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📁 ..	-	2026-03-21 15:45
📄 __init__.py """Private counterpart of ``numpy.typing``.""" from __future__ import annotations from .. import ufunc from .._utils import set_module from typing import TYPE_CHECKING, final @final # Disallow the creation of arbitrary `NBitBase` subclasses @set_module("numpy.typing") class NBitBase: """ A type representing `numpy.number` precision during static type checking. Used exclusively for the purpose static type checking, `NBitBase` represents the base of a hierarchical set of subclasses. Each subsequent subclass is herein used for representing a lower level of precision, e.g. ``64Bit > 32Bit > 16Bit``. .. versionadded:: 1.20 Examples -------- Below is a typical usage example: `NBitBase` is herein used for annotating a function that takes a float and integer of arbitrary precision as arguments and returns a new float of whichever precision is largest (e.g. ``np.float16 + np.int64 -> np.float64``). .. code-block:: python >>> from __future__ import annotations >>> from typing import TypeVar, TYPE_CHECKING >>> import numpy as np >>> import numpy.typing as npt >>> T1 = TypeVar("T1", bound=npt.NBitBase) >>> T2 = TypeVar("T2", bound=npt.NBitBase) >>> def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[T1 \| T2]: ... return a + b >>> a = np.float16() >>> b = np.int64() >>> out = add(a, b) >>> if TYPE_CHECKING: ... reveal_locals() ... # note: Revealed local types are: ... # note: a: numpy.floating[numpy.typing._16Bit] ... # note: b: numpy.signedinteger[numpy.typing._64Bit] ... # note: out: numpy.floating[numpy.typing._64Bit*] """ def __init_subclass__(cls) -> None: allowed_names = { "NBitBase", "_256Bit", "_128Bit", "_96Bit", "_80Bit", "_64Bit", "_32Bit", "_16Bit", "_8Bit", } if cls.__name__ not in allowed_names: raise TypeError('cannot inherit from final class "NBitBase"') super().__init_subclass__() # Silence errors about subclassing a `@final`-decorated class class _256Bit(NBitBase): # type: ignore[misc] pass class _128Bit(_256Bit): # type: ignore[misc] pass class _96Bit(_128Bit): # type: ignore[misc] pass class _80Bit(_96Bit): # type: ignore[misc] pass class _64Bit(_80Bit): # type: ignore[misc] pass class _32Bit(_64Bit): # type: ignore[misc] pass class _16Bit(_32Bit): # type: ignore[misc] pass class _8Bit(_16Bit): # type: ignore[misc] pass from ._nested_sequence import ( _NestedSequence as _NestedSequence, ) from ._nbit import ( _NBitByte as _NBitByte, _NBitShort as _NBitShort, _NBitIntC as _NBitIntC, _NBitIntP as _NBitIntP, _NBitInt as _NBitInt, _NBitLongLong as _NBitLongLong, _NBitHalf as _NBitHalf, _NBitSingle as _NBitSingle, _NBitDouble as _NBitDouble, _NBitLongDouble as _NBitLongDouble, ) from ._char_codes import ( _BoolCodes as _BoolCodes, _UInt8Codes as _UInt8Codes, _UInt16Codes as _UInt16Codes, _UInt32Codes as _UInt32Codes, _UInt64Codes as _UInt64Codes, _Int8Codes as _Int8Codes, _Int16Codes as _Int16Codes, _Int32Codes as _Int32Codes, _Int64Codes as _Int64Codes, _Float16Codes as _Float16Codes, _Float32Codes as _Float32Codes, _Float64Codes as _Float64Codes, _Complex64Codes as _Complex64Codes, _Complex128Codes as _Complex128Codes, _ByteCodes as _ByteCodes, _ShortCodes as _ShortCodes, _IntCCodes as _IntCCodes, _IntPCodes as _IntPCodes, _IntCodes as _IntCodes, _LongLongCodes as _LongLongCodes, _UByteCodes as _UByteCodes, _UShortCodes as _UShortCodes, _UIntCCodes as _UIntCCodes, _UIntPCodes as _UIntPCodes, _UIntCodes as _UIntCodes, _ULongLongCodes as _ULongLongCodes, _HalfCodes as _HalfCodes, _SingleCodes as _SingleCodes, _DoubleCodes as _DoubleCodes, _LongDoubleCodes as _LongDoubleCodes, _CSingleCodes as _CSingleCodes, _CDoubleCodes as _CDoubleCodes, _CLongDoubleCodes as _CLongDoubleCodes, _DT64Codes as _DT64Codes, _TD64Codes as _TD64Codes, _StrCodes as _StrCodes, _BytesCodes as _BytesCodes, _VoidCodes as _VoidCodes, _ObjectCodes as _ObjectCodes, ) from ._scalars import ( _CharLike_co as _CharLike_co, _BoolLike_co as _BoolLike_co, _UIntLike_co as _UIntLike_co, _IntLike_co as _IntLike_co, _FloatLike_co as _FloatLike_co, _ComplexLike_co as _ComplexLike_co, _TD64Like_co as _TD64Like_co, _NumberLike_co as _NumberLike_co, _ScalarLike_co as _ScalarLike_co, _VoidLike_co as _VoidLike_co, ) from ._shape import ( _Shape as _Shape, _ShapeLike as _ShapeLike, ) from ._dtype_like import ( DTypeLike as DTypeLike, _DTypeLike as _DTypeLike, _SupportsDType as _SupportsDType, _VoidDTypeLike as _VoidDTypeLike, _DTypeLikeBool as _DTypeLikeBool, _DTypeLikeUInt as _DTypeLikeUInt, _DTypeLikeInt as _DTypeLikeInt, _DTypeLikeFloat as _DTypeLikeFloat, _DTypeLikeComplex as _DTypeLikeComplex, _DTypeLikeTD64 as _DTypeLikeTD64, _DTypeLikeDT64 as _DTypeLikeDT64, _DTypeLikeObject as _DTypeLikeObject, _DTypeLikeVoid as _DTypeLikeVoid, _DTypeLikeStr as _DTypeLikeStr, _DTypeLikeBytes as _DTypeLikeBytes, _DTypeLikeComplex_co as _DTypeLikeComplex_co, ) from ._array_like import ( NDArray as NDArray, ArrayLike as ArrayLike, _ArrayLike as _ArrayLike, _FiniteNestedSequence as _FiniteNestedSequence, _SupportsArray as _SupportsArray, _SupportsArrayFunc as _SupportsArrayFunc, _ArrayLikeInt as _ArrayLikeInt, _ArrayLikeBool_co as _ArrayLikeBool_co, _ArrayLikeUInt_co as _ArrayLikeUInt_co, _ArrayLikeInt_co as _ArrayLikeInt_co, _ArrayLikeFloat_co as _ArrayLikeFloat_co, _ArrayLikeComplex_co as _ArrayLikeComplex_co, _ArrayLikeNumber_co as _ArrayLikeNumber_co, _ArrayLikeTD64_co as _ArrayLikeTD64_co, _ArrayLikeDT64_co as _ArrayLikeDT64_co, _ArrayLikeObject_co as _ArrayLikeObject_co, _ArrayLikeVoid_co as _ArrayLikeVoid_co, _ArrayLikeStr_co as _ArrayLikeStr_co, _ArrayLikeBytes_co as _ArrayLikeBytes_co, _ArrayLikeUnknown as _ArrayLikeUnknown, _UnknownType as _UnknownType, ) if TYPE_CHECKING: from ._ufunc import ( _UFunc_Nin1_Nout1 as _UFunc_Nin1_Nout1, _UFunc_Nin2_Nout1 as _UFunc_Nin2_Nout1, _UFunc_Nin1_Nout2 as _UFunc_Nin1_Nout2, _UFunc_Nin2_Nout2 as _UFunc_Nin2_Nout2, _GUFunc_Nin2_Nout1 as _GUFunc_Nin2_Nout1, ) else: # Declare the (type-check-only) ufunc subclasses as ufunc aliases during # runtime; this helps autocompletion tools such as Jedi (numpy/numpy#19834) _UFunc_Nin1_Nout1 = ufunc _UFunc_Nin2_Nout1 = ufunc _UFunc_Nin1_Nout2 = ufunc _UFunc_Nin2_Nout2 = ufunc _GUFunc_Nin2_Nout1 = ufunc	6.84 KB	2026-01-20 12:58
