ci: ty

.pre-commit-config.yaml

@@ -6,13 +6,17 @@ repos:
   - id: trailing-whitespace
   - id: no-commit-to-branch
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.14.10
+  rev: v0.14.11
   hooks:
   - id: ruff-check
-    args: [--fix, --exit-non-zero-on-fix]
+    args: [--fix]
   - id: ruff-check
     args: [--preview, --select=CPY]
   - id: ruff-format
+- repo: https://github.com/allganize/ty-pre-commit
+  rev: v0.0.11
+  hooks:
+  - id: ty-check
 - repo: https://github.com/tox-dev/pyproject-fmt
   rev: v2.11.1
   hooks:
@@ -21,89 +25,3 @@ repos:
   rev: v2.3.10
   hooks:
   - id: biome-format
-- repo: https://github.com/H4rryK4ne/update-mypy-hook
-  rev: v0.3.0
-  hooks:
-  - id: update-mypy-hook
-- repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v1.19.1
-  hooks:
-  - id: mypy
-    args: [--config-file=pyproject.toml, .]
-    pass_filenames: false
-    language_version: '3.13'
-    additional_dependencies:
-    - alabaster==1.0.0
-    - anndata==0.12.6
-    - array-api-compat==1.12.0
-    - babel==2.17.0
-    - certifi==2025.11.12
-    - cffi==2.0.0
-    - charset-normalizer==3.4.4
-    - click==8.3.1
-    - cloudpickle==3.1.2
-    - colorama==0.4.6 ; sys_platform == 'win32'
-    - coverage==7.13.0
-    - dask==2025.11.0
-    - docutils==0.22.3
-    - donfig==0.8.1.post1
-    - execnet==2.1.2
-    - fsspec==2025.12.0
-    - google-crc32c==1.7.1
-    - h5py==3.15.1
-    - idna==3.11
-    - imagesize==1.4.1
-    - iniconfig==2.3.0
-    - jinja2==3.1.6
-    - joblib==1.5.2
-    - legacy-api-wrap==1.5
-    - llvmlite==0.46.0
-    - locket==1.0.0
-    - markdown-it-py==4.0.0
-    - markupsafe==3.0.3
-    - mdurl==0.1.2
-    - natsort==8.4.0
-    - numba==0.63.1
-    - numcodecs==0.16.5
-    - numpy==2.3.5
-    - numpy-typing-compat==20251206.2.3
-    - optype==0.15.0
-    - packaging==25.0
-    - pandas==2.3.3
-    - partd==1.4.2
-    - pluggy==1.6.0
-    - pycparser==2.23 ; implementation_name != 'PyPy'
-    - pygments==2.19.2
-    - pytest==9.0.2
-    - pytest-codspeed==4.2.0
-    - pytest-doctestplus==1.6.0
-    - pytest-xdist==3.8.0
-    - python-dateutil==2.9.0.post0
-    - pytz==2025.2
-    - pyyaml==6.0.3
-    - requests==2.32.5
-    - rich==14.2.0
-    - roman-numerals==3.1.0
-    - scikit-learn==1.8.0
-    - scipy==1.16.3
-    - scipy-stubs==1.16.3.3
-    - six==1.17.0
-    - snowballstemmer==3.0.1
-    - sphinx==9.0.4
-    - sphinxcontrib-applehelp==2.0.0
-    - sphinxcontrib-devhelp==2.0.0
-    - sphinxcontrib-htmlhelp==2.1.0
-    - sphinxcontrib-jsmath==1.0.1
-    - sphinxcontrib-qthelp==2.0.0
-    - sphinxcontrib-serializinghtml==2.0.0
-    - threadpoolctl==3.6.0
-    - toolz==1.1.0
-    - types-docutils==0.22.3.20251115
-    - tzdata==2025.2
-    - urllib3==2.6.1
-    - zarr==3.1.5
-ci:
-  autoupdate_commit_msg: 'ci: pre-commit autoupdate'
-  skip:
-  - mypy  # too big
-  - update-mypy-hook  # offline?

