chore: upgrade to spec0

hatch.toml

@@ -30,7 +30,7 @@ overrides.matrix.deps.pre-install-commands = [
 
 ]
 overrides.matrix.deps.python = [
-    { if = [ "min" ], value = "3.11" },
+    { if = [ "min" ], value = "3.12" },
     { if = [ "stable", "pre" ], value = "3.13" },
 ]
 overrides.matrix.deps.features = [

pyproject.toml

@@ -5,7 +5,7 @@ requires = [ "hatchling", "hatch-vcs" ]
 [project]
 name = "anndata"
 description = "Annotated data."
-requires-python = ">=3.11"
+requires-python = ">=3.12"
 license = "BSD-3-Clause"
 authors = [
     { name = "Philipp Angerer" },
@@ -29,18 +29,17 @@ classifiers = [
     "Operating System :: Microsoft :: Windows",
     "Operating System :: POSIX :: Linux",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
     "Topic :: Scientific/Engineering :: Bio-Informatics",
     "Topic :: Scientific/Engineering :: Visualization",
 ]
 dependencies = [
-    "pandas >=2.1.0, !=2.1.2",
+    "pandas >=2.2.2",
     "numpy>=1.26",
     # https://github.com/scverse/anndata/issues/1434
-    "scipy >=1.12",
-    "h5py>=3.8",
+    "scipy >=1.13",
+    "h5py>=3.10",
     "natsort",
     "packaging>=24.2",
     "array_api_compat>=1.7.1",
@@ -108,7 +107,7 @@ lazy = [ "xarray>=2025.06.1", "aiohttp", "requests", "anndata[dask]" ]
 # https://github.com/dask/dask/issues/11290
 # https://github.com/dask/dask/issues/11752
 dask = [
-    "dask[array]>=2023.5.1,!=2024.8.*,!=2024.9.*,!=2025.2.*,!=2025.3.*,!=2025.4.*,!=2025.5.*,!=2025.6.*,!=2025.7.*,!=2025.8.*",
+    "dask[array]>=2023.10.1,!=2024.8.*,!=2024.9.*,!=2025.2.*,!=2025.3.*,!=2025.4.*,!=2025.5.*,!=2025.6.*,!=2025.7.*,!=2025.8.*",
 ]
 
 [tool.hatch.version]

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
+from typing import TYPE_CHECKING, TypeVar
 
 import numpy as np
 import pandas as pd
@@ -135,7 +135,7 @@ def as_dict(self) -> dict:
         return dict(self)
 
 
-class AlignedView(AlignedMappingBase, Generic[P, I]):
+class AlignedView[P: "AlignedMappingBase", I: (OneDIdx, TwoDIdx)](AlignedMappingBase):
     is_view: ClassVar[Literal[True]] = True
 
     # override docstring
@@ -394,7 +394,7 @@ class PairwiseArraysView(AlignedView[PairwiseArraysBase, OneDIdx], PairwiseArray
 
 
 @dataclass
-class AlignedMappingProperty(property, Generic[T]):
+class AlignedMappingProperty[T: AlignedMapping](property):
     """A :class:`property` that creates an ephemeral AlignedMapping.
 
     The actual data is stored as `f'_{self.name}'` in the parent object.

src/anndata/_core/extensions.py

@@ -2,7 +2,7 @@
 
 import inspect
 from pathlib import Path
-from typing import TYPE_CHECKING, Generic, TypeVar, get_type_hints, overload
+from typing import TYPE_CHECKING, TypeVar, get_type_hints, overload
 from warnings import warn
 
 from ..types import ExtensionNamespace
@@ -60,7 +60,7 @@ def find_stacklevel() -> int:
 T = TypeVar("T")
 
 
-class AccessorNameSpace(ExtensionNamespace, Generic[NameSpT]):
+class AccessorNameSpace[NameSpT: ExtensionNamespace](ExtensionNamespace):
     """Establish property-like namespace object for user-defined functionality."""
 
