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5548 lines
214 KiB
Python
5548 lines
214 KiB
Python
# ruff: noqa: I001
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import builtins
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import sys
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import mmap
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import ctypes as ct
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import array as _array
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import datetime as dt
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from abc import abstractmethod
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from types import EllipsisType, ModuleType, TracebackType, MappingProxyType, GenericAlias
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from decimal import Decimal
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from fractions import Fraction
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from uuid import UUID
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import numpy as np
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from numpy.__config__ import show as show_config
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from numpy._pytesttester import PytestTester
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from numpy._core._internal import _ctypes
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from numpy._typing import (
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# Arrays
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ArrayLike,
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NDArray,
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_SupportsArray,
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_NestedSequence,
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_ArrayLike,
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_ArrayLikeBool_co,
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_ArrayLikeUInt_co,
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_ArrayLikeInt,
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_ArrayLikeInt_co,
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_ArrayLikeFloat64_co,
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_ArrayLikeFloat_co,
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_ArrayLikeComplex128_co,
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_ArrayLikeComplex_co,
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_ArrayLikeNumber_co,
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_ArrayLikeObject_co,
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_ArrayLikeBytes_co,
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_ArrayLikeStr_co,
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_ArrayLikeString_co,
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_ArrayLikeTD64_co,
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_ArrayLikeDT64_co,
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# DTypes
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DTypeLike,
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_DTypeLike,
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_DTypeLikeVoid,
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_VoidDTypeLike,
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# Shapes
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_AnyShape,
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_Shape,
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_ShapeLike,
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# Scalars
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_CharLike_co,
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_IntLike_co,
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_FloatLike_co,
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_TD64Like_co,
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_NumberLike_co,
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_ScalarLike_co,
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# `number` precision
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NBitBase,
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# NOTE: Do not remove the extended precision bit-types even if seemingly unused;
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# they're used by the mypy plugin
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_128Bit,
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_96Bit,
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_64Bit,
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_32Bit,
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_16Bit,
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_8Bit,
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_NBitByte,
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_NBitShort,
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_NBitIntC,
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_NBitIntP,
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_NBitLong,
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_NBitLongLong,
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_NBitHalf,
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_NBitSingle,
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_NBitDouble,
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_NBitLongDouble,
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# Character codes
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_BoolCodes,
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_UInt8Codes,
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_UInt16Codes,
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_UInt32Codes,
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_UInt64Codes,
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_Int8Codes,
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_Int16Codes,
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_Int32Codes,
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_Int64Codes,
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_Float16Codes,
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_Float32Codes,
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_Float64Codes,
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_Complex64Codes,
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_Complex128Codes,
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_ByteCodes,
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_ShortCodes,
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_IntCCodes,
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_IntPCodes,
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_LongCodes,
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_LongLongCodes,
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_UByteCodes,
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_UShortCodes,
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_UIntCCodes,
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_UIntPCodes,
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_ULongCodes,
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_ULongLongCodes,
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_HalfCodes,
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_SingleCodes,
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_DoubleCodes,
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_LongDoubleCodes,
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_CSingleCodes,
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_CDoubleCodes,
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_CLongDoubleCodes,
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_DT64Codes,
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_TD64Codes,
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_StrCodes,
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_BytesCodes,
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_VoidCodes,
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_ObjectCodes,
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_StringCodes,
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_UnsignedIntegerCodes,
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_SignedIntegerCodes,
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_IntegerCodes,
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_FloatingCodes,
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_ComplexFloatingCodes,
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_InexactCodes,
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_NumberCodes,
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_CharacterCodes,
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_FlexibleCodes,
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_GenericCodes,
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# Ufuncs
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_UFunc_Nin1_Nout1,
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_UFunc_Nin2_Nout1,
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_UFunc_Nin1_Nout2,
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_UFunc_Nin2_Nout2,
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_GUFunc_Nin2_Nout1,
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)
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from numpy._typing._callable import (
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_IntTrueDiv,
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_UnsignedIntOp,
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_UnsignedIntBitOp,
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_UnsignedIntMod,
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_UnsignedIntDivMod,
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_SignedIntOp,
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_SignedIntBitOp,
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_SignedIntMod,
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_SignedIntDivMod,
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_FloatOp,
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_FloatMod,
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_FloatDivMod,
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_NumberOp,
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_ComparisonOpLT,
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_ComparisonOpLE,
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_ComparisonOpGT,
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_ComparisonOpGE,
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)
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# NOTE: Numpy's mypy plugin is used for removing the types unavailable to the specific platform
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from numpy._typing._extended_precision import (
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float96,
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float128,
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complex192,
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complex256,
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)
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from numpy._array_api_info import __array_namespace_info__
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from collections.abc import (
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Callable,
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Iterable,
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Iterator,
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Mapping,
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Sequence,
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)
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if sys.version_info >= (3, 12):
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from collections.abc import Buffer as _SupportsBuffer
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else:
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_SupportsBuffer: TypeAlias = (
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bytes
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| bytearray
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| memoryview
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| _array.array[Any]
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| mmap.mmap
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| NDArray[Any]
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| generic
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)
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from typing import (
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Any,
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ClassVar,
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Final,
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Generic,
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Literal as L,
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LiteralString,
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Never,
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NoReturn,
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Protocol,
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Self,
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SupportsComplex,
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SupportsFloat,
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SupportsInt,
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SupportsIndex,
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TypeAlias,
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TypedDict,