.vscode/extensions.json

@@ -0,0 +1,8 @@
+{
+    "recommendations": [
+        "astral-sh.ty",
+        "biomejs.biome",
+        "tamasfe.even-better-toml",
+        "charliermarsh.ruff",
+    ],
+}

.vscode/settings.json

@@ -17,4 +17,5 @@
     },
     "python.testing.pytestArgs": ["-vv", "--color=yes", "-m", "not benchmark"],
     "python.testing.pytestEnabled": true,
+    "python.languageServer": "None", // ty instead
 }

pyproject.toml

@@ -56,14 +56,9 @@ doc = [
   "sphinx>=9.0.1",
   "sphinx-autofixture>=0.4.1",
 ]
-# for update-mypy-hook
-mypy = [
-  "fast-array-utils[full]",
+typing = [
   "scipy-stubs",
-  # TODO: replace sphinx with this: { include-group = "doc" },
-  "sphinx",
   "types-docutils",
-  { include-group = "test" },
 ]
 
 [tool.hatch.version]
@@ -84,15 +79,21 @@ packages = [ "src/testing", "src/fast_array_utils" ]
 [tool.hatch.envs.default]
 installer = "uv"
 
+[tool.hatch.envs.typecheck]
+dependencies = [ "ty" ]
+features = [ "full" ]
+dependency-groups = [ "test", "doc", "typing" ]
+scripts.run = "ty check"
+
 [tool.hatch.envs.docs]
-dependency-groups = [ "doc" ]
+dependency-groups = [ "doc", "typing" ]
 scripts.build = "sphinx-build -M html docs docs/_build"
 scripts.clean = "git clean -fdX docs"
 scripts.open = "python -m webbrowser -t docs/_build/html/index.html"
 
 [tool.hatch.envs.hatch-test]
 default-args = [  ]
-dependency-groups = [ "test-min" ]
+dependency-groups = [ "test-min", "typing" ]
 # TODO: remove scipy once https://github.com/pypa/hatch/pull/2127 is released
 extra-dependencies = [ "ipykernel", "ipycytoscape", "scipy" ]
 env-vars.CODSPEED_PROFILE_FOLDER = "test-data/codspeed"
@@ -161,6 +162,9 @@ lint.isort.lines-after-imports = 2
 lint.pydocstyle.convention = "numpy"
 lint.future-annotations = true
 
+# Override the project-wide Python version for a developer scripts directory:
+per-file-target-version."**/*.pyi" = "py313"
+
 [tool.pytest]
 strict = true
 addopts = [
@@ -194,13 +198,9 @@ html.directory = "test-data/htmlcov"
 run.omit = [ "src/testing/*", "tests/*" ]
 report.exclude_also = [ "if TYPE_CHECKING:", "@numba[.]njit", "[.]{3}" ]
 
-[tool.mypy]
-strict = true
-# https://github.com/dask/dask/issues/8853
-implicit_reexport = true
-explicit_package_bases = true
-mypy_path = [ "$MYPY_CONFIG_FILE_DIR/typings", "$MYPY_CONFIG_FILE_DIR/src" ]
-
 [tool.pyright]
 stubPath = "./typings"
 reportPrivateUsage = false
+
+[tool.ty.environment]
+extra-paths = [ "typings" ]

src/fast_array_utils/_plugins/numba_sparse.py

@@ -247,7 +247,7 @@ def overload_sparse_copy(inst: CSType) -> None | Callable[[CSType], CSType]:
 
     # nopython code:
     def copy(inst: CSType) -> CSType:  # pragma: no cover
-        return _sparse_copy(inst, inst.data.copy(), inst.indices.copy(), inst.indptr.copy(), inst.shape)  # type: ignore[return-value]
+        return _sparse_copy(inst, inst.data.copy(), inst.indices.copy(), inst.indptr.copy(), inst.shape)
 
     return copy
 

src/fast_array_utils/conv/_to_dense.py

@@ -3,7 +3,7 @@
 
 import warnings
 from functools import partial, singledispatch
-from typing import TYPE_CHECKING, cast
+from typing import TYPE_CHECKING
 
 import numpy as np
 
@@ -47,7 +47,7 @@ def _to_dense_dask(x: types.DaskArray, /, *, order: Literal["K", "A", "C", "F"]
         msg = f"{order=!r} will probably be ignored: Dask can not be made to emit F-contiguous arrays reliably."
         warnings.warn(msg, RuntimeWarning, stacklevel=4)
     x = x.map_blocks(partial(to_dense, order=order, to_cpu_memory=to_cpu_memory))
-    return x.compute() if to_cpu_memory else x  # type: ignore[return-value]
+    return x.compute() if to_cpu_memory else x
 
 
 @to_dense_.register(types.CSDataset)
@@ -58,7 +58,7 @@ def _to_dense_ooc(x: types.CSDataset, /, *, order: Literal["K", "A", "C", "F"] =
         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)
@@ -77,4 +77,4 @@ def sparse_order(x: types.spmatrix | types.sparray | types.CupySpMatrix | types.
 
