Add support for numpy 1.25+

docs/conf.py

@@ -8,6 +8,8 @@
 from pathlib import Path
 from typing import TYPE_CHECKING
 
+from docutils.nodes import Text
+
 
 if TYPE_CHECKING:
     from docutils.nodes import TextElement, reference
@@ -82,6 +84,7 @@
     source_directory="docs/",
 )
 
+_np_aliases = {"bool_": "bool"}
 _np_nocls = {"float64": "attr"}
 _optional_types = {
     "CupyArray": "cupy.ndarray",
@@ -94,30 +97,31 @@
 }
 
 
-def find_type_alias(name: str) -> tuple[str, str] | tuple[None, None]:
+def find_type_alias(name: str) -> tuple[str, str, str | None] | tuple[None, None, None]:
     """Find a type alias."""
     import numpy.typing as npt
 
     from fast_array_utils import types, typing
 
     if name in typing.__all__:
-        return "data", f"fast_array_utils.typing.{name}"
+        return "data", f"fast_array_utils.typing.{name}", None
     if name.startswith("types.") and name[6:] in {*types.__all__, *_optional_types}:
         if path := _optional_types.get(name[6:]):
-            return "class", path
-        return "data", f"fast_array_utils.{name}"
+            return "class", path, None
+        return "data", f"fast_array_utils.{name}", None
     if name.startswith("np."):
-        return _np_nocls.get(name[3:], "class"), f"numpy.{name[3:]}"
+        name = _np_aliases.get(name[3:], name[3:])
+        return _np_nocls.get(name, "class"), f"numpy.{name}", f"np.{name}"
     if name in npt.__all__:
-        return "data", f"numpy.typing.{name}"
-    return None, None
+        return "data", f"numpy.typing.{name}", None
+    return None, None, None
 
 
 def resolve_type_aliases(app: Sphinx, env: BuildEnvironment, node: pending_xref, contnode: TextElement) -> reference | None:
     """Resolve :class: references to our type aliases as :attr: instead."""
     if (node["refdomain"], node["reftype"]) != ("py", "class"):
         return None
-    typ, target = find_type_alias(node["reftarget"])
+    typ, target, name = find_type_alias(node["reftarget"])
     if typ is None or target is None:
         return None
     if target.startswith("fast_array_utils."):
@@ -131,6 +135,8 @@ def resolve_type_aliases(app: Sphinx, env: BuildEnvironment, node: pending_xref,
     if ref is None:
         msg = f"Could not resolve {typ} {target} (from {node['reftarget']})"
         raise AssertionError(msg)
+    if name:
+        ref.children[:] = [Text(name)]
     return ref
 
 

pyproject.toml

@@ -1,6 +1,12 @@
 [build-system]
 build-backend = "hatchling.build"
-requires = [ "hatch-docstring-description>=1.1.1", "hatch-fancy-pypi-readme", "hatch-vcs", "hatchling" ]
+requires = [
+  "hatch-docstring-description>=1.1.1",
+  "hatch-fancy-pypi-readme",
+  "hatch-min-requirements",
+  "hatch-vcs",
+  "hatchling",
+]
 
 [project]
 name = "fast-array-utils"
@@ -18,7 +24,7 @@ classifiers = [
   "Programming Language :: Python :: 3.13",
 ]
 dynamic = [ "description", "readme", "version" ]
-dependencies = [ "numpy" ]
+dependencies = [ "numpy>=1.25.2" ]
 optional-dependencies.accel = [ "numba" ]
 optional-dependencies.doc = [
   "furo",
@@ -29,7 +35,7 @@ optional-dependencies.doc = [
   "sphinx-autofixture",
 ]
 optional-dependencies.full = [ "dask", "fast-array-utils[accel,sparse]", "h5py", "zarr" ]
-optional-dependencies.sparse = [ "scipy>=1.8" ]
+optional-dependencies.sparse = [ "scipy>=1.11" ]
 optional-dependencies.test = [
   "anndata",
   "fast-array-utils[accel,test-min]",
@@ -61,6 +67,7 @@ path = "README.rst"
 start-after = ".. begin"
 