     def __init__(self, name: str, namespace: type[NameSpT]) -> None:

src/anndata/_core/merge.py

@@ -428,7 +428,7 @@ def _dask_block_diag(mats):
 ###################
 
 
-def unique_value(vals: Collection[T]) -> T | MissingVal:
+def unique_value[T](vals: Collection[T]) -> T | MissingVal:
     """
     Given a collection vals, returns the unique value (if one exists), otherwise
     returns MissingValue.
@@ -440,7 +440,7 @@ def unique_value(vals: Collection[T]) -> T | MissingVal:
     return unique_val
 
 
-def first(vals: Collection[T]) -> T | MissingVal:
+def first[T](vals: Collection[T]) -> T | MissingVal:
     """
     Given a collection of vals, return the first non-missing one.If they're all missing,
     return MissingVal.
@@ -451,7 +451,7 @@ def first(vals: Collection[T]) -> T | MissingVal:
     return MissingVal
 
 
-def only(vals: Collection[T]) -> T | MissingVal:
+def only[T](vals: Collection[T]) -> T | MissingVal:
     """Return the only value in the collection, otherwise MissingVal."""
     if len(vals) == 1:
         return vals[0]

src/anndata/_io/specs/lazy_methods.py

@@ -53,9 +53,9 @@ def maybe_open_h5(
 ) -> Generator[H5File, None, None]: ...
 @overload
 @contextmanager
-def maybe_open_h5(path_or_other: D, elem_name: str) -> Generator[D, None, None]: ...
+def maybe_open_h5[D](path_or_other: D, elem_name: str) -> Generator[D, None, None]: ...
 @contextmanager
-def maybe_open_h5(
+def maybe_open_h5[D](
     path_or_other: H5File | D, elem_name: str
 ) -> Generator[H5File | D, None, None]:
     if not isinstance(path_or_other, Path):
@@ -81,7 +81,7 @@ def compute_chunk_layout_for_axis_size(
     return chunk
 
 
-def make_dask_chunk(
+def make_dask_chunk[D](
     path_or_sparse_dataset: Path | D,
     elem_name: str,
     block_info: BlockInfo | None = None,

src/anndata/_io/specs/registry.py

@@ -6,7 +6,7 @@
 from dataclasses import dataclass
 from functools import partial, singledispatch, wraps
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Generic, TypeVar
+from typing import TYPE_CHECKING, TypeVar
 
 from anndata._io.utils import report_read_key_on_error, report_write_key_on_error
 from anndata._settings import settings
@@ -84,13 +84,13 @@ def wrapper(g: GroupStorageType, k: str, *args, **kwargs):
     return decorator
 
 
-_R = TypeVar("_R", _ReadInternal, _ReadLazyInternal)
+RI = TypeVar("RI", _ReadInternal, _ReadLazyInternal)
 R = TypeVar("R", Read, ReadLazy)
 
 
-class IORegistry(Generic[_R, R]):
+class IORegistry[RI: (_ReadInternal, _ReadLazyInternal), R: (Read, ReadLazy)]:
     def __init__(self):
-        self.read: dict[tuple[type, IOSpec, frozenset[str]], _R] = {}
+        self.read: dict[tuple[type, IOSpec, frozenset[str]], RI] = {}
         self.read_partial: dict[tuple[type, IOSpec, frozenset[str]], Callable] = {}
         self.write: dict[
             tuple[type, type | tuple[type, str], frozenset[str]], _WriteInternal
@@ -155,7 +155,7 @@ def register_read(
         src_type: type,
         spec: IOSpec | Mapping[str, str],
         modifiers: Iterable[str] = frozenset(),
-    ) -> Callable[[_R], _R]:
+    ) -> Callable[[RI], RI]:
         spec = proc_spec(spec)
         modifiers = frozenset(modifiers)
 

src/anndata/_settings.py

@@ -11,7 +11,7 @@
 from functools import partial
 from inspect import Parameter, signature
 from types import GenericAlias
-from typing import TYPE_CHECKING, Generic, NamedTuple, TypeVar, cast
+from typing import TYPE_CHECKING, NamedTuple, TypeVar, cast
 
 from .compat import is_zarr_v2, old_positionals
 
@@ -51,7 +51,7 @@ def describe(self: RegisteredOption, *, as_rst: bool = False) -> str:
     return textwrap.dedent(doc)
 
