(fix): fix ruff issues

src/anndata/_core/aligned_mapping.py

@@ -5,7 +5,7 @@
 from collections.abc import MutableMapping, Sequence
 from copy import copy
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Generic, TypeVar, Union
+from typing import TYPE_CHECKING, Generic, TypeVar
 
 import numpy as np
 import pandas as pd
@@ -33,10 +33,10 @@
     from .raw import Raw
 
 
-OneDIdx = Union[Sequence[int], Sequence[bool], slice]
+OneDIdx = Sequence[int] | Sequence[bool] | slice
 TwoDIdx = tuple[OneDIdx, OneDIdx]
 # TODO: pd.DataFrame only allowed in AxisArrays?
-Value = Union[pd.DataFrame, spmatrix, np.ndarray]
+Value = pd.DataFrame | spmatrix | np.ndarray
 
 P = TypeVar("P", bound="AlignedMappingBase")
 """Parent mapping an AlignedView is based on."""
@@ -376,9 +376,14 @@ class PairwiseArraysView(AlignedView[PairwiseArraysBase, OneDIdx], PairwiseArray
 PairwiseArraysBase._actual_class = PairwiseArrays
 
 
-AlignedMapping = Union[
-    AxisArrays, AxisArraysView, Layers, LayersView, PairwiseArrays, PairwiseArraysView
-]
+AlignedMapping = (
+    AxisArrays
+    | AxisArraysView
+    | Layers
+    | LayersView
+    | PairwiseArrays
+    | PairwiseArraysView
+)
 T = TypeVar("T", bound=AlignedMapping)
 """Pair of types to be aligned."""
 
@@ -408,9 +413,7 @@ def fget(self) -> Callable[[], None]:
 
         def fake(): ...
 
-        fake.__annotations__ = {
-            "return": Union[self.cls._actual_class, self.cls._view_class]
-        }
+        fake.__annotations__ = {"return": self.cls._actual_class | self.cls._view_class}
         return fake
 
     def __get__(self, obj: None | AnnData, objtype: type | None = None) -> T:

src/anndata/_core/anndata.py

@@ -272,12 +272,12 @@ def _init_as_view(self, adata_ref: AnnData, oidx: Index, vidx: Index):
                 "that is, you cannot make a view of a view."
             )
         self._is_view = True
-        if isinstance(oidx, (int, np.integer)):
+        if isinstance(oidx, int | np.integer):
             if not (-adata_ref.n_obs <= oidx < adata_ref.n_obs):
                 raise IndexError(f"Observation index `{oidx}` is out of range.")
             oidx += adata_ref.n_obs * (oidx < 0)
             oidx = slice(oidx, oidx + 1, 1)
-        if isinstance(vidx, (int, np.integer)):
+        if isinstance(vidx, int | np.integer):
             if not (-adata_ref.n_vars <= vidx < adata_ref.n_vars):
                 raise IndexError(f"Variable index `{vidx}` is out of range.")
             vidx += adata_ref.n_vars * (vidx < 0)
@@ -406,7 +406,7 @@ def _init_as_actual(
                     #       as in readwrite.read_10x_h5
                     if X.dtype != np.dtype(dtype):
                         X = X.astype(dtype)
-                elif isinstance(X, (ZarrArray, DaskArray)):
+                elif isinstance(X, ZarrArray | DaskArray):
                     X = X.astype(dtype)
                 else:  # is np.ndarray or a subclass, convert to true np.ndarray
                     X = np.asarray(X, dtype)
@@ -763,16 +763,14 @@ def _prep_dim_index(self, value, attr: str) -> pd.Index:
             raise ValueError(
                 f"Length of passed value for {attr}_names is {len(value)}, but this AnnData has shape: {self.shape}"
             )
-        if isinstance(value, pd.Index) and not isinstance(
-            value.name, (str, type(None))
-        ):
+        if isinstance(value, pd.Index) and not isinstance(value.name, str | type(None)):
             raise ValueError(
                 f"AnnData expects .{attr}.index.name to be a string or None, "
                 f"but you passed a name of type {type(value.name).__name__!r}"
             )
         else:
             value = pd.Index(value)
-            if not isinstance(value.name, (str, type(None))):
+            if not isinstance(value.name, str | type(None)):
                 value.name = None
         if (
             len(value) > 0
@@ -1976,7 +1974,7 @@ def chunk_X(
         if isinstance(select, int):
             select = select if select < self.n_obs else self.n_obs
             choice = np.random.choice(self.n_obs, select, replace)
-        elif isinstance(select, (np.ndarray, Sequence)):
+        elif isinstance(select, np.ndarray | Sequence):
             choice = np.asarray(select)
         else:
             raise ValueError("select should be int or array")