📁 __pycache__	-	2026-03-21 15:45
📄 _add_docstring.py """A module for creating docstrings for sphinx ``data`` domains.""" import re import textwrap from ._array_like import NDArray _docstrings_list = [] def add_newdoc(name: str, value: str, doc: str) -> None: """Append ``_docstrings_list`` with a docstring for `name`. Parameters ---------- name : str The name of the object. value : str A string-representation of the object. doc : str The docstring of the object. """ _docstrings_list.append((name, value, doc)) def _parse_docstrings() -> str: """Convert all docstrings in ``_docstrings_list`` into a single sphinx-legible text block. """ type_list_ret = [] for name, value, doc in _docstrings_list: s = textwrap.dedent(doc).replace("\n", "\n ") # Replace sections by rubrics lines = s.split("\n") new_lines = [] indent = "" for line in lines: m = re.match(r'^(\s+)[-=]+\s$', line) if m and new_lines: prev = textwrap.dedent(new_lines.pop()) if prev == "Examples": indent = "" new_lines.append(f'{m.group(1)}.. rubric:: {prev}') else: indent = 4 " " new_lines.append(f'{m.group(1)}.. admonition:: {prev}') new_lines.append("") else: new_lines.append(f"{indent}{line}") s = "\n".join(new_lines) s_block = f""".. data:: {name}\n :value: {value}\n {s}""" type_list_ret.append(s_block) return "\n".join(type_list_ret) add_newdoc('ArrayLike', 'typing.Union[...]', """ A `~typing.Union` representing objects that can be coerced into an `~numpy.ndarray`. Among others this includes the likes of: * Scalars. * (Nested) sequences. * Objects implementing the `~class.__array__` protocol. .. versionadded:: 1.20 See Also -------- :term:`array_like`: Any scalar or sequence that can be interpreted as an ndarray. Examples -------- .. code-block:: python >>> import numpy as np >>> import numpy.typing as npt >>> def as_array(a: npt.ArrayLike) -> np.ndarray: ... return np.array(a) """) add_newdoc('DTypeLike', 'typing.Union[...]', """ A `~typing.Union` representing objects that can be coerced into a `~numpy.dtype`. Among others this includes the likes of: * :class:`type` objects. * Character codes or the names of :class:`type` objects. * Objects with the ``.dtype`` attribute. .. versionadded:: 1.20 See Also -------- :ref:`Specifying and constructing data types <arrays.dtypes.constructing>` A comprehensive overview of all objects that can be coerced into data types. Examples -------- .. code-block:: python >>> import numpy as np >>> import numpy.typing as npt >>> def as_dtype(d: npt.DTypeLike) -> np.dtype: ... return np.dtype(d) """) add_newdoc('NDArray', repr(NDArray), """ A :term:`generic <generic type>` version of `np.ndarray[Any, np.dtype[+ScalarType]] <numpy.ndarray>`. Can be used during runtime for typing arrays with a given dtype and unspecified shape. .. versionadded:: 1.21 Examples -------- .. code-block:: python >>> import numpy as np >>> import numpy.typing as npt >>> print(npt.NDArray) numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]] >>> print(npt.NDArray[np.float64]) numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] >>> NDArrayInt = npt.NDArray[np.int_] >>> a: NDArrayInt = np.arange(10) >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]: ... return np.array(a) """) _docstrings = _parse_docstrings()	3.83 KB	2026-01-20 12:58
📄 _array_like.py from __future__ import annotations from collections.abc import Collection, Callable, Sequence from typing import Any, Protocol, Union, TypeVar, runtime_checkable from numpy import ( ndarray, dtype, generic, bool_, unsignedinteger, integer, floating, complexfloating, number, timedelta64, datetime64, object_, void, str_, bytes_, ) from ._nested_sequence import _NestedSequence _T = TypeVar("_T") _ScalarType = TypeVar("_ScalarType", bound=generic) _ScalarType_co = TypeVar("_ScalarType_co", bound=generic, covariant=True) _DType = TypeVar("_DType", bound=dtype[Any]) _DType_co = TypeVar("_DType_co", covariant=True, bound=dtype[Any]) NDArray = ndarray[Any, dtype[_ScalarType_co]] # The `_SupportsArray` protocol only cares about the default dtype # (i.e. `dtype=None` or no `dtype` parameter at all) of the to-be returned # array. # Concrete implementations of the protocol are responsible for adding # any and all remaining overloads @runtime_checkable class _SupportsArray(Protocol[_DType_co]): def __array__(self) -> ndarray[Any, _DType_co]: ... @runtime_checkable class _SupportsArrayFunc(Protocol): """A protocol class representing `~class.__array_function__`.""" def __array_function__( self, func: Callable[..., Any], types: Collection[type[Any]], args: tuple[Any, ...], kwargs: dict[str, Any], ) -> object: ... # TODO: Wait until mypy supports recursive objects in combination with typevars _FiniteNestedSequence = Union[ _T, Sequence[_T], Sequence[Sequence[_T]], Sequence[Sequence[Sequence[_T]]], Sequence[Sequence[Sequence[Sequence[_T]]]], ] # A subset of `npt.ArrayLike` that can be parametrized w.r.t. `np.generic` _ArrayLike = Union[ _SupportsArray[dtype[_ScalarType]], _NestedSequence[_SupportsArray[dtype[_ScalarType]]], ] # A union representing array-like objects; consists of two typevars: # One representing types that can be parametrized w.r.t. `np.dtype` # and another one for the rest _DualArrayLike = Union[ _SupportsArray[_DType], _NestedSequence[_SupportsArray[_DType]], _T, _NestedSequence[_T], ] # TODO: support buffer protocols once # # https://bugs.python.org/issue27501 # # is resolved. See also the mypy issue: # # https://github.com/python/typing/issues/593 ArrayLike = _DualArrayLike[ dtype[Any], Union[bool, int, float, complex, str, bytes], ] # `ArrayLike<X>_co`: array-like objects that can be coerced into `X` # given the casting rules `same_kind` _ArrayLikeBool_co = _DualArrayLike[ dtype[bool_], bool, ] _ArrayLikeUInt_co = _DualArrayLike[ dtype[Union[bool_, unsignedinteger[Any]]], bool, ] _ArrayLikeInt_co = _DualArrayLike[ dtype[Union[bool_, integer[Any]]], Union[bool, int], ] _ArrayLikeFloat_co = _DualArrayLike[ dtype[Union[bool_, integer[Any], floating[Any]]], Union[bool, int, float], ] _ArrayLikeComplex_co = _DualArrayLike[ dtype[Union[ bool_, integer[Any], floating[Any], complexfloating[Any, Any], ]], Union[bool, int, float, complex], ] _ArrayLikeNumber_co = _DualArrayLike[ dtype[Union[bool_, number[Any]]], Union[bool, int, float, complex], ] _ArrayLikeTD64_co = _DualArrayLike[ dtype[Union[bool_, integer[Any], timedelta64]], Union[bool, int], ] _ArrayLikeDT64_co = Union[ _SupportsArray[dtype[datetime64]], _NestedSequence[_SupportsArray[dtype[datetime64]]], ] _ArrayLikeObject_co = Union[ _SupportsArray[dtype[object_]], _NestedSequence[_SupportsArray[dtype[object_]]], ] _ArrayLikeVoid_co = Union[ _SupportsArray[dtype[void]], _NestedSequence[_SupportsArray[dtype[void]]], ] _ArrayLikeStr_co = _DualArrayLike[ dtype[str_], str, ] _ArrayLikeBytes_co = _DualArrayLike[ dtype[bytes_], bytes, ] _ArrayLikeInt = _DualArrayLike[ dtype[integer[Any]], int, ] # Extra ArrayLike type so that pyright can deal with NDArray[Any] # Used as the first overload, should only match NDArray[Any], # not any actual types. # https://github.com/numpy/numpy/pull/22193 class _UnknownType: ... _ArrayLikeUnknown = _DualArrayLike[ dtype[_UnknownType], _UnknownType, ]	4.14 KB	2026-01-20 12:58
📄 _callable.pyi """ A module with various ``typing.Protocol`` subclasses that implement the ``__call__`` magic method. See the `Mypy documentation`_ on protocols for more details. .. _`Mypy documentation`: https://mypy.readthedocs.io/en/stable/protocols.html#callback-protocols """ from __future__ import annotations from typing import ( TypeVar, overload, Any, NoReturn, Protocol, ) from numpy import ( ndarray, dtype, generic, bool_, timedelta64, number, integer, unsignedinteger, signedinteger, int8, int_, floating, float64, complexfloating, complex128, ) from ._nbit import _NBitInt, _NBitDouble from ._scalars import ( _BoolLike_co, _IntLike_co, _FloatLike_co, _NumberLike_co, ) from . import NBitBase from ._array_like import NDArray from ._nested_sequence import _NestedSequence _T1 = TypeVar("_T1") _T2 = TypeVar("_T2") _T1_contra = TypeVar("_T1_contra", contravariant=True) _T2_contra = TypeVar("_T2_contra", contravariant=True) _2Tuple = tuple[_T1, _T1] _NBit1 = TypeVar("_NBit1", bound=NBitBase) _NBit2 = TypeVar("_NBit2", bound=NBitBase) _IntType = TypeVar("_IntType", bound=integer) _FloatType = TypeVar("_FloatType", bound=floating) _NumberType = TypeVar("_NumberType", bound=number) _NumberType_co = TypeVar("_NumberType_co", covariant=True, bound=number) _GenericType_co = TypeVar("_GenericType_co", covariant=True, bound=generic) class _BoolOp(Protocol[_GenericType_co]): @overload def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ... @overload # platform dependent def __call__(self, other: int, /) -> int_: ... @overload def __call__(self, other: float, /) -> float64: ... @overload def __call__(self, other: complex, /) -> complex128: ... @overload def __call__(self, other: _NumberType, /) -> _NumberType: ... class _BoolBitOp(Protocol[_GenericType_co]): @overload def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ... @overload # platform dependent def __call__(self, other: int, /) -> int_: ... @overload def __call__(self, other: _IntType, /) -> _IntType: ... class _BoolSub(Protocol): # Note that `other: bool_` is absent here @overload def __call__(self, other: bool, /) -> NoReturn: ... @overload # platform dependent def __call__(self, other: int, /) -> int_: ... @overload def __call__(self, other: float, /) -> float64: ... @overload def __call__(self, other: complex, /) -> complex128: ... @overload def __call__(self, other: _NumberType, /) -> _NumberType: ... class _BoolTrueDiv(Protocol): @overload def __call__(self, other: float \| _IntLike_co, /) -> float64: ... @overload def __call__(self, other: complex, /) -> complex128: ... @overload def __call__(self, other: _NumberType, /) -> _NumberType: ... class _BoolMod(Protocol): @overload def __call__(self, other: _BoolLike_co, /) -> int8: ... @overload # platform dependent def __call__(self, other: int, /) -> int_: ... @overload def __call__(self, other: float, /) -> float64: ... @overload def __call__(self, other: _IntType, /) -> _IntType: ... @overload def __call__(self, other: _FloatType, /) -> _FloatType: ... class _BoolDivMod(Protocol): @overload def __call__(self, other: _BoolLike_co, /) -> _2Tuple[int8]: ... @overload # platform dependent def __call__(self, other: int, /) -> _2Tuple[int_]: ... @overload def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 \| _NBitDouble]]: ... @overload def __call__(self, other: _IntType, /) -> _2Tuple[_IntType]: ... @overload def __call__(self, other: _FloatType, /) -> _2Tuple[_FloatType]: ... class _TD64Div(Protocol[_NumberType_co]): @overload def __call__(self, other: timedelta64, /) -> _NumberType_co: ... @overload def __call__(self, other: _BoolLike_co, /) -> NoReturn: ... @overload def __call__(self, other: _FloatLike_co, /) -> timedelta64: ... class _IntTrueDiv(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> floating[_NBit1]: ... @overload def __call__(self, other: int, /) -> floating[_NBit1 \| _NBitInt]: ... @overload def __call__(self, other: float, /) -> floating[_NBit1 \| _NBitDouble]: ... @overload def __call__( self, other: complex, /, ) -> complexfloating[_NBit1 \| _NBitDouble, _NBit1 \| _NBitDouble]: ... @overload def __call__(self, other: integer[_NBit2], /) -> floating[_NBit1 \| _NBit2]: ... class _UnsignedIntOp(Protocol[_NBit1]): # NOTE: `uint64 + signedinteger -> float64` @overload def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ... @overload def __call__( self, other: int \| signedinteger[Any], / ) -> Any: ... @overload def __call__(self, other: float, /) -> floating[_NBit1 \| _NBitDouble]: ... @overload