 
-class RegisteredOption(NamedTuple, Generic[T]):
+class RegisteredOption[T](NamedTuple):
     option: str
     default_value: T
     description: str
@@ -61,7 +61,7 @@ class RegisteredOption(NamedTuple, Generic[T]):
     describe = describe
 
 
-def check_and_get_environ_var(
+def check_and_get_environ_var[T](
     key: str,
     default_value: str,
     allowed_values: Sequence[str] | None = None,
@@ -394,7 +394,7 @@ def __doc__(self):
 V = TypeVar("V")
 
 
-def gen_validator(_type: type[V]) -> Callable[[V], None]:
+def gen_validator[V](_type: type[V]) -> Callable[[V], None]:
     def validate_type(val: V) -> None:
         if not isinstance(val, _type):
             msg = f"{val} not valid {_type}"

src/anndata/_types.py

@@ -11,7 +11,7 @@
 
 if TYPE_CHECKING:
     from collections.abc import Mapping
-    from typing import Any, TypeAlias
+    from typing import Any
 
     from anndata._core.xarray import Dataset2D
 
@@ -33,9 +33,9 @@
     "_WriteInternal",
 ]
 
-ArrayStorageType: TypeAlias = ZarrArray | H5Array
-GroupStorageType: TypeAlias = ZarrGroup | H5Group
-StorageType: TypeAlias = ArrayStorageType | GroupStorageType
+type ArrayStorageType = ZarrArray | H5Array
+type GroupStorageType = ZarrGroup | H5Group
+type StorageType = ArrayStorageType | GroupStorageType
 
 # NOTE: If you change these, be sure to update `autodoc_type_aliases` in docs/conf.py!
 RWAble_contra = TypeVar("RWAble_contra", bound=typing.RWAble, contravariant=True)

src/anndata/compat/__init__.py

@@ -330,7 +330,7 @@ def _to_fixed_length_strings(value: np.ndarray) -> np.ndarray:
 
 # TODO: This is a workaround for https://github.com/scverse/anndata/issues/874
 # See https://github.com/h5py/h5py/pull/2311#issuecomment-1734102238 for why this is done this way.
-def _require_group_write_dataframe(
+def _require_group_write_dataframe[Group_T: ZarrGroup | h5py.Group](
     f: Group_T, name: str, df: pd.DataFrame, *args, **kwargs
 ) -> Group_T:
     if len(df.columns) > 5_000 and isinstance(f, H5Group):

src/anndata/experimental/backed/_lazy_arrays.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from functools import cached_property
-from typing import TYPE_CHECKING, Generic, TypeVar
+from typing import TYPE_CHECKING, TypeVar
 
 import numpy as np
 import pandas as pd
@@ -32,7 +32,7 @@
 K = TypeVar("K", H5Array, ZarrArray)
 
 
-class ZarrOrHDF5Wrapper(XZarrArrayWrapper, Generic[K]):
+class ZarrOrHDF5Wrapper[K: (H5Array, ZarrArray)](XZarrArrayWrapper):
     def __init__(self, array: K):
         self.chunks = array.chunks
         if isinstance(array, ZarrArray):
@@ -75,7 +75,7 @@ def _getitem(self, key: tuple[int | np.integer | slice | np.ndarray]):
         return self._array[key]
 