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final,
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overload,
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type_check_only,
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)
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# NOTE: `typing_extensions` and `_typeshed` are always available in `.pyi` stubs, even
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# if not available at runtime. This is because the `typeshed` stubs for the standard
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# library include `typing_extensions` stubs:
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# https://github.com/python/typeshed/blob/main/stdlib/typing_extensions.pyi
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from _typeshed import Incomplete, StrOrBytesPath, SupportsFlush, SupportsLenAndGetItem, SupportsWrite
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from typing_extensions import CapsuleType, TypeVar
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from numpy import (
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char,
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core,
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ctypeslib,
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dtypes,
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exceptions,
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f2py,
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fft,
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lib,
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linalg,
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ma,
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polynomial,
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random,
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rec,
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strings,
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testing,
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typing,
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)
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# available through `__getattr__`, but not in `__all__` or `__dir__`
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from numpy import (
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__config__ as __config__,
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matlib as matlib,
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matrixlib as matrixlib,
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version as version,
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)
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if sys.version_info < (3, 12):
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from numpy import distutils as distutils
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from numpy._core.records import (
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record,
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recarray,
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)
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from numpy._core.function_base import (
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linspace,
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logspace,
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geomspace,
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)
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from numpy._core.fromnumeric import (
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take,
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reshape,
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choose,
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repeat,
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put,
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swapaxes,
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transpose,
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matrix_transpose,
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partition,
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argpartition,
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sort,
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argsort,
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argmax,
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argmin,
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searchsorted,
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resize,
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squeeze,
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diagonal,
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trace,
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ravel,
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nonzero,
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shape,
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compress,
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clip,
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sum,
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all,
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any,
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cumsum,
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cumulative_sum,
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ptp,
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max,
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min,
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amax,
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amin,
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prod,
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cumprod,
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cumulative_prod,
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ndim,
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size,
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around,
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round,
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mean,
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std,
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var,
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)
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from numpy._core._asarray import (
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require,
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)
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from numpy._core._type_aliases import (
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sctypeDict,
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)
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from numpy._core._ufunc_config import (
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seterr,
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geterr,
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setbufsize,
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getbufsize,
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seterrcall,
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geterrcall,
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_ErrKind,
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_ErrCall,
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)
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from numpy._core.arrayprint import (
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set_printoptions,
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get_printoptions,
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array2string,
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format_float_scientific,
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format_float_positional,
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array_repr,
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array_str,
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printoptions,
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)
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from numpy._core.einsumfunc import (
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einsum,
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einsum_path,
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)
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from numpy._core.multiarray import (
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array,
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empty_like,
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empty,
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zeros,
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concatenate,
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inner,
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where,
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lexsort,
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can_cast,
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min_scalar_type,
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result_type,
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dot,
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vdot,
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bincount,
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copyto,
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putmask,
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packbits,
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unpackbits,
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shares_memory,
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may_share_memory,
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asarray,
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asanyarray,
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ascontiguousarray,
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asfortranarray,
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arange,
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busday_count,
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busday_offset,
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datetime_as_string,
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datetime_data,
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frombuffer,
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fromfile,
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fromiter,
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is_busday,
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promote_types,
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fromstring,
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frompyfunc,
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nested_iters,
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flagsobj,
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)
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from numpy._core.numeric import (
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zeros_like,
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ones,
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ones_like,
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full,
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full_like,
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count_nonzero,
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isfortran,
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argwhere,
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flatnonzero,
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correlate,
|
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convolve,
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outer,
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|
tensordot,
|
|
roll,
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|
rollaxis,
|
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moveaxis,
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cross,
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|
indices,
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fromfunction,
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isscalar,
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binary_repr,
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base_repr,
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identity,
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|
allclose,
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isclose,
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array_equal,
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array_equiv,
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astype,
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)
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from numpy._core.numerictypes import (
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isdtype,
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issubdtype,
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ScalarType,
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typecodes,
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)
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from numpy._core.shape_base import (
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atleast_1d,
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atleast_2d,
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atleast_3d,
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block,
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hstack,
|
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stack,
|
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vstack,