 [tool.hatch.metadata.hooks.docstring-description]
+[tool.hatch.metadata.hooks.min_requirements]
 
 [tool.hatch.build.targets.wheel]
 packages = [ "src/testing", "src/fast_array_utils" ]
@@ -87,11 +94,19 @@ overrides.matrix.extras.dependencies = [
   { if = [ "full" ], value = "scipy-stubs" },
   { if = [ "full" ], value = "scikit-learn" },
 ]
+overrides.matrix.resolution.features = [
+  { if = [ "lowest" ], value = "min-reqs" }, # feature added by hatch-min-requirements
+]
 
 [[tool.hatch.envs.hatch-test.matrix]]
 python = [ "3.13", "3.11" ]
 extras = [ "full", "min" ]
 
+[[tool.hatch.envs.hatch-test.matrix]]
+python = [ "3.11" ]
+extras = [ "full" ]
+resolution = [ "lowest" ]
+
 [tool.ruff]
 line-length = 160
 namespace-packages = [ "src/testing" ]
@@ -124,6 +139,7 @@ lint.per-file-ignores."typings/**/*.pyi" = [ "A002", "F403", "F405", "N801" ] #
 lint.allowed-confusables = [ "×", "’" ]
 lint.flake8-bugbear.extend-immutable-calls = [ "testing.fast_array_utils.Flags" ]
 lint.flake8-copyright.notice-rgx = "SPDX-License-Identifier: MPL-2\\.0"
+lint.flake8-tidy-imports.banned-api."numpy.bool".msg = "Use `np.bool_` instead for numpy>=1.24<2 compatibility"
 lint.flake8-type-checking.exempt-modules = [  ]
 lint.flake8-type-checking.strict = true
 lint.isort.known-first-party = [ "fast_array_utils" ]
@@ -133,6 +149,7 @@ lint.pydocstyle.convention = "numpy"
 
 [tool.pytest.ini_options]
 addopts = [
+  "-ptesting.fast_array_utils._private",
   "--import-mode=importlib",
   "--strict-markers",
   "--doctest-modules",

src/fast_array_utils/stats/__init__.py

@@ -29,7 +29,7 @@
 @overload
 def is_constant(x: NDArray[Any] | types.CSBase | types.CupyArray, /, *, axis: None = None) -> bool: ...
 @overload
-def is_constant(x: NDArray[Any] | types.CSBase, /, *, axis: Literal[0, 1]) -> NDArray[np.bool]: ...
+def is_constant(x: NDArray[Any] | types.CSBase, /, *, axis: Literal[0, 1]) -> NDArray[np.bool_]: ...
 @overload
 def is_constant(x: types.CupyArray, /, *, axis: Literal[0, 1]) -> types.CupyArray: ...
 @overload
@@ -41,7 +41,7 @@ def is_constant(
     /,
     *,
     axis: Literal[0, 1, None] = None,
-) -> bool | NDArray[np.bool] | types.CupyArray | types.DaskArray:
+) -> bool | NDArray[np.bool_] | types.CupyArray | types.DaskArray:
     """Check whether values in array are constant.
 
     Parameters
@@ -118,7 +118,7 @@ def mean(
     ...     [0, 0, 0],
     ... ])
     >>> mean(x)
-    np.float64(0.5)
+    0.5
     >>> mean(x, axis=0)
     array([0. , 0.5, 1. ])
     >>> mean(x, axis=1)
@@ -184,7 +184,7 @@ def mean_var(
     ...     [0, 0, 0],
     ... ])
     >>> mean_var(x)  # doctest: +FLOAT_CMP
-    (np.float64(0.5), np.float64(0.5833333333333334))
+    (0.5, 0.5833333333333334)
     >>> mean_var(x, axis=0)
     (array([0. , 0.5, 1. ]), array([0.  , 0.25, 1.  ]))
     >>> mean_var(x, axis=1)
@@ -251,7 +251,7 @@ def sum(
     ...     [0, 0, 0],
     ... ])
     >>> sum(x)
-    np.int64(3)
+    3
     >>> sum(x, axis=0)
     array([0, 1, 2])
     >>> sum(x, axis=1)

src/fast_array_utils/stats/_is_constant.py

@@ -25,12 +25,12 @@ def is_constant_(
     /,
     *,
     axis: Literal[0, 1, None] = None,
-) -> bool | NDArray[np.bool] | types.CupyArray | types.DaskArray:  # pragma: no cover
+) -> bool | NDArray[np.bool_] | types.CupyArray | types.DaskArray:  # pragma: no cover
     raise NotImplementedError
 