 
-class CategoricalArray(XBackendArray, Generic[K]):
+class CategoricalArray[K: (H5Array, ZarrArray)](XBackendArray):
     """
     A wrapper class meant to enable working with lazy categorical data.
     We do not guarantee the stability of this API beyond that guaranteed
@@ -130,7 +130,7 @@ def dtype(self):
         return pd.CategoricalDtype(categories=self.categories, ordered=self._ordered)
 
 
-class MaskedArray(XBackendArray, Generic[K]):
+class MaskedArray[K: (H5Array, ZarrArray)](XBackendArray):
     """
     A wrapper class meant to enable working with lazy masked data.
     We do not guarantee the stability of this API beyond that guaranteed

src/anndata/experimental/pytorch/_annloader.py

@@ -22,12 +22,12 @@
 
 if TYPE_CHECKING:
     from collections.abc import Callable, Generator, Sequence
-    from typing import TypeAlias, Union
+    from typing import Union
 
     from scipy.sparse import spmatrix
 
     # need to use Union because of autodoc_mock_imports
-    Array: TypeAlias = Union[torch.Tensor, np.ndarray, spmatrix]  # noqa: UP007
+    type Array = Union[torch.Tensor, np.ndarray, spmatrix]  # noqa: UP007
 
 
 # Custom sampler to get proper batches instead of joined separate indices

src/anndata/typing.py

@@ -32,7 +32,8 @@
 Index = _Index
 """1D or 2D index an :class:`~anndata.AnnData` object can be sliced with."""
 
-XDataType: TypeAlias = (
+# Both of the following two types are used with `get_args` hence the need for `TypeAlias`
+XDataType: TypeAlias = (  # noqa: UP040
     np.ndarray
     | ma.MaskedArray
     | CSMatrix
@@ -46,20 +47,18 @@
     | CupyArray
     | CupySparseMatrix
 )
-ArrayDataStructureTypes: TypeAlias = XDataType | AwkArray | XDataArray
+ArrayDataStructureTypes: TypeAlias = XDataType | AwkArray | XDataArray  # noqa: UP040
 
 
-InMemoryArrayOrScalarType: TypeAlias = (
+type InMemoryArrayOrScalarType = (
     pd.DataFrame | np.number | str | ArrayDataStructureTypes
 )
 
 
-AxisStorable: TypeAlias = (
+type AxisStorable = (
     InMemoryArrayOrScalarType | dict[str, "AxisStorable"] | list["AxisStorable"]
 )
 """A serializable object, excluding :class:`anndata.AnnData` objects i.e., something that can be stored in `uns` or `obsm`."""
 
-RWAble: TypeAlias = (
-    AxisStorable | AnnData | pd.Categorical | pd.api.extensions.ExtensionArray
-)
+type RWAble = AxisStorable | AnnData | pd.Categorical | pd.api.extensions.ExtensionArray
 """A superset of :type:`anndata.typing.AxisStorable` (i.e., including :class:`anndata.AnnData`) which is everything can be read/written by :func:`anndata.io.read_elem` and :func:`anndata.io.write_elem`."""

tests/test_io_elementwise.py

@@ -76,7 +76,7 @@ def create_dense_store(
     return store
 
 
-def create_sparse_store(
+def create_sparse_store[G: (H5Group, ZarrGroup)](
     sparse_format: Literal["csc", "csr"], store: G, shape=DEFAULT_SHAPE
 ) -> G:
     """Returns a store
@@ -225,7 +225,9 @@ def test_io_spec(store, value, encoding_type):
         pytest.param(np.asarray("test"), "string", id="scalar_string"),
     ],
 )
-def test_io_spec_compressed_scalars(store: G, value: np.ndarray, encoding_type: str):
+def test_io_spec_compressed_scalars(
+    store: H5Group | ZarrGroup, value: np.ndarray, encoding_type: str
+):
     key = f"key_for_{encoding_type}"
     write_elem(
         store, key, value, dataset_kwargs={"compression": "gzip", "compression_opts": 5}