chore: anndata 0.11 as min

docs/release-notes/4191.chore.md

@@ -0,0 +1 @@
+{mod}`anndata` is lower bounded by `0.11.2` {smaller}`I Gold`

pyproject.toml

@@ -51,7 +51,7 @@ classifiers = [
 ]
 dynamic = [ "version" ]
 dependencies = [
-    "anndata>=0.10.8",
+    "anndata>=0.11.2",
     "certifi",
     "fast-array-utils[accel,sparse]>=1.4",
     "h5py>=3.11",
@@ -67,7 +67,7 @@ dependencies = [
     "patsy",
     "pynndescent>=0.5.13",
     "scikit-learn>=1.6",
-    "scipy>=1.14",
+    "scipy>=1.15",
     "scverse-misc[settings]>=0.1.1",
     "seaborn>=0.13.2",
     "session-info2",

src/scanpy/_utils/__init__.py

@@ -30,10 +30,9 @@
 import numpy as np
 import pandas as pd
 from anndata._core.sparse_dataset import BaseCompressedSparseDataset
-from packaging.version import Version
 
 from .. import logging as logg
-from .._compat import CSBase, DaskArray, SpBase, _CSArray, pkg_version, warn
+from .._compat import CSBase, DaskArray, SpBase, warn
 from ._numba import _numba_thread_limit
 
 if TYPE_CHECKING:
@@ -728,21 +727,9 @@ def axis_nnz(x: ArrayLike, /, axis: Literal[0, 1]) -> np.ndarray:
     return np.count_nonzero(x, axis=axis)
 
 
-if pkg_version("scipy") >= Version("1.15"):
-    # newer scipy versions support the `axis` argument for count_nonzero
-    @axis_nnz.register(CSBase)
-    def _(x: CSBase, /, axis: Literal[0, 1]) -> np.ndarray:
-        return x.count_nonzero(axis=axis)
-
-else:
-    # older scipy versions don’t have any way to get the nnz of a sparse array
-    @axis_nnz.register(CSBase)
-    def _(x: CSBase, /, axis: Literal[0, 1]) -> np.ndarray:
-        if isinstance(x, _CSArray):
-            from scipy.sparse import csc_array, csr_array  # noqa: TID251
-
-            x = (csr_array if x.format == "csr" else csc_array)(x)
-        return x.getnnz(axis=axis)
+@axis_nnz.register(CSBase)
+def _(x: CSBase, /, axis: Literal[0, 1]) -> np.ndarray:
+    return x.count_nonzero(axis=axis)
 
 
 @axis_nnz.register(DaskArray)

src/testing/scanpy/_helpers/__init__.py

@@ -16,13 +16,14 @@
 from packaging.version import Version
 
 import scanpy as sc
-from scanpy._compat import DaskArray, pkg_version
 
 if TYPE_CHECKING:
     from collections.abc import Iterable
 
     from numpy.typing import NDArray
 
+    from scanpy._compat import DaskArray
+
 
 # TODO: Report more context on the fields being compared on error
 # TODO: Allow specifying paths to ignore on comparison
@@ -128,14 +129,6 @@ def as_dense_dask_array(*args, **kwargs) -> DaskArray:
     from anndata.tests.helpers import as_dense_dask_array
 
     a = as_dense_dask_array(*args, **kwargs)
-    # Newer versions of as_dense_dask_array chunk all axes by halve when the input is not a dask array.
-    if (
-        pkg_version("anndata") < Version("0.11")
-        and not isinstance(args[0], DaskArray)  # keep chunksize intact
-    ):
-        from anndata.tests.helpers import _half_chunk_size
-
-        a = a.rechunk(_half_chunk_size(a.shape))
     return a
 
 

src/testing/scanpy/_pytest/fixtures/data.py

@@ -61,7 +61,7 @@ def random_csr(rng: np.random.Generator, size: tuple[int, int]) -> CSRBase:
 
 
 @pytest.fixture(
-    params=[np.random.Generator.standard_normal, random_csr], ids=["sparse", "dense"]
+    params=[np.random.Generator.standard_normal, random_csr], ids=["dense", "sparse"]
 )
 def backed_adata(request: pytest.FixtureRequest, tmp_path: Path) -> AnnData:
     rng = np.random.default_rng()

src/testing/scanpy/_pytest/params.py

@@ -24,11 +24,6 @@
     from ....scanpy._compat import DaskArray
 
 
-skipif_no_sparray = pytest.mark.skipif(
-    Version(version("anndata")) < Version("0.11"),
-    reason="scipy cs{rc}_array not supported in anndata<0.11",
-)
-
 anndata_test_utils_supports_typ_kwarg = Version(version("anndata")) >= Version("0.12.6")
 
 
@@ -84,7 +79,7 @@ def wrapper(a: np.ndarray) -> DaskArray:
     ("mem", "sparse"): (
         pytest.param(sparse.csr_matrix, id="scipy_csr_mat"),  # noqa: TID251
         pytest.param(sparse.csc_matrix, id="scipy_csc_mat"),  # noqa: TID251
-        pytest.param(sparse.csr_array, id="scipy_csr_arr", marks=[skipif_no_sparray]),  # noqa: TID251
+        pytest.param(sparse.csr_array, id="scipy_csr_arr"),  # noqa: TID251
     ),
     ("dask", "dense"): tuple(
         pytest.param(

tests/test_aggregated.py

@@ -7,11 +7,10 @@
 import numpy as np
 import pandas as pd
 import pytest
-from packaging.version import Version
 from scipy import sparse
 
 import scanpy as sc
-from scanpy._compat import DaskArray, pkg_version
+from scanpy._compat import DaskArray
 from scanpy._utils import _resolve_axis, get_literal_vals
 from scanpy.get._aggregated import AggType
 from testing.scanpy._helpers import assert_equal
@@ -84,19 +83,10 @@ def test_aggregate_vs_pandas(
     remove_unused_categories: bool,
 ) -> None:
     adata = pbmc3k_processed().raw.to_adata()
-    anndata_has_settings = pkg_version("anndata") >= Version("0.11")
     cat_col = adata.obs["louvain"]
     categories = cat_col.cat.categories
-    if anndata_has_settings:
-        with ad.settings.override(remove_unused_categories=remove_unused_categories):
-            adata = adata[cat_col.isin(categories[:5]), :1_000].copy()
-    else:
-        del adata.obs["louvain"]
-        mask = cat_col.isin(categories[:5])
-        adata = adata[mask, :1_000].copy()
-        adata.obs["louvain"] = cat_col[mask]
-        if remove_unused_categories:
-            adata.obs["louvain"] = adata.obs["louvain"].cat.remove_unused_categories()
+    with ad.settings.override(remove_unused_categories=remove_unused_categories):
+        adata = adata[cat_col.isin(categories[:5]), :1_000].copy()
     adata.X = array_type(adata.X)
     if with_na:
         nas = list(range(0, adata.shape[0], 5))