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TYP: Backport typing fixes from main (2) (#28533)
* TYP: don't use literals in shape-types Partial backport of numpy/numtype#122 and numpy/numtype#152 * TYP: annotate the missing ``ufunc.resolve_dtypes`` method Ported from numpy/numtype#218 * TYP: stub ``numpy._core.overrides`` Ported from numpy/numtype#226 * TYP: stub ``numpy._utils`` Ported from numpy/numtype#225 * TYP: stub ``numpy._core._dtype[_ctypes]`` Ported from numpy/numtype#227 * TYP: stub the remaining ``numpy._core.*`` modules Ported from numpy/numtype#241 * TYP: stub the missing submodules of ``numpy.linalg`` Ported from numpy/numtype#248 * TYP: stub ``numpy._pyinstaller`` Ported from numpy/numtype#264 * TYP: stub ``numpy.fft.helper`` (deprecated) Ported from numpy/numtype#261 * TYP: annotate the missing deprecated ``row_stack`` function Ported from numpy/numtype#223 --------- Co-authored-by: jorenham <jhammudoglu@gmail.com>
1 parent 8c6007f commit c694399

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+1131
-128
lines changed

‎numpy/__init__.pyi

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@@ -574,6 +574,7 @@ from numpy.lib._polynomial_impl import (
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from numpy.lib._shape_base_impl import (
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column_stack,
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row_stack,
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dstack,
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array_split,
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split,
@@ -730,10 +731,9 @@ __all__ = [ # noqa: RUF022
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"histogram2d", "mask_indices", "tril_indices", "tril_indices_from", "triu_indices",
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"triu_indices_from",
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# lib._shape_base_impl.__all__
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# NOTE: `row_stack` is omitted because it is deprecated
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"column_stack", "dstack", "array_split", "split", "hsplit", "vsplit", "dsplit",
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"apply_over_axes", "expand_dims", "apply_along_axis", "kron", "tile",
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"take_along_axis", "put_along_axis",
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"take_along_axis", "put_along_axis", "row_stack",
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# lib._type_check_impl.__all__
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"iscomplexobj", "isrealobj", "imag", "iscomplex", "isreal", "nan_to_num", "real",
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"real_if_close", "typename", "mintypecode", "common_type",
@@ -4803,6 +4803,17 @@ class ufunc:
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# outputs, so we can't type it very precisely.
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def at(self, /, *args: Any, **kwargs: Any) -> None: ...
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#
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def resolve_dtypes(
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self,
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/,
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dtypes: tuple[dtype[Any] | type | None, ...],
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*,
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signature: tuple[dtype[Any] | None, ...] | None = None,
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casting: _CastingKind | None = None,
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reduction: builtins.bool = False,
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) -> tuple[dtype[Any], ...]: ...
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# Parameters: `__name__`, `ntypes` and `identity`
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absolute: _UFunc_Nin1_Nout1[L['absolute'], L[20], None]
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add: _UFunc_Nin2_Nout1[L['add'], L[22], L[0]]

‎numpy/_core/_add_newdocs.pyi

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from .overrides import get_array_function_like_doc as get_array_function_like_doc
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def refer_to_array_attribute(attr: str, method: bool = True) -> tuple[str, str]: ...

‎numpy/_core/_add_newdocs_scalars.pyi

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@@ -0,0 +1,16 @@
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from collections.abc import Iterable
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from typing import Final
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import numpy as np
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possible_aliases: Final[list[tuple[type[np.number], str, str]]] = ...
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_system: Final[str] = ...
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_machine: Final[str] = ...
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_doc_alias_string: Final[str] = ...
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_bool_docstring: Final[str] = ...
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int_name: str = ...
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float_name: str = ...
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def numeric_type_aliases(aliases: list[tuple[str, str]]) -> list[tuple[type[np.number], str, str]]: ...
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def add_newdoc_for_scalar_type(obj: str, fixed_aliases: Iterable[str], doc: str) -> None: ...
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def _get_platform_and_machine() -> tuple[str, str]: ...