|
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unstack,
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)
|
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from ._expired_attrs_2_0 import __expired_attributes__ as __expired_attributes__
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from numpy.lib import (
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scimath as emath,
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)
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from numpy.lib._arraypad_impl import (
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pad,
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)
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from numpy.lib._arraysetops_impl import (
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ediff1d,
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in1d,
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intersect1d,
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isin,
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setdiff1d,
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setxor1d,
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union1d,
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unique,
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unique_all,
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unique_counts,
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unique_inverse,
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unique_values,
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)
|
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from numpy.lib._function_base_impl import (
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select,
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piecewise,
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trim_zeros,
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copy,
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iterable,
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percentile,
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diff,
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gradient,
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angle,
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unwrap,
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sort_complex,
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flip,
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rot90,
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extract,
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place,
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asarray_chkfinite,
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average,
|
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digitize,
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cov,
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corrcoef,
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median,
|
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sinc,
|
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hamming,
|
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hanning,
|
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bartlett,
|
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blackman,
|
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kaiser,
|
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trapezoid,
|
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trapz,
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i0,
|
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meshgrid,
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delete,
|
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insert,
|
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append,
|
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interp,
|
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quantile,
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)
|
|
|
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from numpy._globals import _CopyMode
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|
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from numpy.lib._histograms_impl import (
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histogram_bin_edges,
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histogram,
|
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histogramdd,
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)
|
|
|
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from numpy.lib._index_tricks_impl import (
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ndenumerate,
|
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ndindex,
|
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ravel_multi_index,
|
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unravel_index,
|
|
mgrid,
|
|
ogrid,
|
|
r_,
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c_,
|
|
s_,
|
|
index_exp,
|
|
ix_,
|
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fill_diagonal,
|
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diag_indices,
|
|
diag_indices_from,
|
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)
|
|
|
|
from numpy.lib._nanfunctions_impl import (
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nansum,
|
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nanmax,
|
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nanmin,
|
|
nanargmax,
|
|
nanargmin,
|
|
nanmean,
|
|
nanmedian,
|
|
nanpercentile,
|
|
nanvar,
|
|
nanstd,
|
|
nanprod,
|
|
nancumsum,
|
|
nancumprod,
|
|
nanquantile,
|
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)
|
|
|
|
from numpy.lib._npyio_impl import (
|
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savetxt,
|
|
loadtxt,
|
|
genfromtxt,
|
|
load,
|
|
save,
|
|
savez,
|
|
savez_compressed,
|
|
fromregex,
|
|
)
|
|
|
|
from numpy.lib._polynomial_impl import (
|
|
poly,
|
|
roots,
|
|
polyint,
|
|
polyder,
|
|
polyadd,
|
|
polysub,
|
|
polymul,
|
|
polydiv,
|
|
polyval,
|
|
polyfit,
|
|
)
|
|
|
|
from numpy.lib._shape_base_impl import (
|
|
column_stack,
|
|
row_stack,
|
|
dstack,
|
|
array_split,
|
|
split,
|
|
hsplit,
|
|
vsplit,
|
|
dsplit,
|
|
apply_over_axes,
|
|
expand_dims,
|
|
apply_along_axis,
|
|
kron,
|
|
tile,
|
|
take_along_axis,
|
|
put_along_axis,
|
|
)
|
|
|
|
from numpy.lib._stride_tricks_impl import (
|
|
broadcast_to,
|
|
broadcast_arrays,
|
|
broadcast_shapes,
|
|
)
|
|
|
|
from numpy.lib._twodim_base_impl import (
|
|
diag,
|
|
diagflat,
|
|
eye,
|
|
fliplr,
|
|
flipud,
|
|
tri,
|
|
triu,
|
|
tril,
|
|
vander,
|
|
histogram2d,
|
|
mask_indices,
|
|
tril_indices,
|
|
tril_indices_from,
|
|
triu_indices,
|
|
triu_indices_from,
|
|
)
|
|
|
|
from numpy.lib._type_check_impl import (
|
|
mintypecode,
|
|
real,
|
|
imag,
|
|
iscomplex,
|
|
isreal,
|
|
iscomplexobj,
|
|
isrealobj,
|
|
nan_to_num,
|
|
real_if_close,
|
|
typename,
|
|
common_type,
|
|
)
|
|
|
|
from numpy.lib._ufunclike_impl import (
|
|
fix,
|
|
isposinf,
|
|
isneginf,
|
|
)
|
|
|
|
from numpy.lib._utils_impl import (
|
|
get_include,
|
|
info,
|
|
show_runtime,
|
|
)
|
|
|
|
from numpy.matrixlib import (
|
|
asmatrix,
|
|
bmat,
|
|
)
|
|
|
|
__all__ = [ # noqa: RUF022
|
|
# __numpy_submodules__
|
|
"char", "core", "ctypeslib", "dtypes", "exceptions", "f2py", "fft", "lib", "linalg",
|
|
"ma", "polynomial", "random", "rec", "strings", "test", "testing", "typing",
|
|
|
|
# _core.__all__
|
|
"abs", "acos", "acosh", "asin", "asinh", "atan", "atanh", "atan2", "bitwise_invert",
|
|
"bitwise_left_shift", "bitwise_right_shift", "concat", "pow", "permute_dims",
|
|
"memmap", "sctypeDict", "record", "recarray",
|
|
|
|
# _core.numeric.__all__
|
|
"newaxis", "ndarray", "flatiter", "nditer", "nested_iters", "ufunc", "arange",
|
|
"array", "asarray", "asanyarray", "ascontiguousarray", "asfortranarray", "zeros",
|
|
"count_nonzero", "empty", "broadcast", "dtype", "fromstring", "fromfile",
|
|
"frombuffer", "from_dlpack", "where", "argwhere", "copyto", "concatenate",
|
|
"lexsort", "astype", "can_cast", "promote_types", "min_scalar_type", "result_type",
|
|
"isfortran", "empty_like", "zeros_like", "ones_like", "correlate", "convolve",
|
|
"inner", "dot", "outer", "vdot", "roll", "rollaxis", "moveaxis", "cross",
|
|
"tensordot", "little_endian", "fromiter", "array_equal", "array_equiv", "indices",
|
|
"fromfunction", "isclose", "isscalar", "binary_repr", "base_repr", "ones",
|
|
"identity", "allclose", "putmask", "flatnonzero", "inf", "nan", "False_", "True_",
|
|
"bitwise_not", "full", "full_like", "matmul", "vecdot", "vecmat",
|
|
"shares_memory", "may_share_memory",
|
|
"all", "amax", "amin", "any", "argmax", "argmin", "argpartition", "argsort",
|
|
"around", "choose", "clip", "compress", "cumprod", "cumsum", "cumulative_prod",
|
|
"cumulative_sum", "diagonal", "mean", "max", "min", "matrix_transpose", "ndim",
|
|
"nonzero", "partition", "prod", "ptp", "put", "ravel", "repeat", "reshape",
|
|
"resize", "round", "searchsorted", "shape", "size", "sort", "squeeze", "std", "sum",
|
|
"swapaxes", "take", "trace", "transpose", "var",
|
|
"absolute", "add", "arccos", "arccosh", "arcsin", "arcsinh", "arctan", "arctan2",
|
|
"arctanh", "bitwise_and", "bitwise_or", "bitwise_xor", "cbrt", "ceil", "conj",
|
|
"conjugate", "copysign", "cos", "cosh", "bitwise_count", "deg2rad", "degrees",
|
|
"divide", "divmod", "e", "equal", "euler_gamma", "exp", "exp2", "expm1", "fabs",
|
|
"floor", "floor_divide", "float_power", "fmax", "fmin", "fmod", "frexp",
|
|
"frompyfunc", "gcd", "greater", "greater_equal", "heaviside", "hypot", "invert",
|
|
"isfinite", "isinf", "isnan", "isnat", "lcm", "ldexp", "left_shift", "less",
|
|
"less_equal", "log", "log10", "log1p", "log2", "logaddexp", "logaddexp2",
|
|
"logical_and", "logical_not", "logical_or", "logical_xor", "matvec", "maximum", "minimum",
|
|
"mod", "modf", "multiply", "negative", "nextafter", "not_equal", "pi", "positive",
|
|
"power", "rad2deg", "radians", "reciprocal", "remainder", "right_shift", "rint",
|
|
"sign", "signbit", "sin", "sinh", "spacing", "sqrt", "square", "subtract", "tan",
|
|
"tanh", "true_divide", "trunc", "ScalarType", "typecodes", "issubdtype",
|
|
"datetime_data", "datetime_as_string", "busday_offset", "busday_count", "is_busday",
|
|
"busdaycalendar", "isdtype",
|
|
"complexfloating", "character", "unsignedinteger", "inexact", "generic", "floating",
|
|
"integer", "signedinteger", "number", "flexible", "bool", "float16", "float32",
|
|
"float64", "longdouble", "complex64", "complex128", "clongdouble",
|
|
"bytes_", "str_", "void", "object_", "datetime64", "timedelta64", "int8", "byte",
|
|
"uint8", "ubyte", "int16", "short", "uint16", "ushort", "int32", "intc", "uint32",
|
|
"uintc", "int64", "long", "uint64", "ulong", "longlong", "ulonglong", "intp",
|
|
"uintp", "double", "cdouble", "single", "csingle", "half", "bool_", "int_", "uint",
|
|
"float96", "float128", "complex192", "complex256",
|
|
"array2string", "array_str", "array_repr", "set_printoptions", "get_printoptions",
|
|
"printoptions", "format_float_positional", "format_float_scientific", "require",
|
|
"seterr", "geterr", "setbufsize", "getbufsize", "seterrcall", "geterrcall",
|
|
"errstate",
|
|
# _core.function_base.__all__
|
|
"logspace", "linspace", "geomspace",
|
|
# _core.getlimits.__all__
|
|
"finfo", "iinfo",
|
|
# _core.shape_base.__all__
|
|
"atleast_1d", "atleast_2d", "atleast_3d", "block", "hstack", "stack", "unstack",
|
|
"vstack",
|
|
# _core.einsumfunc.__all__
|
|
"einsum", "einsum_path",
|
|
# matrixlib.__all__
|
|
"matrix", "bmat", "asmatrix",
|
|
# lib._histograms_impl.__all__
|
|
"histogram", "histogramdd", "histogram_bin_edges",
|
|
# lib._nanfunctions_impl.__all__
|
|
"nansum", "nanmax", "nanmin", "nanargmax", "nanargmin", "nanmean", "nanmedian",
|
|
"nanpercentile", "nanvar", "nanstd", "nanprod", "nancumsum", "nancumprod",
|
|
"nanquantile",
|
|
# lib._function_base_impl.__all__
|
|
"select", "piecewise", "trim_zeros", "copy", "iterable", "percentile", "diff",
|
|
"gradient", "angle", "unwrap", "sort_complex", "flip", "rot90", "extract", "place",
|
|
"vectorize", "asarray_chkfinite", "average", "bincount", "digitize", "cov",
|
|
"corrcoef", "median", "sinc", "hamming", "hanning", "bartlett", "blackman",
|
|
"kaiser", "trapezoid", "trapz", "i0", "meshgrid", "delete", "insert", "append",
|
|
"interp", "quantile",
|
|
# lib._twodim_base_impl.__all__
|
|
"diag", "diagflat", "eye", "fliplr", "flipud", "tri", "triu", "tril", "vander",
|
|
"histogram2d", "mask_indices", "tril_indices", "tril_indices_from", "triu_indices",
|
|
"triu_indices_from",
|
|
# lib._shape_base_impl.__all__
|
|
"column_stack", "dstack", "array_split", "split", "hsplit", "vsplit", "dsplit",
|
|
"apply_over_axes", "expand_dims", "apply_along_axis", "kron", "tile",
|
|
"take_along_axis", "put_along_axis", "row_stack",
|
|
# lib._type_check_impl.__all__
|
|
"iscomplexobj", "isrealobj", "imag", "iscomplex", "isreal", "nan_to_num", "real",
|
|
"real_if_close", "typename", "mintypecode", "common_type",
|
|
# lib._arraysetops_impl.__all__
|
|
"ediff1d", "in1d", "intersect1d", "isin", "setdiff1d", "setxor1d", "union1d",
|
|
"unique", "unique_all", "unique_counts", "unique_inverse", "unique_values",
|
|
# lib._ufunclike_impl.__all__
|
|
"fix", "isneginf", "isposinf",
|
|
# lib._arraypad_impl.__all__
|
|
"pad",
|
|
# lib._utils_impl.__all__
|
|
"get_include", "info", "show_runtime",
|
|
# lib._stride_tricks_impl.__all__
|
|
"broadcast_to", "broadcast_arrays", "broadcast_shapes",
|
|
# lib._polynomial_impl.__all__
|
|
"poly", "roots", "polyint", "polyder", "polyadd", "polysub", "polymul", "polydiv",