def __call__( self, other: complex, /, ) -> complexfloating[_NBit1 \| _NBitDouble, _NBit1 \| _NBitDouble]: ... @overload def __call__( self, other: unsignedinteger[_NBit2], / ) -> unsignedinteger[_NBit1 \| _NBit2]: ... class _UnsignedIntBitOp(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ... @overload def __call__(self, other: int, /) -> signedinteger[Any]: ... @overload def __call__(self, other: signedinteger[Any], /) -> signedinteger[Any]: ... @overload def __call__( self, other: unsignedinteger[_NBit2], / ) -> unsignedinteger[_NBit1 \| _NBit2]: ... class _UnsignedIntMod(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ... @overload def __call__( self, other: int \| signedinteger[Any], / ) -> Any: ... @overload def __call__(self, other: float, /) -> floating[_NBit1 \| _NBitDouble]: ... @overload def __call__( self, other: unsignedinteger[_NBit2], / ) -> unsignedinteger[_NBit1 \| _NBit2]: ... class _UnsignedIntDivMod(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ... @overload def __call__( self, other: int \| signedinteger[Any], / ) -> _2Tuple[Any]: ... @overload def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 \| _NBitDouble]]: ... @overload def __call__( self, other: unsignedinteger[_NBit2], / ) -> _2Tuple[unsignedinteger[_NBit1 \| _NBit2]]: ... class _SignedIntOp(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ... @overload def __call__(self, other: int, /) -> signedinteger[_NBit1 \| _NBitInt]: ... @overload def __call__(self, other: float, /) -> floating[_NBit1 \| _NBitDouble]: ... @overload def __call__( self, other: complex, /, ) -> complexfloating[_NBit1 \| _NBitDouble, _NBit1 \| _NBitDouble]: ... @overload def __call__( self, other: signedinteger[_NBit2], /, ) -> signedinteger[_NBit1 \| _NBit2]: ... class _SignedIntBitOp(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ... @overload def __call__(self, other: int, /) -> signedinteger[_NBit1 \| _NBitInt]: ... @overload def __call__( self, other: signedinteger[_NBit2], /, ) -> signedinteger[_NBit1 \| _NBit2]: ... class _SignedIntMod(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ... @overload def __call__(self, other: int, /) -> signedinteger[_NBit1 \| _NBitInt]: ... @overload def __call__(self, other: float, /) -> floating[_NBit1 \| _NBitDouble]: ... @overload def __call__( self, other: signedinteger[_NBit2], /, ) -> signedinteger[_NBit1 \| _NBit2]: ... class _SignedIntDivMod(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ... @overload def __call__(self, other: int, /) -> _2Tuple[signedinteger[_NBit1 \| _NBitInt]]: ... @overload def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 \| _NBitDouble]]: ... @overload def __call__( self, other: signedinteger[_NBit2], /, ) -> _2Tuple[signedinteger[_NBit1 \| _NBit2]]: ... class _FloatOp(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> floating[_NBit1]: ... @overload def __call__(self, other: int, /) -> floating[_NBit1 \| _NBitInt]: ... @overload def __call__(self, other: float, /) -> floating[_NBit1 \| _NBitDouble]: ... @overload def __call__( self, other: complex, /, ) -> complexfloating[_NBit1 \| _NBitDouble, _NBit1 \| _NBitDouble]: ... @overload def __call__( self, other: integer[_NBit2] \| floating[_NBit2], / ) -> floating[_NBit1 \| _NBit2]: ... class _FloatMod(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> floating[_NBit1]: ... @overload def __call__(self, other: int, /) -> floating[_NBit1 \| _NBitInt]: ... @overload def __call__(self, other: float, /) -> floating[_NBit1 \| _NBitDouble]: ... @overload def __call__( self, other: integer[_NBit2] \| floating[_NBit2], / ) -> floating[_NBit1 \| _NBit2]: ... class _FloatDivMod(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> _2Tuple[floating[_NBit1]]: ... @overload def __call__(self, other: int, /) -> _2Tuple[floating[_NBit1 \| _NBitInt]]: ... @overload def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 \| _NBitDouble]]: ... @overload def __call__( self, other: integer[_NBit2] \| floating[_NBit2], / ) -> _2Tuple[floating[_NBit1 \| _NBit2]]: ... class _ComplexOp(Protocol[_NBit1]): @overload def __call__(self, other: bool, /) -> complexfloating[_NBit1, _NBit1]: ... @overload def __call__(self, other: int, /) -> complexfloating[_NBit1 \| _NBitInt, _NBit1 \| _NBitInt]: ... @overload def __call__( self, other: complex, /, ) -> complexfloating[_NBit1 \| _NBitDouble, _NBit1 \| _NBitDouble]: ... @overload def __call__( self, other: ( integer[_NBit2] \| floating[_NBit2] \| complexfloating[_NBit2, _NBit2] ), /, ) -> complexfloating[_NBit1 \| _NBit2, _NBit1 \| _NBit2]: ... class _NumberOp(Protocol): def __call__(self, other: _NumberLike_co, /) -> Any: ... class _SupportsLT(Protocol): def __lt__(self, other: Any, /) -> object: ... class _SupportsGT(Protocol): def __gt__(self, other: Any, /) -> object: ... class _ComparisonOp(Protocol[_T1_contra, _T2_contra]): @overload def __call__(self, other: _T1_contra, /) -> bool_: ... @overload def __call__(self, other: _T2_contra, /) -> NDArray[bool_]: ... @overload def __call__( self, other: _SupportsLT \| _SupportsGT \| _NestedSequence[_SupportsLT \| _SupportsGT], /, ) -> Any: ...	10.86 KB	2026-01-20 12:58