‎numpy/_core/_dtype.pyi

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from typing import Any, Final, TypeAlias, TypedDict, overload, type_check_only
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from typing import Literal as L
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from typing_extensions import ReadOnly, TypeVar
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import numpy as np
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###
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_T = TypeVar("_T")
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_Name: TypeAlias = L["uint", "int", "complex", "float", "bool", "void", "object", "datetime", "timedelta", "bytes", "str"]
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@type_check_only
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class _KindToStemType(TypedDict):
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u: ReadOnly[L["uint"]]
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i: ReadOnly[L["int"]]
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c: ReadOnly[L["complex"]]
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f: ReadOnly[L["float"]]
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b: ReadOnly[L["bool"]]
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V: ReadOnly[L["void"]]
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O: ReadOnly[L["object"]]
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M: ReadOnly[L["datetime"]]
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m: ReadOnly[L["timedelta"]]
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S: ReadOnly[L["bytes"]]
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U: ReadOnly[L["str"]]
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###
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_kind_to_stem: Final[_KindToStemType] = ...
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#
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def _kind_name(dtype: np.dtype[Any]) -> _Name: ...
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def __str__(dtype: np.dtype[Any]) -> str: ...
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def __repr__(dtype: np.dtype[Any]) -> str: ...
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#
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def _isunsized(dtype: np.dtype[Any]) -> bool: ...
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def _is_packed(dtype: np.dtype[Any]) -> bool: ...
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def _name_includes_bit_suffix(dtype: np.dtype[Any]) -> bool: ...
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#
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def _construction_repr(dtype: np.dtype[Any], include_align: bool = False, short: bool = False) -> str: ...
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def _scalar_str(dtype: np.dtype[Any], short: bool) -> str: ...
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def _byte_order_str(dtype: np.dtype[Any]) -> str: ...
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def _datetime_metadata_str(dtype: np.dtype[Any]) -> str: ...
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def _struct_dict_str(dtype: np.dtype[Any], includealignedflag: bool) -> str: ...
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def _struct_list_str(dtype: np.dtype[Any]) -> str: ...
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def _struct_str(dtype: np.dtype[Any], include_align: bool) -> str: ...
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def _subarray_str(dtype: np.dtype[Any]) -> str: ...
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def _name_get(dtype: np.dtype[Any]) -> str: ...
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#
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@overload
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def _unpack_field(dtype: np.dtype[Any], offset: int, title: _T) -> tuple[np.dtype[Any], int, _T]: ...
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@overload
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def _unpack_field(dtype: np.dtype[Any], offset: int, title: None = None) -> tuple[np.dtype[Any], int, None]: ...
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def _aligned_offset(offset: int, alignment: int) -> int: ...