|
|
"polyval", "poly1d", "polyfit",
|
|
# lib._npyio_impl.__all__
|
|
"savetxt", "loadtxt", "genfromtxt", "load", "save", "savez", "savez_compressed",
|
|
"packbits", "unpackbits", "fromregex",
|
|
# lib._index_tricks_impl.__all__
|
|
"ravel_multi_index", "unravel_index", "mgrid", "ogrid", "r_", "c_", "s_",
|
|
"index_exp", "ix_", "ndenumerate", "ndindex", "fill_diagonal", "diag_indices",
|
|
"diag_indices_from",
|
|
|
|
# __init__.__all__
|
|
"emath", "show_config", "__version__", "__array_namespace_info__",
|
|
] # fmt: skip
|
|
|
|
### Constrained types (for internal use only)
|
|
# Only use these for functions; never as generic type parameter.
|
|
|
|
_AnyStr = TypeVar("_AnyStr", LiteralString, str, bytes)
|
|
_AnyShapeT = TypeVar(
|
|
"_AnyShapeT",
|
|
tuple[()], # 0-d
|
|
tuple[int], # 1-d
|
|
tuple[int, int], # 2-d
|
|
tuple[int, int, int], # 3-d
|
|
tuple[int, int, int, int], # 4-d
|
|
tuple[int, int, int, int, int], # 5-d
|
|
tuple[int, int, int, int, int, int], # 6-d
|
|
tuple[int, int, int, int, int, int, int], # 7-d
|
|
tuple[int, int, int, int, int, int, int, int], # 8-d
|
|
tuple[int, ...], # N-d
|
|
)
|
|
_AnyTD64Item = TypeVar("_AnyTD64Item", dt.timedelta, int, None, dt.timedelta | int | None)
|
|
_AnyDT64Arg = TypeVar("_AnyDT64Arg", dt.datetime, dt.date, None)
|
|
_AnyDT64Item = TypeVar("_AnyDT64Item", dt.datetime, dt.date, int, None, dt.date, int | None)
|
|
_AnyDate = TypeVar("_AnyDate", dt.date, dt.datetime)
|
|
_AnyDateOrTime = TypeVar("_AnyDateOrTime", dt.date, dt.datetime, dt.timedelta)
|
|
|
|
### Type parameters (for internal use only)
|
|
|
|
_T = TypeVar("_T")
|
|
_T_co = TypeVar("_T_co", covariant=True)
|
|
_T_contra = TypeVar("_T_contra", contravariant=True)
|
|
_RealT_co = TypeVar("_RealT_co", covariant=True)
|
|
_ImagT_co = TypeVar("_ImagT_co", covariant=True)
|
|
|
|
_CallableT = TypeVar("_CallableT", bound=Callable[..., object])
|
|
|
|
_DTypeT = TypeVar("_DTypeT", bound=dtype)
|
|
_DTypeT_co = TypeVar("_DTypeT_co", bound=dtype, default=dtype, covariant=True)
|
|
_FlexDTypeT = TypeVar("_FlexDTypeT", bound=dtype[flexible])
|
|
|
|
_ArrayT = TypeVar("_ArrayT", bound=ndarray)
|
|
_ArrayT_co = TypeVar("_ArrayT_co", bound=ndarray, default=ndarray, covariant=True)
|
|
_IntegralArrayT = TypeVar("_IntegralArrayT", bound=NDArray[integer | np.bool | object_])
|
|
_RealArrayT = TypeVar("_RealArrayT", bound=NDArray[floating | integer | timedelta64 | np.bool | object_])
|
|
_NumericArrayT = TypeVar("_NumericArrayT", bound=NDArray[number | timedelta64 | object_])
|
|
|
|
_ShapeT = TypeVar("_ShapeT", bound=_Shape)
|
|
_ShapeT_co = TypeVar("_ShapeT_co", bound=_Shape, default=_AnyShape, covariant=True)
|
|
_1DShapeT = TypeVar("_1DShapeT", bound=_1D)
|
|
_2DShapeT_co = TypeVar("_2DShapeT_co", bound=_2D, default=_2D, covariant=True)
|
|
_1NShapeT = TypeVar("_1NShapeT", bound=tuple[L[1], *tuple[L[1], ...]]) # (1,) | (1, 1) | (1, 1, 1) | ...
|
|
|
|
_ScalarT = TypeVar("_ScalarT", bound=generic)
|
|
_ScalarT_co = TypeVar("_ScalarT_co", bound=generic, default=Any, covariant=True)
|
|
_NumberT = TypeVar("_NumberT", bound=number)
|
|
_InexactT = TypeVar("_InexactT", bound=inexact)
|
|
_RealNumberT = TypeVar("_RealNumberT", bound=floating | integer)
|
|
_FloatingT_co = TypeVar("_FloatingT_co", bound=floating, default=floating, covariant=True)
|
|
_IntegerT = TypeVar("_IntegerT", bound=integer)
|
|
_IntegerT_co = TypeVar("_IntegerT_co", bound=integer, default=integer, covariant=True)
|
|
_NonObjectScalarT = TypeVar("_NonObjectScalarT", bound=np.bool | number | flexible | datetime64 | timedelta64)
|
|
|
|
_NBit = TypeVar("_NBit", bound=NBitBase, default=Any) # pyright: ignore[reportDeprecated]
|
|
_NBit1 = TypeVar("_NBit1", bound=NBitBase, default=Any) # pyright: ignore[reportDeprecated]
|
|
_NBit2 = TypeVar("_NBit2", bound=NBitBase, default=_NBit1) # pyright: ignore[reportDeprecated]
|
|
|
|
_ItemT_co = TypeVar("_ItemT_co", default=Any, covariant=True)
|
|
_BoolItemT = TypeVar("_BoolItemT", bound=builtins.bool)
|
|
_BoolItemT_co = TypeVar("_BoolItemT_co", bound=builtins.bool, default=builtins.bool, covariant=True)
|
|
_NumberItemT_co = TypeVar("_NumberItemT_co", bound=complex, default=int | float | complex, covariant=True)
|
|
_InexactItemT_co = TypeVar("_InexactItemT_co", bound=complex, default=float | complex, covariant=True)
|
|
_FlexibleItemT_co = TypeVar(
|
|
"_FlexibleItemT_co",
|
|
bound=_CharLike_co | tuple[Any, ...],
|
|
default=_CharLike_co | tuple[Any, ...],
|
|
covariant=True,
|
|
)
|
|
_CharacterItemT_co = TypeVar("_CharacterItemT_co", bound=_CharLike_co, default=_CharLike_co, covariant=True)
|
|
_TD64ItemT_co = TypeVar("_TD64ItemT_co", bound=dt.timedelta | int | None, default=dt.timedelta | int | None, covariant=True)
|
|
_DT64ItemT_co = TypeVar("_DT64ItemT_co", bound=dt.date | int | None, default=dt.date | int | None, covariant=True)
|
|
_TD64UnitT = TypeVar("_TD64UnitT", bound=_TD64Unit, default=_TD64Unit)
|
|
_BoolOrIntArrayT = TypeVar("_BoolOrIntArrayT", bound=NDArray[integer | np.bool])
|
|
|
|
### Type Aliases (for internal use only)
|
|
|
|
_Falsy: TypeAlias = L[False, 0] | np.bool[L[False]]
|
|
_Truthy: TypeAlias = L[True, 1] | np.bool[L[True]]
|
|
|
|
_1D: TypeAlias = tuple[int]
|
|
_2D: TypeAlias = tuple[int, int]
|
|
_2Tuple: TypeAlias = tuple[_T, _T]
|
|
|
|
_ArrayUInt_co: TypeAlias = NDArray[unsignedinteger | np.bool]
|
|
_ArrayInt_co: TypeAlias = NDArray[integer | np.bool]
|
|
_ArrayFloat64_co: TypeAlias = NDArray[floating[_64Bit] | float32 | float16 | integer | np.bool]
|
|
_ArrayFloat_co: TypeAlias = NDArray[floating | integer | np.bool]
|
|
_ArrayComplex128_co: TypeAlias = NDArray[number[_64Bit] | number[_32Bit] | float16 | integer | np.bool]
|
|
_ArrayComplex_co: TypeAlias = NDArray[inexact | integer | np.bool]
|
|
_ArrayNumber_co: TypeAlias = NDArray[number | np.bool]
|
|
_ArrayTD64_co: TypeAlias = NDArray[timedelta64 | integer | np.bool]
|
|
|
|
_Float64_co: TypeAlias = float | floating[_64Bit] | float32 | float16 | integer | np.bool
|
|
_Complex64_co: TypeAlias = number[_32Bit] | number[_16Bit] | number[_8Bit] | builtins.bool | np.bool
|
|
_Complex128_co: TypeAlias = complex | number[_64Bit] | _Complex64_co
|
|
|
|
_ToIndex: TypeAlias = SupportsIndex | slice | EllipsisType | _ArrayLikeInt_co | None
|
|
_ToIndices: TypeAlias = _ToIndex | tuple[_ToIndex, ...]