     if order in {"K", "A"}:
         order = "F" if x.format == "csc" else "C"
-    return cast("Literal['C', 'F']", order)
+    return order

src/fast_array_utils/stats/__init__.py

@@ -25,7 +25,6 @@
     from ._generic_ops import Ops
     from ._typing import NoDtypeOps, StatFunDtype, StatFunNoDtype
 
-
 __all__ = ["is_constant", "max", "mean", "mean_var", "min", "sum"]
 
 
@@ -87,9 +86,9 @@ def mean(x: CpuArray | GpuArray | DiskArray, /, *, axis: None = None, dtype: DTy
 @overload
 def mean(x: CpuArray | DiskArray, /, *, axis: Literal[0, 1], dtype: DTypeLike | None = None) -> NDArray[np.number[Any]]: ...
 @overload
-def mean(x: GpuArray, /, *, axis: Literal[0, 1], dtype: DTypeLike | None = None) -> types.CupyArray: ...
+def mean(x: GpuArray, /, *, axis: Literal[0, 1] | None = None, dtype: DTypeLike | None = None) -> types.CupyArray: ...
 @overload
-def mean(x: types.DaskArray, /, *, axis: Literal[0, 1], dtype: ToDType[Any] | None = None) -> types.DaskArray: ...
+def mean(x: types.DaskArray, /, *, axis: Literal[0, 1] | None = None, dtype: ToDType[Any] | None = None) -> types.DaskArray: ...
 
 
 def mean(
@@ -144,7 +143,17 @@ def mean_var(x: CpuArray, /, *, axis: Literal[0, 1], correction: int = 0) -> tup
 @overload
 def mean_var(x: GpuArray, /, *, axis: Literal[0, 1], correction: int = 0) -> tuple[types.CupyArray, types.CupyArray]: ...
 @overload
-def mean_var(x: types.DaskArray, /, *, axis: Literal[0, 1] | None = None, correction: int = 0) -> tuple[types.DaskArray, types.DaskArray]: ...
+def mean_var(
+    x: types.DaskArray[CpuArray | GpuArray], /, *, axis: None = None, correction: int = 0
+) -> tuple[types.DaskArray[np.float64], types.DaskArray[np.float64]]: ...
+@overload
+def mean_var(
+    x: types.DaskArray[CpuArray], /, *, axis: Literal[0, 1], correction: int = 0
+) -> tuple[types.DaskArray[NDArray[np.float64]], types.DaskArray[NDArray[np.float64]]]: ...
+@overload
+def mean_var(
+    x: types.DaskArray[GpuArray], /, *, axis: Literal[0, 1], correction: int = 0
+) -> tuple[types.DaskArray[types.CupyArray], types.DaskArray[types.CupyArray]]: ...
 
 
 def mean_var(
@@ -157,7 +166,8 @@ def mean_var(
     tuple[np.float64, np.float64]
     | tuple[NDArray[np.float64], NDArray[np.float64]]
     | tuple[types.CupyArray, types.CupyArray]
-    | tuple[types.DaskArray, types.DaskArray]
+    | tuple[types.DaskArray[np.float64], types.DaskArray[np.float64]]
+    | tuple[types.DaskArray[NDArray[np.float64]], types.DaskArray[NDArray[np.float64]]]
 ):
     """Mean and variance over both or one axis.
 
@@ -201,7 +211,7 @@ def mean_var(
     from ._mean_var import mean_var_
 
     validate_axis(x.ndim, axis)
-    return mean_var_(x, axis=axis, correction=correction)  # type: ignore[no-any-return]
+    return mean_var_(x, axis=axis, correction=correction)
 
 
 @overload

src/fast_array_utils/stats/_generic_ops.py

@@ -88,7 +88,7 @@ def _generic_op_cs(
     if TYPE_CHECKING:  # scipy-stubs thinks e.g. "int64" is invalid, which isn’t true
         assert isinstance(dtype, np.dtype | type | None)
     # convert to array so dimensions collapse as expected
-    x = (sp.csr_array if x.format == "csr" else sp.csc_array)(x, **_dtype_kw(dtype, op))  # type: ignore[arg-type]
+    x = (sp.csr_array if x.format == "csr" else sp.csc_array)(x, **_dtype_kw(dtype, op))
     rv = cast("NDArray[Any] | types.coo_array | np.number[Any]", getattr(x, op)(axis=axis))
     # old scipy versions’ sparray.{max,min}() return a 1×n/n×1 sparray here, so we squeeze
     return rv.toarray().squeeze() if isinstance(rv, types.coo_array) else rv