‎numpy/_core/_dtype_ctypes.pyi

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import _ctypes
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import ctypes as ct
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from typing import Any, overload
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import numpy as np
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#
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@overload
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def dtype_from_ctypes_type(t: type[_ctypes.Array[Any] | _ctypes.Structure]) -> np.dtype[np.void]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_bool]) -> np.dtype[np.bool]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_int8 | ct.c_byte]) -> np.dtype[np.int8]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_uint8 | ct.c_ubyte]) -> np.dtype[np.uint8]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_int16 | ct.c_short]) -> np.dtype[np.int16]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_uint16 | ct.c_ushort]) -> np.dtype[np.uint16]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_int32 | ct.c_int]) -> np.dtype[np.int32]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_uint32 | ct.c_uint]) -> np.dtype[np.uint32]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_ssize_t | ct.c_long]) -> np.dtype[np.int32 | np.int64]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_size_t | ct.c_ulong]) -> np.dtype[np.uint32 | np.uint64]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_int64 | ct.c_longlong]) -> np.dtype[np.int64]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_uint64 | ct.c_ulonglong]) -> np.dtype[np.uint64]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_float]) -> np.dtype[np.float32]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_double]) -> np.dtype[np.float64]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_longdouble]) -> np.dtype[np.longdouble]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.c_char]) -> np.dtype[np.bytes_]: ...
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@overload
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def dtype_from_ctypes_type(t: type[ct.py_object[Any]]) -> np.dtype[np.object_]: ...
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43+
# NOTE: the complex ctypes on python>=3.14 are not yet supported at runtim, see
44+
# https://github.com/numpy/numpy/issues/28360
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#
47+
def _from_ctypes_array(t: type[_ctypes.Array[Any]]) -> np.dtype[np.void]: ...
48+
def _from_ctypes_structure(t: type[_ctypes.Structure]) -> np.dtype[np.void]: ...
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def _from_ctypes_union(t: type[_ctypes.Union]) -> np.dtype[np.void]: ...
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51+
# keep in sync with `dtype_from_ctypes_type` (minus the first overload)
52+
@overload
53+
def _from_ctypes_scalar(t: type[ct.c_bool]) -> np.dtype[np.bool]: ...
54+
@overload
55+
def _from_ctypes_scalar(t: type[ct.c_int8 | ct.c_byte]) -> np.dtype[np.int8]: ...
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@overload
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def _from_ctypes_scalar(t: type[ct.c_uint8 | ct.c_ubyte]) -> np.dtype[np.uint8]: ...
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@overload
59+
def _from_ctypes_scalar(t: type[ct.c_int16 | ct.c_short]) -> np.dtype[np.int16]: ...
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@overload
61+
def _from_ctypes_scalar(t: type[ct.c_uint16 | ct.c_ushort]) -> np.dtype[np.uint16]: ...
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@overload
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def _from_ctypes_scalar(t: type[ct.c_int32 | ct.c_int]) -> np.dtype[np.int32]: ...
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@overload
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def _from_ctypes_scalar(t: type[ct.c_uint32 | ct.c_uint]) -> np.dtype[np.uint32]: ...
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@overload
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def _from_ctypes_scalar(t: type[ct.c_ssize_t | ct.c_long]) -> np.dtype[np.int32 | np.int64]: ...
68+
@overload
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def _from_ctypes_scalar(t: type[ct.c_size_t | ct.c_ulong]) -> np.dtype[np.uint32 | np.uint64]: ...
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@overload
71+
def _from_ctypes_scalar(t: type[ct.c_int64 | ct.c_longlong]) -> np.dtype[np.int64]: ...
72+
@overload
73+
def _from_ctypes_scalar(t: type[ct.c_uint64 | ct.c_ulonglong]) -> np.dtype[np.uint64]: ...
74+
@overload
75+
def _from_ctypes_scalar(t: type[ct.c_float]) -> np.dtype[np.float32]: ...
76+
@overload
77+
def _from_ctypes_scalar(t: type[ct.c_double]) -> np.dtype[np.float64]: ...
78+
@overload
79+
def _from_ctypes_scalar(t: type[ct.c_longdouble]) -> np.dtype[np.longdouble]: ...
80+
@overload
81+
def _from_ctypes_scalar(t: type[ct.c_char]) -> np.dtype[np.bytes_]: ...
82+
@overload
83+
def _from_ctypes_scalar(t: type[ct.py_object[Any]]) -> np.dtype[np.object_]: ...

‎numpy/_core/_exceptions.pyi

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from collections.abc import Iterable
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from typing import Any, Final, overload
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4+
from typing_extensions import TypeVar, Unpack
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6+
import numpy as np
7+
from numpy import _CastingKind
8+
from numpy._utils import set_module as set_module
9+
10+
###
11+
12+
_T = TypeVar("_T")
13+
_TupleT = TypeVar("_TupleT", bound=tuple[()] | tuple[Any, Any, Unpack[tuple[Any, ...]]])
14+
_ExceptionT = TypeVar("_ExceptionT", bound=Exception)
15+
16+
###
17+
18+
class UFuncTypeError(TypeError):
19+
ufunc: Final[np.ufunc]
20+
def __init__(self, /, ufunc: np.ufunc) -> None: ...
21+
22+
class _UFuncNoLoopError(UFuncTypeError):
23+
dtypes: tuple[np.dtype[Any], ...]
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def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype[Any]]) -> None: ...
25+
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class _UFuncBinaryResolutionError(_UFuncNoLoopError):
27+
dtypes: tuple[np.dtype[Any], np.dtype[Any]]
28+
def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype[Any]]) -> None: ...
29+
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class _UFuncCastingError(UFuncTypeError):
31+
casting: Final[_CastingKind]
32+
from_: Final[np.dtype[Any]]
33+
to: Final[np.dtype[Any]]
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def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype[Any], to: np.dtype[Any]) -> None: ...
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36+
class _UFuncInputCastingError(_UFuncCastingError):
37+
in_i: Final[int]
38+
def __init__(
39+
self,
40+
/,
41+
ufunc: np.ufunc,
42+
casting: _CastingKind,
43+
from_: np.dtype[Any],
44+
to: np.dtype[Any],
45+
i: int,
46+
) -> None: ...
47+
48+
class _UFuncOutputCastingError(_UFuncCastingError):
49+
out_i: Final[int]
50+
def __init__(
51+
self,
52+
/,
53+
ufunc: np.ufunc,
54+
casting: _CastingKind,
55+
from_: np.dtype[Any],
56+
to: np.dtype[Any],
57+
i: int,
58+
) -> None: ...
59+
60+
class _ArrayMemoryError(MemoryError):
61+
shape: tuple[int, ...]
62+
dtype: np.dtype[Any]
63+
def __init__(self, /, shape: tuple[int, ...], dtype: np.dtype[Any]) -> None: ...
64+
@property
65+
def _total_size(self) -> int: ...
66+
@staticmethod
67+
def _size_to_string(num_bytes: int) -> str: ...
68+
69+
@overload
70+
def _unpack_tuple(tup: tuple[_T]) -> _T: ...
71+
@overload
72+
def _unpack_tuple(tup: _TupleT) -> _TupleT: ...
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def _display_as_base(cls: type[_ExceptionT]) -> type[_ExceptionT]: ...