|
|
|
|
_UnsignedIntegerCType: TypeAlias = type[
|
|
ct.c_uint8 | ct.c_uint16 | ct.c_uint32 | ct.c_uint64
|
|
| ct.c_ushort | ct.c_uint | ct.c_ulong | ct.c_ulonglong
|
|
| ct.c_size_t | ct.c_void_p
|
|
] # fmt: skip
|
|
_SignedIntegerCType: TypeAlias = type[
|
|
ct.c_int8 | ct.c_int16 | ct.c_int32 | ct.c_int64
|
|
| ct.c_short | ct.c_int | ct.c_long | ct.c_longlong
|
|
| ct.c_ssize_t
|
|
] # fmt: skip
|
|
_FloatingCType: TypeAlias = type[ct.c_float | ct.c_double | ct.c_longdouble]
|
|
_IntegerCType: TypeAlias = _UnsignedIntegerCType | _SignedIntegerCType
|
|
_NumberCType: TypeAlias = _IntegerCType
|
|
_GenericCType: TypeAlias = _NumberCType | type[ct.c_bool | ct.c_char | ct.py_object[Any]]
|
|
|
|
# some commonly used builtin types that are known to result in a
|
|
# `dtype[object_]`, when their *type* is passed to the `dtype` constructor
|
|
# NOTE: `builtins.object` should not be included here
|
|
_BuiltinObjectLike: TypeAlias = (
|
|
slice | Decimal | Fraction | UUID
|
|
| dt.date | dt.time | dt.timedelta | dt.tzinfo
|
|
| tuple[Any, ...] | list[Any] | set[Any] | frozenset[Any] | dict[Any, Any]
|
|
) # fmt: skip
|
|
|
|
# Introduce an alias for `dtype` to avoid naming conflicts.
|
|
_dtype: TypeAlias = dtype[_ScalarT]
|
|
|
|
_ByteOrderChar: TypeAlias = L["<", ">", "=", "|"]
|
|
# can be anything, is case-insensitive, and only the first character matters
|
|
_ByteOrder: TypeAlias = L[
|
|
"S", # swap the current order (default)
|
|
"<", "L", "little", # little-endian
|
|
">", "B", "big", # big endian
|
|
"=", "N", "native", # native order
|
|
"|", "I", # ignore
|
|
] # fmt: skip
|
|
_DTypeKind: TypeAlias = L[
|
|
"b", # boolean
|
|
"i", # signed integer
|
|
"u", # unsigned integer
|
|
"f", # floating-point
|
|
"c", # complex floating-point
|
|
"m", # timedelta64
|
|
"M", # datetime64
|
|
"O", # python object
|
|
"S", # byte-string (fixed-width)
|
|
"U", # unicode-string (fixed-width)
|
|
"V", # void
|
|
"T", # unicode-string (variable-width)
|
|
]
|
|
_DTypeChar: TypeAlias = L[
|
|
"?", # bool
|
|
"b", # byte
|
|
"B", # ubyte
|
|
"h", # short
|
|
"H", # ushort
|
|
"i", # intc
|
|
"I", # uintc
|
|
"l", # long
|
|
"L", # ulong
|
|
"q", # longlong
|
|
"Q", # ulonglong
|
|
"e", # half
|
|
"f", # single
|
|
"d", # double
|
|
"g", # longdouble
|
|
"F", # csingle
|
|
"D", # cdouble
|
|
"G", # clongdouble
|
|
"O", # object
|
|
"S", # bytes_ (S0)
|
|
"a", # bytes_ (deprecated)
|
|
"U", # str_
|
|
"V", # void
|
|
"M", # datetime64
|
|
"m", # timedelta64
|
|
"c", # bytes_ (S1)
|
|
"T", # StringDType
|
|
]
|
|
_DTypeNum: TypeAlias = L[
|
|
0, # bool
|
|
1, # byte
|
|
2, # ubyte
|
|
3, # short
|
|
4, # ushort
|
|
5, # intc
|
|
6, # uintc
|
|
7, # long
|
|
8, # ulong
|
|
9, # longlong
|
|
10, # ulonglong
|
|
23, # half
|
|
11, # single
|
|
12, # double
|
|
13, # longdouble
|
|
14, # csingle
|
|
15, # cdouble
|
|
16, # clongdouble
|
|
17, # object
|
|
18, # bytes_
|
|
19, # str_
|
|
20, # void
|
|
21, # datetime64
|
|
22, # timedelta64
|
|
25, # no type
|
|
256, # user-defined
|
|
2056, # StringDType
|
|
]
|
|
_DTypeBuiltinKind: TypeAlias = L[0, 1, 2]
|
|
|
|
_ArrayAPIVersion: TypeAlias = L["2021.12", "2022.12", "2023.12", "2024.12"]
|
|
|
|
_CastingKind: TypeAlias = L["no", "equiv", "safe", "same_kind", "unsafe"]
|
|
|
|
_OrderKACF: TypeAlias = L["K", "A", "C", "F"] | None
|
|
_OrderACF: TypeAlias = L["A", "C", "F"] | None
|
|
_OrderCF: TypeAlias = L["C", "F"] | None
|
|
|
|
_ModeKind: TypeAlias = L["raise", "wrap", "clip"]
|
|
_PartitionKind: TypeAlias = L["introselect"]
|
|
# in practice, only the first case-insensitive character is considered (so e.g.
|
|
# "QuantumSort3000" will be interpreted as quicksort).
|
|
_SortKind: TypeAlias = L[
|
|
"Q", "quick", "quicksort",
|
|
"M", "merge", "mergesort",
|
|
"H", "heap", "heapsort",
|
|
"S", "stable", "stablesort",
|
|
]
|
|
_SortSide: TypeAlias = L["left", "right"]
|
|
|
|
_ConvertibleToInt: TypeAlias = SupportsInt | SupportsIndex | _CharLike_co
|
|
_ConvertibleToFloat: TypeAlias = SupportsFloat | SupportsIndex | _CharLike_co
|
|
_ConvertibleToComplex: TypeAlias = SupportsComplex | SupportsFloat | SupportsIndex | _CharLike_co
|
|
_ConvertibleToTD64: TypeAlias = dt.timedelta | int | _CharLike_co | character | number | timedelta64 | np.bool | None
|
|
_ConvertibleToDT64: TypeAlias = dt.date | int | _CharLike_co | character | number | datetime64 | np.bool | None
|
|
|
|
_NDIterFlagsKind: TypeAlias = L[
|
|
"buffered",
|
|
"c_index",
|
|
"copy_if_overlap",
|
|
"common_dtype",
|
|
"delay_bufalloc",
|
|
"external_loop",
|
|
"f_index",
|
|
"grow_inner", "growinner",
|
|
"multi_index",
|
|
"ranged",
|
|
"refs_ok",
|
|
"reduce_ok",
|
|
"zerosize_ok",
|
|
]
|
|
_NDIterFlagsOp: TypeAlias = L[
|
|
"aligned",
|
|
"allocate",
|
|
"arraymask",
|
|
"copy",
|
|
"config",
|
|
"nbo",
|
|
"no_subtype",
|
|
"no_broadcast",
|
|
"overlap_assume_elementwise",
|
|
"readonly",
|
|
"readwrite",
|
|
"updateifcopy",
|
|
"virtual",
|
|
"writeonly",
|
|
"writemasked"
|
|
]
|
|
|
|
_MemMapModeKind: TypeAlias = L[
|
|
"readonly", "r",
|
|
"copyonwrite", "c",
|
|
"readwrite", "r+",
|
|
"write", "w+",
|
|
]
|
|
|
|
_DT64Date: TypeAlias = _HasDateAttributes | L["TODAY", "today", b"TODAY", b"today"]
|
|
_DT64Now: TypeAlias = L["NOW", "now", b"NOW", b"now"]
|
|
_NaTValue: TypeAlias = L["NAT", "NaT", "nat", b"NAT", b"NaT", b"nat"]
|
|
|
|
_MonthUnit: TypeAlias = L["Y", "M", b"Y", b"M"]
|
|
_DayUnit: TypeAlias = L["W", "D", b"W", b"D"]
|
|
_DateUnit: TypeAlias = L[_MonthUnit, _DayUnit]
|
|
_NativeTimeUnit: TypeAlias = L["h", "m", "s", "ms", "us", "μs", b"h", b"m", b"s", b"ms", b"us"]
|
|
_IntTimeUnit: TypeAlias = L["ns", "ps", "fs", "as", b"ns", b"ps", b"fs", b"as"]
|
|
_TimeUnit: TypeAlias = L[_NativeTimeUnit, _IntTimeUnit]
|
|
_NativeTD64Unit: TypeAlias = L[_DayUnit, _NativeTimeUnit]
|
|
_IntTD64Unit: TypeAlias = L[_MonthUnit, _IntTimeUnit]
|
|
_TD64Unit: TypeAlias = L[_DateUnit, _TimeUnit]
|
|
_TimeUnitSpec: TypeAlias = _TD64UnitT | tuple[_TD64UnitT, SupportsIndex]
|
|
|
|
### TypedDict's (for internal use only)
|
|
|
|
@type_check_only
|
|
class _FormerAttrsDict(TypedDict):
|
|
object: LiteralString
|
|
float: LiteralString
|
|
complex: LiteralString
|
|
str: LiteralString
|
|
int: LiteralString
|
|
|
|
### Protocols (for internal use only)
|
|
|
|
@type_check_only
|
|
class _SupportsFileMethods(SupportsFlush, Protocol):
|
|
# Protocol for representing file-like-objects accepted by `ndarray.tofile` and `fromfile`
|
|
def fileno(self) -> SupportsIndex: ...
