ci: pre-commit autoupdate

.pre-commit-config.yaml

@@ -6,23 +6,23 @@ repos:
   - id: trailing-whitespace
   - id: no-commit-to-branch
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.15.20
+  rev: v0.15.21
   hooks:
   - id: ruff-check
     args: [--fix, --exit-non-zero-on-fix]
   - id: ruff-check
     args: [--preview, --select=CPY]
   - id: ruff-format
 - repo: https://github.com/tox-dev/pyproject-fmt
-  rev: v2.25.1
+  rev: v2.25.2
   hooks:
   - id: pyproject-fmt
 - repo: https://github.com/biomejs/pre-commit
-  rev: v2.5.2
+  rev: v2.5.3
   hooks:
   - id: biome-format
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v2.1.0
+  rev: v2.3.0
   hooks:
   - id: mypy
     args: [--config-file=pyproject.toml, .]

src/fast_array_utils/conv/_to_dense.py

@@ -21,7 +21,7 @@
 # fallback’s arg0 type has to include types of registered functions
 @singledispatch
 def to_dense_(
-    x: CpuArray | GpuArray | DiskArray | types.DaskArray | types.sparray | types.spmatrix | types.CupySpMatrix | types.HasArrayNamespace,
+    x: CpuArray | GpuArray | DiskArray | types.CSDataset | types.DaskArray | types.sparray | types.spmatrix | types.CupySpMatrix | types.HasArrayNamespace,
     /,
     *,
     order: Literal["K", "A", "C", "F"] = "K",
@@ -64,8 +64,7 @@ def _to_dense_ooc(x: types.CSDataset, /, *, order: Literal["K", "A", "C", "F"] =
     if not to_cpu_memory:
         msg = "to_cpu_memory must be True if x is an CS{R,C}Dataset"
         raise ValueError(msg)
-    # TODO(flying-sheep): why is to_memory of type Any?  # noqa: TD003
-    return to_dense(cast("types.CSBase", x.to_memory()), order=sparse_order(x, order=order))
+    return to_dense(x.to_memory(), order=sparse_order(x, order=order))
 
 
 @to_dense_.register(types.CupyArray | types.CupySpMatrix)

src/fast_array_utils/stats/_power.py

@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 from functools import singledispatch
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, cast
 
 import numpy as np
 
@@ -47,10 +47,10 @@ def _power_array_api[A: AArray[object, object]](x: A, n: int, /, dtype: DTypeLik
 @_power.register(types.CSBase | types.CupyCSMatrix)
 def _power_cs[Mat: types.CSBase | types.CupyCSMatrix](x: Mat, n: int, /, dtype: DTypeLike | None = None) -> Mat:
     new_data = power(x.data, n, dtype=dtype)
-    return type(x)((new_data, x.indices, x.indptr), shape=x.shape, dtype=new_data.dtype)  # type: ignore[call-overload,return-value]
+    return type(x)((new_data, x.indices, x.indptr), shape=x.shape, dtype=new_data.dtype)  # type: ignore[call-overload]
 
 
 @_power.register(types.DaskArray)
 def _power_dask(x: types.DaskArray, n: int, /, dtype: DTypeLike | None = None) -> types.DaskArray:
-    meta = x._meta.astype(dtype or x.dtype)  # noqa: SLF001
-    return x.map_blocks(lambda c: power(c, n, dtype=dtype), dtype=dtype, meta=meta)  # type: ignore[type-var,arg-type]
+    meta = cast("CpuArray | GpuArray", x._meta.astype(dtype or x.dtype))  # noqa: SLF001  # https://github.com/python/mypy/issues/16826
+    return x.map_blocks(lambda c: power(c, n, dtype=dtype), dtype=dtype, meta=meta)

src/fast_array_utils/stats/_utils.py

@@ -54,7 +54,7 @@ def to_scalar(a: types.CupyArray | NDArray[Any]) -> np.number[Any]:
             a = a.get()
         return a.reshape(())[()]  # type: ignore[return-value]
 
-    return rv.map_blocks(to_scalar, meta=x.dtype.type(0))  # type: ignore[arg-type]
+    return rv.map_blocks(to_scalar, meta=x.dtype.type(0))
 
 
 def _dask_block(

src/fast_array_utils/types.py

@@ -111,7 +111,7 @@
 
 
 if TYPE_CHECKING:
-    from anndata.abc import CSCDataset, CSRDataset  # type: ignore[import-untyped]
+    from anndata.abc import CSCDataset, CSRDataset
 else:  # pragma: no cover
     try:  # only exists in anndata 0.11+
         from anndata.abc import CSCDataset, CSRDataset

src/testing/fast_array_utils/_array_type.py

@@ -146,7 +146,7 @@ def cls(self) -> type[Arr]:  # noqa: PLR0911
 
                 return cast("type[Arr]", zarr.Array)
             case "anndata.abc", ("CSCDataset" | "CSRDataset") as cls_name, _:
-                import anndata.abc  # type: ignore[import-untyped]
+                import anndata.abc
 
                 return cast("type[Arr]", getattr(anndata.abc, cls_name))
             case _:
@@ -188,13 +188,7 @@ def random(
                 import dask.array as da
 