📄 _char_codes.py from typing import Literal _BoolCodes = Literal["?", "=?", "<?", ">?", "bool", "bool_", "bool8"] _UInt8Codes = Literal["uint8", "u1", "=u1", "<u1", ">u1"] _UInt16Codes = Literal["uint16", "u2", "=u2", "<u2", ">u2"] _UInt32Codes = Literal["uint32", "u4", "=u4", "<u4", ">u4"] _UInt64Codes = Literal["uint64", "u8", "=u8", "<u8", ">u8"] _Int8Codes = Literal["int8", "i1", "=i1", "<i1", ">i1"] _Int16Codes = Literal["int16", "i2", "=i2", "<i2", ">i2"] _Int32Codes = Literal["int32", "i4", "=i4", "<i4", ">i4"] _Int64Codes = Literal["int64", "i8", "=i8", "<i8", ">i8"] _Float16Codes = Literal["float16", "f2", "=f2", "<f2", ">f2"] _Float32Codes = Literal["float32", "f4", "=f4", "<f4", ">f4"] _Float64Codes = Literal["float64", "f8", "=f8", "<f8", ">f8"] _Complex64Codes = Literal["complex64", "c8", "=c8", "<c8", ">c8"] _Complex128Codes = Literal["complex128", "c16", "=c16", "<c16", ">c16"] _ByteCodes = Literal["byte", "b", "=b", "<b", ">b"] _ShortCodes = Literal["short", "h", "=h", "<h", ">h"] _IntCCodes = Literal["intc", "i", "=i", "<i", ">i"] _IntPCodes = Literal["intp", "int0", "p", "=p", "<p", ">p"] _IntCodes = Literal["long", "int", "int_", "l", "=l", "<l", ">l"] _LongLongCodes = Literal["longlong", "q", "=q", "<q", ">q"] _UByteCodes = Literal["ubyte", "B", "=B", "<B", ">B"] _UShortCodes = Literal["ushort", "H", "=H", "<H", ">H"] _UIntCCodes = Literal["uintc", "I", "=I", "<I", ">I"] _UIntPCodes = Literal["uintp", "uint0", "P", "=P", "<P", ">P"] _UIntCodes = Literal["ulong", "uint", "L", "=L", "<L", ">L"] _ULongLongCodes = Literal["ulonglong", "Q", "=Q", "<Q", ">Q"] _HalfCodes = Literal["half", "e", "=e", "<e", ">e"] _SingleCodes = Literal["single", "f", "=f", "<f", ">f"] _DoubleCodes = Literal["double", "float", "float_", "d", "=d", "<d", ">d"] _LongDoubleCodes = Literal["longdouble", "longfloat", "g", "=g", "<g", ">g"] _CSingleCodes = Literal["csingle", "singlecomplex", "F", "=F", "<F", ">F"] _CDoubleCodes = Literal["cdouble", "complex", "complex_", "cfloat", "D", "=D", "<D", ">D"] _CLongDoubleCodes = Literal["clongdouble", "clongfloat", "longcomplex", "G", "=G", "<G", ">G"] _StrCodes = Literal["str", "str_", "str0", "unicode", "unicode_", "U", "=U", "<U", ">U"] _BytesCodes = Literal["bytes", "bytes_", "bytes0", "S", "=S", "<S", ">S"] _VoidCodes = Literal["void", "void0", "V", "=V", "<V", ">V"] _ObjectCodes = Literal["object", "object_", "O", "=O", "<O", ">O"] _DT64Codes = Literal[ "datetime64", "=datetime64", "<datetime64", ">datetime64", "datetime64[Y]", "=datetime64[Y]", "<datetime64[Y]", ">datetime64[Y]", "datetime64[M]", "=datetime64[M]", "<datetime64[M]", ">datetime64[M]", "datetime64[W]", "=datetime64[W]", "<datetime64[W]", ">datetime64[W]", "datetime64[D]", "=datetime64[D]", "<datetime64[D]", ">datetime64[D]", "datetime64[h]", "=datetime64[h]", "<datetime64[h]", ">datetime64[h]", "datetime64[m]", "=datetime64[m]", "<datetime64[m]", ">datetime64[m]", "datetime64[s]", "=datetime64[s]", "<datetime64[s]", ">datetime64[s]", "datetime64[ms]", "=datetime64[ms]", "<datetime64[ms]", ">datetime64[ms]", "datetime64[us]", "=datetime64[us]", "<datetime64[us]", ">datetime64[us]", "datetime64[ns]", "=datetime64[ns]", "<datetime64[ns]", ">datetime64[ns]", "datetime64[ps]", "=datetime64[ps]", "<datetime64[ps]", ">datetime64[ps]", "datetime64[fs]", "=datetime64[fs]", "<datetime64[fs]", ">datetime64[fs]", "datetime64[as]", "=datetime64[as]", "<datetime64[as]", ">datetime64[as]", "M", "=M", "<M", ">M", "M8", "=M8", "<M8", ">M8", "M8[Y]", "=M8[Y]", "<M8[Y]", ">M8[Y]", "M8[M]", "=M8[M]", "<M8[M]", ">M8[M]", "M8[W]", "=M8[W]", "<M8[W]", ">M8[W]", "M8[D]", "=M8[D]", "<M8[D]", ">M8[D]", "M8[h]", "=M8[h]", "<M8[h]", ">M8[h]", "M8[m]", "=M8[m]", "<M8[m]", ">M8[m]", "M8[s]", "=M8[s]", "<M8[s]", ">M8[s]", "M8[ms]", "=M8[ms]", "<M8[ms]", ">M8[ms]", "M8[us]", "=M8[us]", "<M8[us]", ">M8[us]", "M8[ns]", "=M8[ns]", "<M8[ns]", ">M8[ns]", "M8[ps]", "=M8[ps]", "<M8[ps]", ">M8[ps]", "M8[fs]", "=M8[fs]", "<M8[fs]", ">M8[fs]", "M8[as]", "=M8[as]", "<M8[as]", ">M8[as]", ] _TD64Codes = Literal[ "timedelta64", "=timedelta64", "<timedelta64", ">timedelta64", "timedelta64[Y]", "=timedelta64[Y]", "<timedelta64[Y]", ">timedelta64[Y]", "timedelta64[M]", "=timedelta64[M]", "<timedelta64[M]", ">timedelta64[M]", "timedelta64[W]", "=timedelta64[W]", "<timedelta64[W]", ">timedelta64[W]", "timedelta64[D]", "=timedelta64[D]", "<timedelta64[D]", ">timedelta64[D]", "timedelta64[h]", "=timedelta64[h]", "<timedelta64[h]", ">timedelta64[h]", "timedelta64[m]", "=timedelta64[m]", "<timedelta64[m]", ">timedelta64[m]", "timedelta64[s]", "=timedelta64[s]", "<timedelta64[s]", ">timedelta64[s]", "timedelta64[ms]", "=timedelta64[ms]", "<timedelta64[ms]", ">timedelta64[ms]", "timedelta64[us]", "=timedelta64[us]", "<timedelta64[us]", ">timedelta64[us]", "timedelta64[ns]", "=timedelta64[ns]", "<timedelta64[ns]", ">timedelta64[ns]", "timedelta64[ps]", "=timedelta64[ps]", "<timedelta64[ps]", ">timedelta64[ps]", "timedelta64[fs]", "=timedelta64[fs]", "<timedelta64[fs]", ">timedelta64[fs]", "timedelta64[as]", "=timedelta64[as]", "<timedelta64[as]", ">timedelta64[as]", "m", "=m", "<m", ">m", "m8", "=m8", "<m8", ">m8", "m8[Y]", "=m8[Y]", "<m8[Y]", ">m8[Y]", "m8[M]", "=m8[M]", "<m8[M]", ">m8[M]", "m8[W]", "=m8[W]", "<m8[W]", ">m8[W]", "m8[D]", "=m8[D]", "<m8[D]", ">m8[D]", "m8[h]", "=m8[h]", "<m8[h]", ">m8[h]", "m8[m]", "=m8[m]", "<m8[m]", ">m8[m]", "m8[s]", "=m8[s]", "<m8[s]", ">m8[s]", "m8[ms]", "=m8[ms]", "<m8[ms]", ">m8[ms]", "m8[us]", "=m8[us]", "<m8[us]", ">m8[us]", "m8[ns]", "=m8[ns]", "<m8[ns]", ">m8[ns]", "m8[ps]", "=m8[ps]", "<m8[ps]", ">m8[ps]", "m8[fs]", "=m8[fs]", "<m8[fs]", ">m8[fs]", "m8[as]", "=m8[as]", "<m8[as]", ">m8[as]", ]	5.78 KB	2026-01-20 12:58
📄 _dtype_like.py from collections.abc import Sequence from typing import ( Any, Sequence, Union, TypeVar, Protocol, TypedDict, runtime_checkable, ) import numpy as np from ._shape import _ShapeLike from ._char_codes import ( _BoolCodes, _UInt8Codes, _UInt16Codes, _UInt32Codes, _UInt64Codes, _Int8Codes, _Int16Codes, _Int32Codes, _Int64Codes, _Float16Codes, _Float32Codes, _Float64Codes, _Complex64Codes, _Complex128Codes, _ByteCodes, _ShortCodes, _IntCCodes, _IntPCodes, _IntCodes, _LongLongCodes, _UByteCodes, _UShortCodes, _UIntCCodes, _UIntPCodes, _UIntCodes, _ULongLongCodes, _HalfCodes, _SingleCodes, _DoubleCodes, _LongDoubleCodes, _CSingleCodes, _CDoubleCodes, _CLongDoubleCodes, _DT64Codes, _TD64Codes, _StrCodes, _BytesCodes, _VoidCodes, _ObjectCodes, ) _SCT = TypeVar("_SCT", bound=np.generic) _DType_co = TypeVar("_DType_co", covariant=True, bound=np.dtype[Any]) _DTypeLikeNested = Any # TODO: wait for support for recursive types # Mandatory keys class _DTypeDictBase(TypedDict): names: Sequence[str] formats: Sequence[_DTypeLikeNested] # Mandatory + optional keys class _DTypeDict(_DTypeDictBase, total=False): # Only `str` elements are usable as indexing aliases, # but `titles` can in principle accept any object offsets: Sequence[int] titles: Sequence[Any] itemsize: int aligned: bool # A protocol for anything with the dtype attribute @runtime_checkable class _SupportsDType(Protocol[_DType_co]): @property def dtype(self) -> _DType_co: ... # A subset of `npt.DTypeLike` that can be parametrized w.r.t. `np.generic` _DTypeLike = Union[ np.dtype[_SCT], type[_SCT], _SupportsDType[np.dtype[_SCT]], ] # Would create a dtype[np.void] _VoidDTypeLike = Union[ # (flexible_dtype, itemsize) tuple[_DTypeLikeNested, int], # (fixed_dtype, shape) tuple[_DTypeLikeNested, _ShapeLike], # [(field_name, field_dtype, field_shape), ...] # # The type here is quite broad because NumPy accepts quite a wide # range of inputs inside the list; see the tests for some # examples. list[Any], # {'names': ..., 'formats': ..., 'offsets': ..., 'titles': ..., # 'itemsize': ...} _DTypeDict, # (base_dtype, new_dtype) tuple[_DTypeLikeNested, _DTypeLikeNested], ] # Anything that can be coerced into numpy.dtype. # Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html DTypeLike = Union[ np.dtype[Any], # default data type (float64) None, # array-scalar types and generic types type[Any], # NOTE: We're stuck with `type[Any]` due to object dtypes # anything with a dtype attribute _SupportsDType[np.dtype[Any]], # character codes, type strings or comma-separated fields, e.g., 'float64' str, _VoidDTypeLike, ] # NOTE: while it is possible to provide the dtype as a dict of # dtype-like objects (e.g. `{'field1': ..., 'field2': ..., ...}`), # this syntax is officially discourged and # therefore not included in the Union defining `DTypeLike`. # # See https://github.com/numpy/numpy/issues/16891 for more details. # Aliases for commonly used dtype-like objects. # Note that the precision of `np.number` subclasses is ignored herein. _DTypeLikeBool = Union[ type[bool], type[np.bool_], np.dtype[np.bool_], _SupportsDType[np.dtype[np.bool_]], _BoolCodes, ] _DTypeLikeUInt = Union[ type[np.unsignedinteger], np.dtype[np.unsignedinteger], _SupportsDType[np.dtype[np.unsignedinteger]], _UInt8Codes, _UInt16Codes, _UInt32Codes, _UInt64Codes, _UByteCodes, _UShortCodes, _UIntCCodes, _UIntPCodes, _UIntCodes, _ULongLongCodes, ] _DTypeLikeInt = Union[ type[int], type[np.signedinteger], np.dtype[np.signedinteger], _SupportsDType[np.dtype[np.signedinteger]], _Int8Codes, _Int16Codes, _Int32Codes, _Int64Codes, _ByteCodes, _ShortCodes, _IntCCodes, _IntPCodes, _IntCodes, _LongLongCodes, ] _DTypeLikeFloat = Union[ type[float], type[np.floating], np.dtype[np.floating], _SupportsDType[np.dtype[np.floating]], _Float16Codes, _Float32Codes, _Float64Codes, _HalfCodes, _SingleCodes, _DoubleCodes, _LongDoubleCodes, ] _DTypeLikeComplex = Union[ type[complex], type[np.complexfloating], np.dtype[np.complexfloating], _SupportsDType[np.dtype[np.complexfloating]], _Complex64Codes, _Complex128Codes, _CSingleCodes, _CDoubleCodes, _CLongDoubleCodes, ] _DTypeLikeDT64 = Union[ type[np.timedelta64], np.dtype[np.timedelta64], _SupportsDType[np.dtype[np.timedelta64]], _TD64Codes, ] _DTypeLikeTD64 = Union[ type[np.datetime64], np.dtype[np.datetime64], _SupportsDType[np.dtype[np.datetime64]], _DT64Codes, ] _DTypeLikeStr = Union[ type[str], type[np.str_], np.dtype[np.str_], _SupportsDType[np.dtype[np.str_]], _StrCodes, ] _DTypeLikeBytes = Union[ type[bytes], type[np.bytes_], np.dtype[np.bytes_], _SupportsDType[np.dtype[np.bytes_]], _BytesCodes, ] _DTypeLikeVoid = Union[ type[np.void], np.dtype[np.void], _SupportsDType[np.dtype[np.void]], _VoidCodes, _VoidDTypeLike, ] _DTypeLikeObject = Union[ type, np.dtype[np.object_], _SupportsDType[np.dtype[np.object_]], _ObjectCodes, ] _DTypeLikeComplex_co = Union[ _DTypeLikeBool, _DTypeLikeUInt, _DTypeLikeInt, _DTypeLikeFloat, _DTypeLikeComplex, ]	5.53 KB	2026-01-20 12:58
📄 _extended_precision.py """A module with platform-specific extended precision `numpy.number` subclasses. The subclasses are defined here (instead of ``__init__.pyi``) such that they can be imported conditionally via the numpy's mypy plugin. """ import numpy as np from . import ( _80Bit, _96Bit, _128Bit, _256Bit, ) uint128 = np.unsignedinteger[_128Bit] uint256 = np.unsignedinteger[_256Bit] int128 = np.signedinteger[_128Bit] int256 = np.signedinteger[_256Bit] float80 = np.floating[_80Bit] float96 = np.floating[_96Bit] float128 = np.floating[_128Bit] float256 = np.floating[_256Bit] complex160 = np.complexfloating[_80Bit, _80Bit] complex192 = np.complexfloating[_96Bit, _96Bit] complex256 = np.complexfloating[_128Bit, _128Bit] complex512 = np.complexfloating[_256Bit, _256Bit]	777 B	2026-01-20 12:58
📄 _nbit.py """A module with the precisions of platform-specific `~numpy.number`s.""" from typing import Any # To-be replaced with a `npt.NBitBase` subclass by numpy's mypy plugin _NBitByte = Any _NBitShort = Any _NBitIntC = Any _NBitIntP = Any _NBitInt = Any _NBitLongLong = Any _NBitHalf = Any _NBitSingle = Any _NBitDouble = Any _NBitLongDouble = Any	345 B	2026-01-20 12:58
📄 _nested_sequence.py """A module containing the `_NestedSequence` protocol.""" from __future__ import annotations from collections.abc import Iterator from typing import ( Any, overload, TypeVar, Protocol, runtime_checkable, ) __all__ = ["_NestedSequence"] _T_co = TypeVar("_T_co", covariant=True) @runtime_checkable class _NestedSequence(Protocol[_T_co]): """A protocol for representing nested sequences. Warning ------- `_NestedSequence` currently does not work in combination with typevars, e.g. ``def func(a: _NestedSequnce[T]) -> T: ...