|
|
def tell(self) -> SupportsIndex: ...
|
|
def seek(self, offset: int, whence: int, /) -> object: ...
|
|
|
|
@type_check_only
|
|
class _SupportsFileMethodsRW(SupportsWrite[bytes], _SupportsFileMethods, Protocol): ...
|
|
|
|
@type_check_only
|
|
class _SupportsItem(Protocol[_T_co]):
|
|
def item(self, /) -> _T_co: ...
|
|
|
|
@type_check_only
|
|
class _SupportsDLPack(Protocol[_T_contra]):
|
|
def __dlpack__(self, /, *, stream: _T_contra | None = None) -> CapsuleType: ...
|
|
|
|
@type_check_only
|
|
class _HasDType(Protocol[_T_co]):
|
|
@property
|
|
def dtype(self, /) -> _T_co: ...
|
|
|
|
@type_check_only
|
|
class _HasRealAndImag(Protocol[_RealT_co, _ImagT_co]):
|
|
@property
|
|
def real(self, /) -> _RealT_co: ...
|
|
@property
|
|
def imag(self, /) -> _ImagT_co: ...
|
|
|
|
@type_check_only
|
|
class _HasTypeWithRealAndImag(Protocol[_RealT_co, _ImagT_co]):
|
|
@property
|
|
def type(self, /) -> type[_HasRealAndImag[_RealT_co, _ImagT_co]]: ...
|
|
|
|
@type_check_only
|
|
class _HasDTypeWithRealAndImag(Protocol[_RealT_co, _ImagT_co]):
|
|
@property
|
|
def dtype(self, /) -> _HasTypeWithRealAndImag[_RealT_co, _ImagT_co]: ...
|
|
|
|
@type_check_only
|
|
class _HasDateAttributes(Protocol):
|
|
# The `datetime64` constructors requires an object with the three attributes below,
|
|
# and thus supports datetime duck typing
|
|
@property
|
|
def day(self) -> int: ...
|
|
@property
|
|
def month(self) -> int: ...
|
|
@property
|
|
def year(self) -> int: ...
|
|
|
|
### Mixins (for internal use only)
|
|
|
|
@type_check_only
|
|
class _RealMixin:
|
|
@property
|
|
def real(self) -> Self: ...
|
|
@property
|
|
def imag(self) -> Self: ...
|
|
|
|
@type_check_only
|
|
class _RoundMixin:
|
|
@overload
|
|
def __round__(self, /, ndigits: None = None) -> int: ...
|
|
@overload
|
|
def __round__(self, /, ndigits: SupportsIndex) -> Self: ...
|
|
|
|
@type_check_only
|
|
class _IntegralMixin(_RealMixin):
|
|
@property
|
|
def numerator(self) -> Self: ...
|
|
@property
|
|
def denominator(self) -> L[1]: ...
|
|
|
|
def is_integer(self, /) -> L[True]: ...
|
|
|
|
### Public API
|
|
|
|
__version__: Final[LiteralString] = ...
|
|
|
|
e: Final[float] = ...
|
|
euler_gamma: Final[float] = ...
|
|
pi: Final[float] = ...
|
|
inf: Final[float] = ...
|
|
nan: Final[float] = ...
|
|
little_endian: Final[builtins.bool] = ...
|
|
False_: Final[np.bool[L[False]]] = ...
|
|
True_: Final[np.bool[L[True]]] = ...
|
|
newaxis: Final[None] = None
|
|
|
|
# not in __all__
|
|
__NUMPY_SETUP__: Final[L[False]] = False
|
|
__numpy_submodules__: Final[set[LiteralString]] = ...
|
|
__former_attrs__: Final[_FormerAttrsDict] = ...
|
|
__future_scalars__: Final[set[L["bytes", "str", "object"]]] = ...
|
|
__array_api_version__: Final[L["2024.12"]] = "2024.12"
|
|
test: Final[PytestTester] = ...
|
|
|
|
@type_check_only
|
|
class _DTypeMeta(type):
|
|
@property
|
|
def type(cls, /) -> type[generic] | None: ...
|
|
@property
|
|
def _abstract(cls, /) -> bool: ...
|
|
@property
|
|
def _is_numeric(cls, /) -> bool: ...
|
|
@property
|
|
def _parametric(cls, /) -> bool: ...
|
|
@property
|
|
def _legacy(cls, /) -> bool: ...
|
|
|
|
@final
|
|
class dtype(Generic[_ScalarT_co], metaclass=_DTypeMeta):
|
|
names: tuple[builtins.str, ...] | None
|
|
def __hash__(self) -> int: ...
|
|
|
|
# `None` results in the default dtype
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[float64] | None,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...
|
|
) -> dtype[float64]: ...
|
|
|
|
# Overload for `dtype` instances, scalar types, and instances that have a
|
|
# `dtype: dtype[_ScalarT]` attribute
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _DTypeLike[_ScalarT],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[_ScalarT]: ...
|
|
|
|
# Builtin types
|
|
#
|
|
# NOTE: Typecheckers act as if `bool <: int <: float <: complex <: object`,
|
|
# even though at runtime `int`, `float`, and `complex` aren't subtypes..
|
|
# This makes it impossible to express e.g. "a float that isn't an int",
|
|
# since type checkers treat `_: float` like `_: float | int`.
|
|
#
|
|
# For more details, see:
|
|
# - https://github.com/numpy/numpy/issues/27032#issuecomment-2278958251
|
|
# - https://typing.readthedocs.io/en/latest/spec/special-types.html#special-cases-for-float-and-complex
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[builtins.bool | np.bool],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[str, Any] = ...,
|
|
) -> dtype[np.bool]: ...
|
|
# NOTE: `_: type[int]` also accepts `type[int | bool]`
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[int | int_ | np.bool],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[str, Any] = ...,
|
|
) -> dtype[int_ | np.bool]: ...
|
|
# NOTE: `_: type[float]` also accepts `type[float | int | bool]`
|
|
# NOTE: `float64` inherits from `float` at runtime; but this isn't
|
|
# reflected in these stubs. So an explicit `float64` is required here.
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[float | float64 | int_ | np.bool] | None,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[str, Any] = ...,
|
|
) -> dtype[float64 | int_ | np.bool]: ...
|
|
# NOTE: `_: type[complex]` also accepts `type[complex | float | int | bool]`
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[complex | complex128 | float64 | int_ | np.bool],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[str, Any] = ...,
|
|
) -> dtype[complex128 | float64 | int_ | np.bool]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[bytes], # also includes `type[bytes_]`
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[str, Any] = ...,
|
|
) -> dtype[bytes_]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[str], # also includes `type[str_]`
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[str, Any] = ...,
|
|
) -> dtype[str_]: ...
