparse import issparse
 
-from scanpy._utils import _check_use_raw, is_backed_type
-
 from .. import logging as logg
 from .._compat import old_positionals
+from .._utils import _check_use_raw, is_backed_type
 from ..get import _get_obs_rep
 
 if TYPE_CHECKING:
@@ -20,15 +19,14 @@
 
     from anndata import AnnData
     from numpy.typing import DTypeLike, NDArray
-    from scipy.sparse import csc_matrix, csr_matrix
 
     from .._compat import _LegacyRandom
+    from .._utils import _CSMatrix
 
     try:
         _StrIdx = pd.Index[str]
     except TypeError:  # Sphinx
         _StrIdx = pd.Index
-    _CSMatrix = csr_matrix | csc_matrix
     _GetSubset = Callable[[_StrIdx], np.ndarray | _CSMatrix]
 
 

src/scanpy/tools/_utils_clustering.py

@@ -9,9 +9,8 @@
     import pandas as pd
     from anndata import AnnData
     from numpy.typing import NDArray
-    from scipy.sparse import csc_matrix, csr_matrix
 
-    _CSMatrix = csr_matrix | csc_matrix
+    from .._utils import _CSMatrix
 
 
 def rename_groups(