‎numpy/_core/_machar.pyi

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from collections.abc import Iterable
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from typing import Any, Final, overload
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from typing_extensions import TypeVar, Unpack
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import numpy as np
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from numpy import _CastingKind
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from numpy._utils import set_module as set_module
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10+
###
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12+
_T = TypeVar("_T")
13+
_TupleT = TypeVar("_TupleT", bound=tuple[()] | tuple[Any, Any, Unpack[tuple[Any, ...]]])
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_ExceptionT = TypeVar("_ExceptionT", bound=Exception)
15+
16+
###
17+
18+
class UFuncTypeError(TypeError):
19+
ufunc: Final[np.ufunc]
20+
def __init__(self, /, ufunc: np.ufunc) -> None: ...
21+
22+
class _UFuncNoLoopError(UFuncTypeError):
23+
dtypes: tuple[np.dtype[Any], ...]
24+
def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype[Any]]) -> None: ...
25+
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class _UFuncBinaryResolutionError(_UFuncNoLoopError):
27+
dtypes: tuple[np.dtype[Any], np.dtype[Any]]
28+
def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype[Any]]) -> None: ...
29+
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class _UFuncCastingError(UFuncTypeError):
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casting: Final[_CastingKind]
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from_: Final[np.dtype[Any]]
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to: Final[np.dtype[Any]]
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def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype[Any], to: np.dtype[Any]) -> None: ...
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class _UFuncInputCastingError(_UFuncCastingError):
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in_i: Final[int]
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def __init__(
39+
self,
40+
/,
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ufunc: np.ufunc,
42+
casting: _CastingKind,
43+
from_: np.dtype[Any],
44+
to: np.dtype[Any],
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i: int,
46+
) -> None: ...
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48+
class _UFuncOutputCastingError(_UFuncCastingError):
49+
out_i: Final[int]
50+
def __init__(
51+
self,
52+
/,
53+
ufunc: np.ufunc,
54+
casting: _CastingKind,
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from_: np.dtype[Any],
56+
to: np.dtype[Any],
57+
i: int,
58+
) -> None: ...
59+
60+
class _ArrayMemoryError(MemoryError):
61+
shape: tuple[int, ...]
62+
dtype: np.dtype[Any]
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def __init__(self, /, shape: tuple[int, ...], dtype: np.dtype[Any]) -> None: ...
64+
@property
65+
def _total_size(self) -> int: ...
66+
@staticmethod
67+
def _size_to_string(num_bytes: int) -> str: ...
68+
69+
@overload
70+
def _unpack_tuple(tup: tuple[_T]) -> _T: ...
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@overload
72+
def _unpack_tuple(tup: _TupleT) -> _TupleT: ...
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def _display_as_base(cls: type[_ExceptionT]) -> type[_ExceptionT]: ...

‎numpy/_core/_methods.pyi

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from collections.abc import Callable
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from typing import Any, TypeAlias
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from typing_extensions import Concatenate
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import numpy as np
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8+
from . import _exceptions as _exceptions
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10+
###
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_Reduce2: TypeAlias = Callable[Concatenate[object, ...], Any]
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###
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bool_dt: np.dtype[np.bool] = ...
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umr_maximum: _Reduce2 = ...
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umr_minimum: _Reduce2 = ...
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umr_sum: _Reduce2 = ...
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umr_prod: _Reduce2 = ...
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umr_bitwise_count = np.bitwise_count
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umr_any: _Reduce2 = ...
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umr_all: _Reduce2 = ...
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_complex_to_float: dict[np.dtype[np.complexfloating], np.dtype[np.floating]] = ...

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