|
|
# NOTE: These `memoryview` overloads assume PEP 688, which requires mypy to
|
|
# be run with the (undocumented) `--disable-memoryview-promotion` flag,
|
|
# This will be the default in a future mypy release, see:
|
|
# https://github.com/python/mypy/issues/15313
|
|
# Pyright / Pylance requires setting `disableBytesTypePromotions=true`,
|
|
# which is the default in strict mode
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[memoryview | void],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[str, Any] = ...,
|
|
) -> dtype[void]: ...
|
|
# NOTE: `_: type[object]` would also accept e.g. `type[object | complex]`,
|
|
# and is therefore not included here
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[_BuiltinObjectLike | object_],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[str, Any] = ...,
|
|
) -> dtype[object_]: ...
|
|
|
|
# Unions of builtins.
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[bytes | str],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[str, Any] = ...,
|
|
) -> dtype[character]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[bytes | str | memoryview],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[str, Any] = ...,
|
|
) -> dtype[flexible]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[complex | bytes | str | memoryview | _BuiltinObjectLike],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[str, Any] = ...,
|
|
) -> dtype[np.bool | int_ | float64 | complex128 | flexible | object_]: ...
|
|
|
|
# `unsignedinteger` string-based representations and ctypes
|
|
@overload
|
|
def __new__(cls, dtype: _UInt8Codes | type[ct.c_uint8], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[uint8]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UInt16Codes | type[ct.c_uint16], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[uint16]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UInt32Codes | type[ct.c_uint32], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[uint32]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UInt64Codes | type[ct.c_uint64], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[uint64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UByteCodes | type[ct.c_ubyte], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[ubyte]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UShortCodes | type[ct.c_ushort], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[ushort]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UIntCCodes | type[ct.c_uint], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[uintc]: ...
|
|
# NOTE: We're assuming here that `uint_ptr_t == size_t`,
|
|
# an assumption that does not hold in rare cases (same for `ssize_t`)
|
|
@overload
|
|
def __new__(cls, dtype: _UIntPCodes | type[ct.c_void_p] | type[ct.c_size_t], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[uintp]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _ULongCodes | type[ct.c_ulong], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[ulong]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _ULongLongCodes | type[ct.c_ulonglong], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[ulonglong]: ...
|
|
|
|
# `signedinteger` string-based representations and ctypes
|
|
@overload
|
|
def __new__(cls, dtype: _Int8Codes | type[ct.c_int8], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[int8]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Int16Codes | type[ct.c_int16], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[int16]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Int32Codes | type[ct.c_int32], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[int32]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Int64Codes | type[ct.c_int64], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[int64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _ByteCodes | type[ct.c_byte], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[byte]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _ShortCodes | type[ct.c_short], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[short]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _IntCCodes | type[ct.c_int], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[intc]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _IntPCodes | type[ct.c_ssize_t], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[intp]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _LongCodes | type[ct.c_long], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[long]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _LongLongCodes | type[ct.c_longlong], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[longlong]: ...
|
|
|
|
# `floating` string-based representations and ctypes
|
|
@overload
|
|
def __new__(cls, dtype: _Float16Codes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[float16]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Float32Codes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[float32]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Float64Codes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[float64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _HalfCodes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[half]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _SingleCodes | type[ct.c_float], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[single]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _DoubleCodes | type[ct.c_double], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[double]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _LongDoubleCodes | type[ct.c_longdouble], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[longdouble]: ...
|
|
|
|
# `complexfloating` string-based representations
|
|
@overload
|
|
def __new__(cls, dtype: _Complex64Codes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[complex64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Complex128Codes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[complex128]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _CSingleCodes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[csingle]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _CDoubleCodes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[cdouble]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _CLongDoubleCodes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[clongdouble]: ...
|
|
|
|
# Miscellaneous string-based representations and ctypes
|
|
@overload
|
|
def __new__(cls, dtype: _BoolCodes | type[ct.c_bool], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[np.bool]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _TD64Codes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[timedelta64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _DT64Codes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[datetime64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _StrCodes, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[str_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _BytesCodes | type[ct.c_char], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[bytes_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _VoidCodes | _VoidDTypeLike, align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[void]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _ObjectCodes | type[ct.py_object[Any]], align: builtins.bool = ..., copy: builtins.bool = ..., metadata: dict[builtins.str, Any] = ...) -> dtype[object_]: ...
|
|
|
|
# `StringDType` requires special treatment because it has no scalar type
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: dtypes.StringDType | _StringCodes,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...
|
|
) -> dtypes.StringDType: ...
|
|
|
|
# Combined char-codes and ctypes, analogous to the scalar-type hierarchy
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _UnsignedIntegerCodes | _UnsignedIntegerCType,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[unsignedinteger]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _SignedIntegerCodes | _SignedIntegerCType,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[signedinteger]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _IntegerCodes | _IntegerCType,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[integer]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _FloatingCodes | _FloatingCType,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[floating]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _ComplexFloatingCodes,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[complexfloating]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _InexactCodes | _FloatingCType,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[inexact]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _NumberCodes | _NumberCType,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[number]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _CharacterCodes | type[ct.c_char],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[character]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _FlexibleCodes | type[ct.c_char],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[flexible]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _GenericCodes | _GenericCType,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[generic]: ...
|
|
|
|
# Handle strings that can't be expressed as literals; i.e. "S1", "S2", ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: builtins.str,
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype: ...
|
|
|
|
# Catch-all overload for object-likes
|
|
# NOTE: `object_ | Any` is *not* equivalent to `Any` -- it describes some
|
|
# (static) type `T` s.t. `object_ <: T <: builtins.object` (`<:` denotes
|
|
# the subtyping relation, the (gradual) typing analogue of `issubclass()`).
|
|
# https://typing.readthedocs.io/en/latest/spec/concepts.html#union-types
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[object],
|
|
align: builtins.bool = ...,
|
|
copy: builtins.bool = ...,
|
|
metadata: dict[builtins.str, Any] = ...,
|
|
) -> dtype[object_ | Any]: ...
|
|
|
|
def __class_getitem__(cls, item: Any, /) -> GenericAlias: ...
|
|
|
|
@overload
|
|
def __getitem__(self: dtype[void], key: list[builtins.str], /) -> dtype[void]: ...
|
|
@overload
|
|
def __getitem__(self: dtype[void], key: builtins.str | SupportsIndex, /) -> dtype: ...
|
|
|
|
# NOTE: In the future 1-based multiplications will also yield `flexible` dtypes
|
|
@overload
|
|
def __mul__(self: _DTypeT, value: L[1], /) -> _DTypeT: ...
|
|
@overload
|
|
def __mul__(self: _FlexDTypeT, value: SupportsIndex, /) -> _FlexDTypeT: ...
|
|
@overload
|
|
def __mul__(self, value: SupportsIndex, /) -> dtype[void]: ...
|
|
|
|
# NOTE: `__rmul__` seems to be broken when used in combination with
|
|
# literals as of mypy 0.902. Set the return-type to `dtype` for
|
|
# now for non-flexible dtypes.
|
|
@overload
|
|
def __rmul__(self: _FlexDTypeT, value: SupportsIndex, /) -> _FlexDTypeT: ...
|
|
@overload
|
|
def __rmul__(self, value: SupportsIndex, /) -> dtype: ...
|
|
|
|
def __gt__(self, other: DTypeLike, /) -> builtins.bool: ...
|
|
def __ge__(self, other: DTypeLike, /) -> builtins.bool: ...
|
|
def __lt__(self, other: DTypeLike, /) -> builtins.bool: ...
|
|
def __le__(self, other: DTypeLike, /) -> builtins.bool: ...
|
|
|
|
# Explicitly defined `__eq__` and `__ne__` to get around mypy's
|
|
# `strict_equality` option; even though their signatures are
|
|
# identical to their `object`-based counterpart
|
|
def __eq__(self, other: Any, /) -> builtins.bool: ...
|
|
def __ne__(self, other: Any, /) -> builtins.bool: ...