``. See Also -------- collections.abc.Sequence ABCs for read-only and mutable :term:`sequences`. Examples -------- .. code-block:: python >>> from __future__ import annotations >>> from typing import TYPE_CHECKING >>> import numpy as np >>> from numpy._typing import _NestedSequence >>> def get_dtype(seq: _NestedSequence[float]) -> np.dtype[np.float64]: ... return np.asarray(seq).dtype >>> a = get_dtype([1.0]) >>> b = get_dtype([[1.0]]) >>> c = get_dtype([[[1.0]]]) >>> d = get_dtype([[[[1.0]]]]) >>> if TYPE_CHECKING: ... reveal_locals() ... # note: Revealed local types are: ... # note: a: numpy.dtype[numpy.floating[numpy._typing._64Bit]] ... # note: b: numpy.dtype[numpy.floating[numpy._typing._64Bit]] ... # note: c: numpy.dtype[numpy.floating[numpy._typing._64Bit]] ... # note: d: numpy.dtype[numpy.floating[numpy._typing._64Bit]] """ def __len__(self, /) -> int: """Implement ``len(self)``.""" raise NotImplementedError @overload def __getitem__(self, index: int, /) -> _T_co \| _NestedSequence[_T_co]: ... @overload def __getitem__(self, index: slice, /) -> _NestedSequence[_T_co]: ... def __getitem__(self, index, /): """Implement ``self[x]``.""" raise NotImplementedError def __contains__(self, x: object, /) -> bool: """Implement ``x in self``.""" raise NotImplementedError def __iter__(self, /) -> Iterator[_T_co \| _NestedSequence[_T_co]]: """Implement ``iter(self)``.""" raise NotImplementedError def __reversed__(self, /) -> Iterator[_T_co \| _NestedSequence[_T_co]]: """Implement ``reversed(self)``.""" raise NotImplementedError def count(self, value: Any, /) -> int: """Return the number of occurrences of `value`.""" raise NotImplementedError def index(self, value: Any, /) -> int: """Return the first index of `value`.""" raise NotImplementedError	2.66 KB	2026-01-20 12:58
📄 _scalars.py from typing import Union, Any import numpy as np # NOTE: `_StrLike_co` and `_BytesLike_co` are pointless, as `np.str_` and # `np.bytes_` are already subclasses of their builtin counterpart _CharLike_co = Union[str, bytes] # The 6 `<X>Like_co` type-aliases below represent all scalars that can be # coerced into `<X>` (with the casting rule `same_kind`) _BoolLike_co = Union[bool, np.bool_] _UIntLike_co = Union[_BoolLike_co, np.unsignedinteger[Any]] _IntLike_co = Union[_BoolLike_co, int, np.integer[Any]] _FloatLike_co = Union[_IntLike_co, float, np.floating[Any]] _ComplexLike_co = Union[_FloatLike_co, complex, np.complexfloating[Any, Any]] _TD64Like_co = Union[_IntLike_co, np.timedelta64] _NumberLike_co = Union[int, float, complex, np.number[Any], np.bool_] _ScalarLike_co = Union[ int, float, complex, str, bytes, np.generic, ] # `_VoidLike_co` is technically not a scalar, but it's close enough _VoidLike_co = Union[tuple[Any, ...], np.void]	980 B	2026-01-20 12:58
📄 _shape.py from collections.abc import Sequence from typing import Union, SupportsIndex _Shape = tuple[int, ...] # Anything that can be coerced to a shape tuple _ShapeLike = Union[SupportsIndex, Sequence[SupportsIndex]]	211 B	2026-01-20 12:58
📄 _ufunc.pyi """A module with private type-check-only `numpy.ufunc` subclasses. The signatures of the ufuncs are too varied to reasonably type with a single class. So instead, `ufunc` has been expanded into four private subclasses, one for each combination of `~ufunc.nin` and `~ufunc.nout`. """ from typing import ( Any, Generic, overload, TypeVar, Literal, SupportsIndex, Protocol, ) from numpy import ufunc, _CastingKind, _OrderKACF from numpy.typing import NDArray from ._shape import _ShapeLike from ._scalars import _ScalarLike_co from ._array_like import ArrayLike, _ArrayLikeBool_co, _ArrayLikeInt_co from ._dtype_like import DTypeLike _T = TypeVar("_T") _2Tuple = tuple[_T, _T] _3Tuple = tuple[_T, _T, _T] _4Tuple = tuple[_T, _T, _T, _T] _NTypes = TypeVar("_NTypes", bound=int) _IDType = TypeVar("_IDType", bound=Any) _NameType = TypeVar("_NameType", bound=str) class _SupportsArrayUFunc(Protocol): def __array_ufunc__( self, ufunc: ufunc, method: Literal["__call__", "reduce", "reduceat", "accumulate", "outer", "inner"], inputs: Any, kwargs: Any, ) -> Any: ... # NOTE: In reality `extobj` should be a length of list 3 containing an # int, an int, and a callable, but there's no way to properly express # non-homogenous lists. # Use `Any` over `Union` to avoid issues related to lists invariance. # NOTE: `reduce`, `accumulate`, `reduceat` and `outer` raise a ValueError for # ufuncs that don't accept two input arguments and return one output argument. # In such cases the respective methods are simply typed as `None`. # NOTE: Similarly, `at` won't be defined for ufuncs that return # multiple outputs; in such cases `at` is typed as `None` # NOTE: If 2 output types are returned then `out` must be a # 2-tuple of arrays. Otherwise `None` or a plain array are also acceptable class _UFunc_Nin1_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc] @property def __name__(self) -> _NameType: ... @property def ntypes(self) -> _NTypes: ... @property def identity(self) -> _IDType: ... @property def nin(self) -> Literal[1]: ... @property def nout(self) -> Literal[1]: ... @property def nargs(self) -> Literal[2]: ... @property def signature(self) -> None: ... @property def reduce(self) -> None: ... @property def accumulate(self) -> None: ... @property def reduceat(self) -> None: ... @property def outer(self) -> None: ... @overload def __call__( self, __x1: _ScalarLike_co, out: None = ..., , where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _2Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> Any: ... @overload def __call__( self, __x1: ArrayLike, out: None \| NDArray[Any] \| tuple[NDArray[Any]] = ..., , where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _2Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> NDArray[Any]: ... @overload def __call__( self, __x1: _SupportsArrayUFunc, out: None \| NDArray[Any] \| tuple[NDArray[Any]] = ..., , where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _2Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> Any: ... def at( self, a: _SupportsArrayUFunc, indices: _ArrayLikeInt_co, /, ) -> None: ... class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc] @property def __name__(self) -> _NameType: ... @property def ntypes(self) -> _NTypes: ... @property def identity(self) -> _IDType: ... @property def nin(self) -> Literal[2]: ... @property def nout(self) -> Literal[1]: ... @property