                 arr = da.zeros(shape, dtype=dtype, chunks=_half_chunk_size(shape))
-                return cast(
-                    "Arr",
-                    arr.map_blocks(
-                        lambda x: self.random(x.shape, dtype=x.dtype, gen=gen, density=density),  # type: ignore[attr-defined]
-                        dtype=dtype,
-                    ),
-                )
+                return cast("Arr", arr.map_blocks(lambda x: self.random(x.shape, dtype=x.dtype, gen=gen, density=density), dtype=dtype))
             case "h5py", "Dataset", _:
                 raise NotImplementedError
             case "zarr", "Array", _:
@@ -235,7 +229,7 @@ def __call__(self, x: ArrayLike | Array, /, *, dtype: DTypeLike | None = None) -
     @staticmethod
     def _to_numpy_array(x: ArrayLike | Array, /, *, dtype: DTypeLike | None = None) -> NDArray[np.number[Any]]:
         """Convert to a numpy array."""
-        x = to_dense(x, to_cpu_memory=True)
+        x = to_dense(x, to_cpu_memory=True)  # type: ignore[arg-type]  # doesn’t officially handle ArrayLike
         return x if dtype is None else x.astype(dtype)
 
     def _to_dask_array(self, x: ArrayLike | Array, /, *, dtype: DTypeLike | None = None) -> types.DaskArray:
@@ -277,7 +271,7 @@ def _to_zarr_array(cls, x: ArrayLike | Array, /, *, dtype: DTypeLike | None = No
 
     def _to_cs_dataset(self, x: ArrayLike | Array, /, *, dtype: DTypeLike | None = None) -> types.CSDataset:
         """Convert to a scipy sparse dataset."""
-        import anndata.io  # type: ignore[import-untyped]
+        import anndata.io
         from scipy.sparse import csc_array, csr_array
 
         assert self.inner is not None
@@ -298,7 +292,7 @@ def _to_cs_dataset(self, x: ArrayLike | Array, /, *, dtype: DTypeLike | None = N
         cls = cast("type[types.csr_array[Any, tuple[int, int]] | types.csc_array]", csr_array if self.cls is types.CSRDataset else csc_array)
         x_sparse = self._to_scipy_sparse(x, dtype=dtype, cls=cls)
         anndata.io.write_elem(grp, "/mtx", x_sparse)
-        return anndata.io.sparse_dataset(grp["mtx"])
+        return anndata.io.sparse_dataset(cast("types.H5Group | types.ZarrGroup", grp["mtx"]))
 
     def _to_scipy_sparse(
         self,
@@ -314,7 +308,7 @@ def _to_scipy_sparse(
         if isinstance(x, types.CupySpMatrix):
             x = x.get()  # can be a coo_matrix due to dask concatenation
         elif not isinstance(x, types.spmatrix | types.sparray | np.ndarray):
-            x = to_dense(x, to_cpu_memory=True)
+            x = to_dense(x, to_cpu_memory=True)  # type: ignore[arg-type]  # doesn’t officially handle ArrayLike
 
         cls = cast("type[types.CSBase]", cls or self.cls)
         return cls(x, dtype=dtype)  # type: ignore[arg-type,misc]

tests/test_jax.py

@@ -98,7 +98,7 @@ def test_mean_var(subtests: pytest.Subtests, jax_arr: jax.Array, axis: Literal[0
 def test_to_dense(*, jax_arr: jax.Array, to_cpu_memory: bool) -> None:
     import jax.numpy as jnp
 
-    result = to_dense(jax_arr, to_cpu_memory=to_cpu_memory)
+    result = to_dense(jax_arr, to_cpu_memory=to_cpu_memory)  # type: ignore[call-overload]  # https://github.com/python/mypy/issues/16777
 
     if to_cpu_memory:
         assert isinstance(result, np.ndarray)

tests/test_stats.py

@@ -155,7 +155,7 @@ def test_ndim_error(
     arr = array_type(np_arr)
 
     with pytest.raises(AxisError):
-        func(arr, axis=axis)
+        func(arr, axis=axis)  # type: ignore[arg-type]  # https://github.com/python/mypy/issues/16777
 
 
 @pytest.mark.array_type(skip=ATS_SPARSE_DS)

tests/test_to_dense.py

@@ -41,7 +41,7 @@ def test_to_dense(array_type: ArrayType[Array], *, order: Literal["K", "C", "F"]
         else nullcontext(),
         WARNS_NUMBA if issubclass(array_type.cls, types.CSBase) and not find_spec("numba") else nullcontext(),
     ):
-        arr = to_dense(x, order=order, to_cpu_memory=to_cpu_memory)
+        arr = to_dense(x, order=order, to_cpu_memory=to_cpu_memory)  # type: ignore[arg-type]  # https://github.com/python/mypy/issues/16777
 
     assert_expected_cls(x, arr, to_cpu_memory=to_cpu_memory)
     assert arr.shape == (2, 3)
@@ -56,7 +56,7 @@ def test_to_dense_extra(coo_matrix_type: ArrayType[types.COOBase | types.CupyCOO
     src_mtx = coo_matrix_type([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
 
     with WARNS_NUMBA if not find_spec("numba") else nullcontext():
-        arr = to_dense(src_mtx, order=order, to_cpu_memory=to_cpu_memory)
+        arr = to_dense(src_mtx, order=order, to_cpu_memory=to_cpu_memory)  # type: ignore[arg-type]  # https://github.com/python/mypy/issues/16777
 
     assert_expected_cls(src_mtx, arr, to_cpu_memory=to_cpu_memory)
     assert arr.shape == (2, 3)

typings/dask/array/core.pyi

@@ -63,7 +63,7 @@ class Array:
     def map_blocks(
         self,
         # TODO(flying-sheep): make this generic, _Array the default  # noqa: TD003
-        func: Callable[[object], object],
+        func: Callable[..., object],
         *args: Never,
         name: str | None = None,
         token: str | None = None,
@@ -89,7 +89,7 @@ def from_array(
 ) -> Array: ...
 def map_blocks(
     # TODO(flying-sheep): make this generic, _Array the default  # noqa: TD003
-    func: Callable[[object], object],
+    func: Callable[..., object],
     *args: Array,
     name: str | None = None,
     token: str | None = None,