src/fast_array_utils/stats/_is_constant.py

@@ -92,7 +92,7 @@ def _is_constant_dask(a: types.DaskArray, /, *, axis: Literal[0, 1] | None = Non
         (a == a[0, 0].compute()).all()
         if isinstance(a._meta, np.ndarray)  # noqa: SLF001
         else da.map_overlap(
-            lambda a: np.array([[is_constant(a)]]),  # type: ignore[arg-type]
+            lambda a: np.array([[is_constant(a)]]),
             a,
             # use asymmetric overlaps to avoid unnecessary computation
             depth=dict.fromkeys(range(a.ndim), (0, 1)),

src/fast_array_utils/stats/_mean.py

@@ -24,6 +24,6 @@ def mean_(
     axis: Literal[0, 1] | None = None,
     dtype: DTypeLike | None = None,
 ) -> NDArray[np.number[Any]] | np.number[Any] | types.DaskArray:
-    total = sum(x, axis=axis, dtype=dtype)  # type: ignore[misc,arg-type]
+    total = sum(x, axis=axis, dtype=dtype)
     n = np.prod(x.shape) if axis is None else x.shape[axis]
-    return total / n  # type: ignore[no-any-return]
+    return total / n

src/fast_array_utils/stats/_mean_var.py

@@ -29,7 +29,7 @@ def mean_var_(
     tuple[NDArray[np.float64], NDArray[np.float64]]
     | tuple[types.CupyArray, types.CupyArray]
     | tuple[np.float64, np.float64]
-    | tuple[types.DaskArray, types.DaskArray]
+    | tuple[types.DaskArray[NDArray[np.float64]], types.DaskArray[NDArray[np.float64]]]
 ):
     from . import mean
 

src/fast_array_utils/stats/_power.py

@@ -21,23 +21,23 @@
 def power[Arr: Array](x: Arr, n: int, /, dtype: DTypeLike | None = None) -> Arr:
     """Take array or matrix to a power."""
     # This wrapper is necessary because TypeVars can’t be used in `singledispatch` functions
-    return _power(x, n, dtype=dtype)  # type: ignore[return-value]
+    return _power(x, n, dtype=dtype)
 
 
 @singledispatch
 def _power(x: Array, n: int, /, dtype: DTypeLike | None = None) -> Array:
     if TYPE_CHECKING:
         assert not isinstance(x, types.DaskArray | types.CSBase | types.CupyCSMatrix)
-    return x**n if dtype is None else np.power(x, n, dtype=dtype)  # type: ignore[operator]
+    return x**n if dtype is None else np.power(x, n, dtype=dtype)
 
 
 @_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)
 
 
 @_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]
+    return x.map_blocks(lambda c: power(c, n, dtype=dtype), dtype=dtype, meta=meta)

src/fast_array_utils/stats/_utils.py

@@ -52,9 +52,9 @@ def _dask_inner(x: types.DaskArray, op: Ops, /, *, axis: Literal[0, 1] | None, d
     def to_scalar(a: types.CupyArray | NDArray[Any]) -> np.number[Any]:
         if isinstance(a, types.CupyArray):
             a = a.get()
-        return a.reshape(())[()]  # type: ignore[return-value]
+        return a.reshape(())[()]
 
-    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(
@@ -75,7 +75,7 @@ def _dask_block(
     fns = {fn.__name__: fn for fn in (min, max, sum)}
 
     axis = _normalize_axis(axis, a.ndim)
-    rv = fns[op](a, axis=axis, keep_cupy_as_array=True, **_dtype_kw(dtype, op))  # type: ignore[call-overload]
+    rv = fns[op](a, axis=axis, keep_cupy_as_array=True, **_dtype_kw(dtype, op))
     shape = _get_shape(rv, axis=axis, keepdims=keepdims)
     return cast("NDArray[Any] | types.CupyArray", rv.reshape(shape))
 
@@ -90,7 +90,7 @@ def _normalize_axis(axis: ComplexAxis, ndim: int) -> Literal[0, 1] | None:
         case (0, 1) | (1, 0):
             axis = None
         case _:  # pragma: no cover
-            raise AxisError(axis, ndim)  # type: ignore[call-overload]
+            raise AxisError(axis, ndim)
     if axis == 0 and ndim == 1:
         return None  # dask’s aggregate doesn’t know we don’t accept `axis=0` for 1D arrays
     return axis

src/fast_array_utils/types.py

@@ -106,7 +106,7 @@
 
 
 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