def nargs(self) -> Literal[3]: ... @property def signature(self) -> None: ... @overload def __call__( self, __x1: _ScalarLike_co, __x2: _ScalarLike_co, out: None = ..., , where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _3Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> Any: ... @overload def __call__( self, __x1: ArrayLike, __x2: ArrayLike, out: None \| NDArray[Any] \| tuple[NDArray[Any]] = ..., , where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _3Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> NDArray[Any]: ... def at( self, a: NDArray[Any], indices: _ArrayLikeInt_co, b: ArrayLike, /, ) -> None: ... def reduce( self, array: ArrayLike, axis: None \| _ShapeLike = ..., dtype: DTypeLike = ..., out: None \| NDArray[Any] = ..., keepdims: bool = ..., initial: Any = ..., where: _ArrayLikeBool_co = ..., ) -> Any: ... def accumulate( self, array: ArrayLike, axis: SupportsIndex = ..., dtype: DTypeLike = ..., out: None \| NDArray[Any] = ..., ) -> NDArray[Any]: ... def reduceat( self, array: ArrayLike, indices: _ArrayLikeInt_co, axis: SupportsIndex = ..., dtype: DTypeLike = ..., out: None \| NDArray[Any] = ..., ) -> NDArray[Any]: ... # Expand `*kwargs` into explicit keyword-only arguments @overload def outer( self, A: _ScalarLike_co, B: _ScalarLike_co, /, , out: None = ..., where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _3Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> Any: ... @overload def outer( # type: ignore[misc] self, A: ArrayLike, B: ArrayLike, /, , out: None \| NDArray[Any] \| tuple[NDArray[Any]] = ..., where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _3Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> NDArray[Any]: ... class _UFunc_Nin1_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc] @property def __name__(self) -> _NameType: ... @property def ntypes(self) -> _NTypes: ... @property def identity(self) -> _IDType: ... @property def nin(self) -> Literal[1]: ... @property def nout(self) -> Literal[2]: ... @property def nargs(self) -> Literal[3]: ... @property def signature(self) -> None: ... @property def at(self) -> None: ... @property def reduce(self) -> None: ... @property def accumulate(self) -> None: ... @property def reduceat(self) -> None: ... @property def outer(self) -> None: ... @overload def __call__( self, __x1: _ScalarLike_co, __out1: None = ..., __out2: None = ..., , where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _3Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> _2Tuple[Any]: ... @overload def __call__( self, __x1: ArrayLike, __out1: None \| NDArray[Any] = ..., __out2: None \| NDArray[Any] = ..., , out: _2Tuple[NDArray[Any]] = ..., where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _3Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> _2Tuple[NDArray[Any]]: ... @overload def __call__( self, __x1: _SupportsArrayUFunc, __out1: None \| NDArray[Any] = ..., __out2: None \| NDArray[Any] = ..., , out: _2Tuple[NDArray[Any]] = ..., where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _3Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> _2Tuple[Any]: ... class _UFunc_Nin2_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc] @property def __name__(self) -> _NameType: ... @property def ntypes(self) -> _NTypes: ... @property def identity(self) -> _IDType: ... @property def nin(self) -> Literal[2]: ... @property def nout(self) -> Literal[2]: ... @property def nargs(self) -> Literal[4]: ... @property def signature(self) -> None: ... @property def at(self) -> None: ... @property def reduce(self) -> None: ... @property def accumulate(self) -> None: ... @property def reduceat(self) -> None: ... @property def outer(self) -> None: ... @overload def __call__( self, __x1: _ScalarLike_co, __x2: _ScalarLike_co, __out1: None = ..., __out2: None = ..., , where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _4Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> _2Tuple[Any]: ... @overload def __call__( self, __x1: ArrayLike, __x2: ArrayLike, __out1: None \| NDArray[Any] = ..., __out2: None \| NDArray[Any] = ..., , out: _2Tuple[NDArray[Any]] = ..., where: None \| _ArrayLikeBool_co = ..., casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _4Tuple[None \| str] = ..., extobj: list[Any] = ..., ) -> _2Tuple[NDArray[Any]]: ... class _GUFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc] @property def __name__(self) -> _NameType: ... @property def ntypes(self) -> _NTypes: ... @property def identity(self) -> _IDType: ... @property def nin(self) -> Literal[2]: ... @property def nout(self) -> Literal[1]: ... @property def nargs(self) -> Literal[3]: ... # NOTE: In practice the only gufunc in the main namespace is `matmul`, # so we can use its signature here @property def signature(self) -> Literal["(n?,k),(k,m?)->(n?,m?)"]: ... @property def reduce(self) -> None: ... @property def accumulate(self) -> None: ... @property def reduceat(self) -> None: ... @property def outer(self) -> None: ... @property def at(self) -> None: ... # Scalar for 1D array-likes; ndarray otherwise @overload def __call__( self, __x1: ArrayLike, __x2: ArrayLike, out: None = ..., , casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _3Tuple[None \| str] = ..., extobj: list[Any] = ..., axes: list[_2Tuple[SupportsIndex]] = ..., ) -> Any: ... @overload def __call__( self, __x1: ArrayLike, __x2: ArrayLike, out: NDArray[Any] \| tuple[NDArray[Any]], , casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., signature: str \| _3Tuple[None \| str] = ..., extobj: list[Any] = ..., axes: list[_2Tuple[SupportsIndex]] = ..., ) -> NDArray[Any]: ...	12.34 KB	2026-01-20 12:58
📄 setup.py def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration config = Configuration('_typing', parent_package, top_path) config.add_data_files('*.pyi') return config if __name__ == '__main__': from numpy.distutils.core import setup setup(configuration=configuration)	337 B	2026-01-20 12:58

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