src/anndata/_core/index.py

@@ -70,25 +70,25 @@ def name_idx(i):
             stop = None if stop is None else stop + 1
         step = indexer.step
         return slice(start, stop, step)
-    elif isinstance(indexer, (np.integer, int)):
+    elif isinstance(indexer, np.integer | int):
         return indexer
     elif isinstance(indexer, str):
         return index.get_loc(indexer)  # int
     elif isinstance(
-        indexer, (Sequence, np.ndarray, pd.Index, spmatrix, np.matrix, SpArray)
+        indexer, Sequence | np.ndarray | pd.Index | spmatrix | np.matrix | SpArray
     ):
         if hasattr(indexer, "shape") and (
             (indexer.shape == (index.shape[0], 1))
             or (indexer.shape == (1, index.shape[0]))
         ):
-            if isinstance(indexer, (spmatrix, SpArray)):
+            if isinstance(indexer, spmatrix | SpArray):
                 indexer = indexer.toarray()
             indexer = np.ravel(indexer)
-        if not isinstance(indexer, (np.ndarray, pd.Index)):
+        if not isinstance(indexer, np.ndarray | pd.Index):
             indexer = np.array(indexer)
             if len(indexer) == 0:
                 indexer = indexer.astype(int)
-        if issubclass(indexer.dtype.type, (np.integer, np.floating)):
+        if issubclass(indexer.dtype.type, np.integer | np.floating):
             return indexer  # Might not work for range indexes
         elif issubclass(indexer.dtype.type, np.bool_):
             if indexer.shape != index.shape:

src/anndata/_core/merge.py

@@ -174,7 +174,7 @@ def equal_sparse(a, b) -> bool:
 
     xp = array_api_compat.array_namespace(a.data)
 
-    if isinstance(b, (CupySparseMatrix, sparse.spmatrix, SpArray)):
+    if isinstance(b, CupySparseMatrix | sparse.spmatrix | SpArray):
         if isinstance(a, CupySparseMatrix):
             # Comparison broken for CSC matrices
             # https://github.com/cupy/cupy/issues/7757
@@ -206,7 +206,7 @@ def equal_awkward(a, b) -> bool:
 
 
 def as_sparse(x, use_sparse_array=False):
-    if not isinstance(x, (sparse.spmatrix, SpArray)):
+    if not isinstance(x, sparse.spmatrix | SpArray):
         if CAN_USE_SPARSE_ARRAY and use_sparse_array:
             return sparse.csr_array(x)
         return sparse.csr_matrix(x)
@@ -536,7 +536,7 @@ def apply(self, el, *, axis, fill_value=None):
             return el
         if isinstance(el, pd.DataFrame):
             return self._apply_to_df(el, axis=axis, fill_value=fill_value)
-        elif isinstance(el, (sparse.spmatrix, SpArray, CupySparseMatrix)):
+        elif isinstance(el, sparse.spmatrix | SpArray | CupySparseMatrix):
             return self._apply_to_sparse(el, axis=axis, fill_value=fill_value)
         elif isinstance(el, AwkArray):
             return self._apply_to_awkward(el, axis=axis, fill_value=fill_value)
@@ -723,7 +723,7 @@ def default_fill_value(els):
 
     This is largely due to backwards compat, and might not be the ideal solution.
     """
-    if any(isinstance(el, (sparse.spmatrix, SpArray)) for el in els):
+    if any(isinstance(el, sparse.spmatrix | SpArray) for el in els):
         return 0
     else:
         return np.nan
@@ -794,7 +794,7 @@ def concat_arrays(arrays, reindexers, axis=0, index=None, fill_value=None):
         import cupyx.scipy.sparse as cpsparse
 