 
 @is_constant_.register(np.ndarray | types.CupyArray)  # type: ignore[call-overload,misc]
-def _is_constant_ndarray(a: NDArray[Any] | types.CupyArray, /, *, axis: Literal[0, 1, None] = None) -> bool | NDArray[np.bool] | types.CupyArray:
+def _is_constant_ndarray(a: NDArray[Any] | types.CupyArray, /, *, axis: Literal[0, 1, None] = None) -> bool | NDArray[np.bool_] | types.CupyArray:
     # Should eventually support nd, not now.
     match axis:
         case None:
@@ -41,13 +41,13 @@ def _is_constant_ndarray(a: NDArray[Any] | types.CupyArray, /, *, axis: Literal[
             return _is_constant_rows(a)
 
 
-def _is_constant_rows(a: NDArray[Any] | types.CupyArray) -> NDArray[np.bool] | types.CupyArray:
+def _is_constant_rows(a: NDArray[Any] | types.CupyArray) -> NDArray[np.bool_] | types.CupyArray:
     b = np.broadcast_to(a[:, 0][:, np.newaxis], a.shape)
-    return cast("NDArray[np.bool]", (a == b).all(axis=1))
+    return cast("NDArray[np.bool_]", (a == b).all(axis=1))
 
 
 @is_constant_.register(types.CSBase)  # type: ignore[call-overload,misc]
-def _is_constant_cs(a: types.CSBase, /, *, axis: Literal[0, 1, None] = None) -> bool | NDArray[np.bool]:
+def _is_constant_cs(a: types.CSBase, /, *, axis: Literal[0, 1, None] = None) -> bool | NDArray[np.bool_]:
     from . import is_constant
 
     if len(a.shape) == 1:  # pragma: no cover
@@ -68,9 +68,9 @@ def _is_constant_cs(a: types.CSBase, /, *, axis: Literal[0, 1, None] = None) ->
 
 
 @numba.njit(cache=True)
-def _is_constant_cs_major(a: types.CSBase, shape: tuple[int, int]) -> NDArray[np.bool]:
+def _is_constant_cs_major(a: types.CSBase, shape: tuple[int, int]) -> NDArray[np.bool_]:
     n = len(a.indptr) - 1
-    result = np.ones(n, dtype=np.bool)
+    result = np.ones(n, dtype=np.bool_)
     for i in numba.prange(n):
         start = a.indptr[i]
         stop = a.indptr[i + 1]
@@ -89,7 +89,7 @@ def _is_constant_dask(a: types.DaskArray, /, *, axis: Literal[0, 1, None] = None
     from . import is_constant
 
     if axis is not None:
-        return da.map_blocks(partial(is_constant, axis=axis), a, drop_axis=axis, meta=np.array([], dtype=np.bool))
+        return da.map_blocks(partial(is_constant, axis=axis), a, drop_axis=axis, meta=np.array([], dtype=np.bool_))
 
     rv = (
         (a == a[0, 0].compute()).all()

src/testing/fast_array_utils/_private.py

@@ -0,0 +1,11 @@
+# SPDX-License-Identifier: MPL-2.0
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def _set_numpy_print() -> None:  # TODO(flying-sheep): #97 remove once we depend on numpy >=2
+    if int(np.__version__.split(".", 1)[0]) > 1:
+        np.set_printoptions(legacy="1.25")

tests/test_numpy_scipy_sparse.py

@@ -66,10 +66,10 @@ def test_copy(
     assert mat.indptr.ctypes.data != copied.indptr.ctypes.data
     # check that the array contents and dtypes are the same
     assert mat.shape == copied.shape
-    np.testing.assert_equal(copied.toarray(), mat.toarray(), strict=True)
-    np.testing.assert_equal(copied.data, mat.data, strict=True)
-    np.testing.assert_equal(copied.indices, mat.indices, strict=not downcasts_idx(mat))
-    np.testing.assert_equal(copied.indptr, mat.indptr, strict=not downcasts_idx(mat))
+    np.testing.assert_array_equal(copied.toarray(), mat.toarray(), strict=True)
+    np.testing.assert_array_equal(copied.data, mat.data, strict=True)
+    np.testing.assert_array_equal(copied.indices, mat.indices, strict=not downcasts_idx(mat))
+    np.testing.assert_array_equal(copied.indptr, mat.indptr, strict=not downcasts_idx(mat))
 
 
 def downcasts_idx(mat: types.CSBase) -> bool:

tests/test_stats.py

@@ -24,7 +24,7 @@
 
     Array: TypeAlias = CpuArray | GpuArray | DiskArray | types.CSDataset | types.DaskArray
 
-    DTypeIn = np.float32 | np.float64 | np.int32 | np.bool
+    DTypeIn = np.float32 | np.float64 | np.int32 | np.bool_
     DTypeOut = np.float32 | np.float64 | np.int64
 
     NdAndAx: TypeAlias = tuple[Literal[1], Literal[None]] | tuple[Literal[2], Literal[0, 1, None]]
@@ -81,7 +81,7 @@ def axis(ndim_and_axis: NdAndAx) -> Literal[0, 1, None]:
     return ndim_and_axis[1]
 
 
-@pytest.fixture(params=[np.float32, np.float64, np.int32, np.bool])
+@pytest.fixture(params=[np.float32, np.float64, np.int32, np.bool_])
 def dtype_in(request: pytest.FixtureRequest, array_type: ArrayType) -> type[DTypeIn]:
     dtype = cast("type[DTypeIn]", request.param)
     inner_cls = array_type.inner.cls if array_type.inner else array_type.cls
@@ -152,7 +152,7 @@ def test_sum(
 
     if dtype_arg is not None:
         assert sum_.dtype == dtype_arg, (sum_.dtype, dtype_arg)
-    elif dtype_in in {np.bool, np.int32}:
+    elif dtype_in in {np.bool_, np.int32}:
         assert sum_.dtype == np.int64
     else:
         assert sum_.dtype == dtype_in
@@ -210,7 +210,7 @@ def test_mean_var(
         mean, var = mean.get(), var.get()
 
     mean_expected = np.mean(np_arr, axis=axis)  # type: ignore[arg-type]
-    var_expected = np.var(np_arr, axis=axis, correction=1)  # type: ignore[arg-type]
+    var_expected = np.var(np_arr, axis=axis, ddof=1)  # type: ignore[arg-type]
     np.testing.assert_array_equal(mean, mean_expected)
     np.testing.assert_array_almost_equal(var, var_expected)  # type: ignore[arg-type]
 
@@ -276,7 +276,7 @@ def test_is_constant(
     x = array_type(x_data, dtype=np.float64)
     result = stats.is_constant(x, axis=axis)
     if isinstance(result, types.DaskArray):
-        result = cast("NDArray[np.bool] | bool", result.compute())
+        result = cast("NDArray[np.bool_] | bool", result.compute())
     if isinstance(result, types.CupyArray | types.CupyCSMatrix):
         result = result.get()
     if isinstance(expected, list):

typings/cupy/_core/core.pyi

@@ -32,7 +32,7 @@ class ndarray:
     @property
     def T(self) -> Self: ...  # noqa: N802
     @overload
-    def all(self, axis: None = None) -> np.bool: ...
+    def all(self, axis: None = None) -> np.bool_: ...
     @overload
     def all(self, axis: int) -> ndarray: ...
     def reshape(self, shape: tuple[int, ...] | int) -> ndarray: ...