         if not all(
-            isinstance(a, (CupySparseMatrix, CupyArray)) or 0 in a.shape for a in arrays
+            isinstance(a, CupySparseMatrix | CupyArray) or 0 in a.shape for a in arrays
         ):
             raise NotImplementedError(
                 "Cannot concatenate a cupy array with other array types."
@@ -821,7 +821,7 @@ def concat_arrays(arrays, reindexers, axis=0, index=None, fill_value=None):
             ],
             axis=axis,
         )
-    elif any(isinstance(a, (sparse.spmatrix, SpArray)) for a in arrays):
+    elif any(isinstance(a, sparse.spmatrix | SpArray) for a in arrays):
         sparse_stack = (sparse.vstack, sparse.hstack)[axis]
         use_sparse_array = any(issubclass(type(a), SpArray) for a in arrays)
         return sparse_stack(
@@ -980,7 +980,7 @@ def concat_pairwise_mapping(
         els = [
             m.get(k, sparse_class((s, s), dtype=bool)) for m, s in zip(mappings, shapes)
         ]
-        if all(isinstance(el, (CupySparseMatrix, CupyArray)) for el in els):
+        if all(isinstance(el, CupySparseMatrix | CupyArray) for el in els):
             result[k] = _cp_block_diag(els, format="csr")
         elif all(isinstance(el, DaskArray) for el in els):
             result[k] = _dask_block_diag(els)

src/anndata/_core/raw.py

@@ -40,7 +40,7 @@ def __init__(
         # construct manually
         if adata.isbacked == (X is None):
             # Move from GPU to CPU since it's large and not always used
-            if isinstance(X, (CupyArray, CupySparseMatrix)):
+            if isinstance(X, CupyArray | CupySparseMatrix):
                 self._X = X.get()
             else:
                 self._X = X
@@ -51,7 +51,7 @@ def __init__(
             self.varm = varm
         elif X is None:  # construct from adata
             # Move from GPU to CPU since it's large and not always used
-            if isinstance(adata.X, (CupyArray, CupySparseMatrix)):
+            if isinstance(adata.X, CupyArray | CupySparseMatrix):
                 self._X = adata.X.get()
             else:
                 self._X = adata.X.copy()
@@ -124,9 +124,9 @@ def __getitem__(self, index):
         oidx, vidx = self._normalize_indices(index)
 
         # To preserve two dimensional shape
-        if isinstance(vidx, (int, np.integer)):
+        if isinstance(vidx, int | np.integer):
             vidx = slice(vidx, vidx + 1, 1)
-        if isinstance(oidx, (int, np.integer)):
+        if isinstance(oidx, int | np.integer):
             oidx = slice(oidx, oidx + 1, 1)
 
         if not self._adata.isbacked:

src/anndata/_core/sparse_dataset.py

@@ -343,7 +343,7 @@ def _get_group_format(group: GroupStorageType) -> str:
 def is_sparse_indexing_overridden(format: Literal["csr", "csc"], row, col):
     major_indexer, minor_indexer = (row, col) if format == "csr" else (col, row)
     return isinstance(minor_indexer, slice) and (
-        (isinstance(major_indexer, (int, np.integer)))
+        (isinstance(major_indexer, int | np.integer))
         or (isinstance(major_indexer, slice))
         or (isinstance(major_indexer, np.ndarray) and major_indexer.ndim == 1)
     )

src/anndata/_io/h5ad.py

@@ -82,14 +82,14 @@ def write_h5ad(
         f.attrs.setdefault("encoding-version", "0.1.0")
 
         if "X" in as_dense and isinstance(
-            adata.X, (sparse.spmatrix, BaseCompressedSparseDataset)
+            adata.X, sparse.spmatrix | BaseCompressedSparseDataset
         ):
             write_sparse_as_dense(f, "X", adata.X, dataset_kwargs=dataset_kwargs)
         elif not (adata.isbacked and Path(adata.filename) == Path(filepath)):
             # If adata.isbacked, X should already be up to date
             write_elem(f, "X", adata.X, dataset_kwargs=dataset_kwargs)
         if "raw/X" in as_dense and isinstance(
-            adata.raw.X, (sparse.spmatrix, BaseCompressedSparseDataset)
+            adata.raw.X, sparse.spmatrix | BaseCompressedSparseDataset
         ):
             write_sparse_as_dense(
                 f, "raw/X", adata.raw.X, dataset_kwargs=dataset_kwargs

src/anndata/_io/read.py

@@ -337,7 +337,7 @@ def read_text(
     dtype
         Numpy data type.
     """
-    if not isinstance(filename, (PathLike, str, bytes)):
+    if not isinstance(filename, PathLike | str | bytes):
         return _read_text(filename, delimiter, first_column_names, dtype)
 
     filename = Path(filename)

src/anndata/_io/utils.py

@@ -1,23 +1,24 @@
 from __future__ import annotations
 
 from functools import wraps
+from itertools import pairwise
 from typing import TYPE_CHECKING, cast
 from warnings import warn
 
 import h5py
 from packaging.version import Version
 
 from .._core.sparse_dataset import BaseCompressedSparseDataset
-from ..compat import add_note, pairwise
+from ..compat import add_note
 
 if TYPE_CHECKING:
     from collections.abc import Callable
-    from typing import Literal, Union
+    from typing import Literal
 
     from .._types import StorageType
     from ..compat import H5Group, ZarrGroup
 
-    Storage = Union[StorageType, BaseCompressedSparseDataset]
+    Storage = StorageType | BaseCompressedSparseDataset
 
 # For allowing h5py v3
 # https://github.com/scverse/anndata/issues/442

src/anndata/compat/__init__.py

@@ -10,7 +10,7 @@
 from importlib.util import find_spec
 from inspect import Parameter, signature
 from pathlib import Path
-from typing import TYPE_CHECKING, TypeVar, Union
+from typing import TYPE_CHECKING, TypeVar
 from warnings import warn
 
 import h5py
@@ -46,8 +46,8 @@ class Empty:
     pass
 
 
-Index1D = Union[slice, int, str, np.int64, np.ndarray]
-Index = Union[Index1D, tuple[Index1D, Index1D], scipy.sparse.spmatrix, SpArray]
+Index1D = slice | int | str | np.int64 | np.ndarray
+Index = Index1D | tuple[Index1D, Index1D] | scipy.sparse.spmatrix | SpArray
 H5Group = h5py.Group
 H5Array = h5py.Dataset
 H5File = h5py.File
@@ -75,18 +75,6 @@ def __exit__(self, *_exc_info) -> None:
             os.chdir(self._old_cwd.pop())
 
 
-if sys.version_info >= (3, 10):
-    from itertools import pairwise
-else:
-
-    def pairwise(iterable):
-        from itertools import tee
-
-        a, b = tee(iterable)
-        next(b, None)
-        return zip(a, b)
-
-
 #############################
 # Optional deps
 #############################
@@ -319,7 +307,7 @@ def _clean_uns(adata: AnnData):  # noqa: F821
             continue
         name = cats_name.replace("_categories", "")
         # fix categories with a single category
-        if isinstance(cats, (str, int)):
+        if isinstance(cats, str | int):
             cats = [cats]
         for ann in [adata.obs, adata.var]:
             if name not in ann:
@@ -344,7 +332,7 @@ def _move_adj_mtx(d):
     for k in ("distances", "connectivities"):
         if (
             (k in n)
-            and isinstance(n[k], (scipy.sparse.spmatrix, np.ndarray))
+            and isinstance(n[k], scipy.sparse.spmatrix | np.ndarray)
             and len(n[k].shape) == 2
         ):
             warn(

src/anndata/experimental/merge.py

@@ -352,7 +352,7 @@ def _write_concat_sequence(
         )
         write_elem(output_group, output_path, df)
     elif all(
-        isinstance(a, (pd.DataFrame, BaseCompressedSparseDataset, H5Array, ZarrArray))
+        isinstance(a, pd.DataFrame | BaseCompressedSparseDataset | H5Array | ZarrArray)
         for a in arrays
     ):
         _write_concat_arrays(

src/anndata/experimental/multi_files/_anncollection.py

@@ -3,7 +3,7 @@
 import warnings
 from collections.abc import Callable, Mapping
 from functools import reduce
-from typing import TYPE_CHECKING, Union
+from typing import TYPE_CHECKING
 
 import numpy as np
 import pandas as pd
@@ -584,7 +584,7 @@ def attrs_keys(self):
 
 
 DictCallable = dict[str, Callable]
-ConvertType = Union[Callable, dict[str, Callable | DictCallable]]
+ConvertType = Callable | dict[str, Callable | DictCallable]
 
 
 class AnnCollection(_ConcatViewMixin, _IterateViewMixin):

tests/test_backed_hdf5.py

@@ -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 | sparse.spmatrix | SpArray)
+        and isinstance(var_idx, list | np.ndarray | sparse.spmatrix | SpArray)
     ):
         pytest.xfail(
             "Fancy indexing does not work with multiple arrays on a h5py.Dataset"