|
|
|
|
@property
|
|
def alignment(self) -> int: ...
|
|
@property
|
|
def base(self) -> dtype: ...
|
|
@property
|
|
def byteorder(self) -> _ByteOrderChar: ...
|
|
@property
|
|
def char(self) -> _DTypeChar: ...
|
|
@property
|
|
def descr(self) -> list[tuple[LiteralString, LiteralString] | tuple[LiteralString, LiteralString, _Shape]]: ...
|
|
@property
|
|
def fields(self,) -> MappingProxyType[LiteralString, tuple[dtype, int] | tuple[dtype, int, Any]] | None: ...
|
|
@property
|
|
def flags(self) -> int: ...
|
|
@property
|
|
def hasobject(self) -> builtins.bool: ...
|
|
@property
|
|
def isbuiltin(self) -> _DTypeBuiltinKind: ...
|
|
@property
|
|
def isnative(self) -> builtins.bool: ...
|
|
@property
|
|
def isalignedstruct(self) -> builtins.bool: ...
|
|
@property
|
|
def itemsize(self) -> int: ...
|
|
@property
|
|
def kind(self) -> _DTypeKind: ...
|
|
@property
|
|
def metadata(self) -> MappingProxyType[builtins.str, Any] | None: ...
|
|
@property
|
|
def name(self) -> LiteralString: ...
|
|
@property
|
|
def num(self) -> _DTypeNum: ...
|
|
@property
|
|
def shape(self) -> _AnyShape: ...
|
|
@property
|
|
def ndim(self) -> int: ...
|
|
@property
|
|
def subdtype(self) -> tuple[dtype, _AnyShape] | None: ...
|
|
def newbyteorder(self, new_order: _ByteOrder = ..., /) -> Self: ...
|
|
@property
|
|
def str(self) -> LiteralString: ...
|
|
@property
|
|
def type(self) -> type[_ScalarT_co]: ...
|
|
|
|
@final
|
|
class flatiter(Generic[_ArrayT_co]):
|
|
__hash__: ClassVar[None]
|
|
@property
|
|
def base(self) -> _ArrayT_co: ...
|
|
@property
|
|
def coords(self) -> _Shape: ...
|
|
@property
|
|
def index(self) -> int: ...
|
|
def copy(self) -> _ArrayT_co: ...
|
|
def __iter__(self) -> Self: ...
|
|
def __next__(self: flatiter[NDArray[_ScalarT]]) -> _ScalarT: ...
|
|
def __len__(self) -> int: ...
|
|
@overload
|
|
def __getitem__(
|
|
self: flatiter[NDArray[_ScalarT]],
|
|
key: int | integer | tuple[int | integer],
|
|
) -> _ScalarT: ...
|
|
@overload
|
|
def __getitem__(
|
|
self,
|
|
key: _ArrayLikeInt | slice | EllipsisType | tuple[_ArrayLikeInt | slice | EllipsisType],
|
|
) -> _ArrayT_co: ...
|
|
# TODO: `__setitem__` operates via `unsafe` casting rules, and can
|
|
# thus accept any type accepted by the relevant underlying `np.generic`
|
|
# constructor.
|
|
# This means that `value` must in reality be a supertype of `npt.ArrayLike`.
|
|
def __setitem__(
|
|
self,
|
|
key: _ArrayLikeInt | slice | EllipsisType | tuple[_ArrayLikeInt | slice | EllipsisType],
|
|
value: Any,
|
|
) -> None: ...
|
|
@overload
|
|
def __array__(self: flatiter[ndarray[_1DShapeT, _DTypeT]], dtype: None = ..., /) -> ndarray[_1DShapeT, _DTypeT]: ...
|
|
@overload
|
|
def __array__(self: flatiter[ndarray[_1DShapeT, Any]], dtype: _DTypeT, /) -> ndarray[_1DShapeT, _DTypeT]: ...
|
|
@overload
|
|
def __array__(self: flatiter[ndarray[Any, _DTypeT]], dtype: None = ..., /) -> ndarray[_AnyShape, _DTypeT]: ...
|
|
@overload
|
|
def __array__(self, dtype: _DTypeT, /) -> ndarray[_AnyShape, _DTypeT]: ...
|
|
|
|
@type_check_only
|
|
class _ArrayOrScalarCommon:
|
|
@property
|
|
def real(self, /) -> Any: ...
|
|
@property
|
|
def imag(self, /) -> Any: ...
|
|
@property
|
|
def T(self) -> Self: ...
|
|
@property
|
|
def mT(self) -> Self: ...
|
|
@property
|
|
def data(self) -> memoryview: ...
|
|
@property
|
|
def flags(self) -> flagsobj: ...
|
|
@property
|
|
def itemsize(self) -> int: ...
|
|
@property
|
|
def nbytes(self) -> int: ...
|
|
@property
|
|
def device(self) -> L["cpu"]: ...
|
|
|
|
def __bool__(self, /) -> builtins.bool: ...
|
|
def __int__(self, /) -> int: ...
|
|
def __float__(self, /) -> float: ...
|
|
def __copy__(self) -> Self: ...
|
|
def __deepcopy__(self, memo: dict[int, Any] | None, /) -> Self: ...
|
|
|
|
# TODO: How to deal with the non-commutative nature of `==` and `!=`?
|
|
# xref numpy/numpy#17368
|
|
def __eq__(self, other: Any, /) -> Any: ...
|
|
def __ne__(self, other: Any, /) -> Any: ...
|
|
|
|
def copy(self, order: _OrderKACF = ...) -> Self: ...
|
|
def dump(self, file: StrOrBytesPath | SupportsWrite[bytes]) -> None: ...
|
|
def dumps(self) -> bytes: ...
|
|
def tobytes(self, order: _OrderKACF = ...) -> bytes: ...
|
|
def tofile(self, fid: StrOrBytesPath | _SupportsFileMethods, sep: str = ..., format: str = ...) -> None: ...
|
|
# generics and 0d arrays return builtin scalars
|
|
def tolist(self) -> Any: ...
|
|
def to_device(self, device: L["cpu"], /, *, stream: int | Any | None = ...) -> Self: ...
|
|
|
|
@property
|
|
def __array_interface__(self) -> dict[str, Any]: ...
|
|
@property
|
|
def __array_priority__(self) -> float: ...
|
|
@property
|
|
def __array_struct__(self) -> CapsuleType: ... # builtins.PyCapsule
|
|
def __array_namespace__(self, /, *, api_version: _ArrayAPIVersion | None = None) -> ModuleType: ...
|
|
def __setstate__(self, state: tuple[
|
|
SupportsIndex, # version
|
|
_ShapeLike, # Shape
|
|
_DTypeT_co, # DType
|
|
np.bool, # F-continuous
|
|
bytes | list[Any], # Data
|
|
], /) -> None: ...
|
|
|
|
def conj(self) -> Self: ...
|
|
def conjugate(self) -> Self: ...
|
|
|
|
def argsort(
|
|
self,
|
|
axis: SupportsIndex | None = ...,
|
|
kind: _SortKind | None = ...,
|
|
order: str | Sequence[str] | None = ...,
|
|
*,
|
|
stable: bool | None = ...,
|
|
) -> NDArray[Any]: ...
|
|
|
|
@overload # axis=None (default), out=None (default), keepdims=False (default)
|
|
def argmax(self, /, axis: None = None, out: None = None, *, keepdims: L[False] = False) -> intp: ...
|
|
@overload # axis=index, out=None (default)
|
|
def argmax(self, /, axis: SupportsIndex, out: None = None, *, keepdims: builtins.bool = False) -> Any: ...
|
|
@overload # axis=index, out=ndarray
|
|
def argmax(self, /, axis: SupportsIndex | None, out: _BoolOrIntArrayT, *, keepdims: builtins.bool = False) -> _BoolOrIntArrayT: ...
|
|
@overload
|
|
def argmax(self, /, axis: SupportsIndex | None = None, *, out: _BoolOrIntArrayT, keepdims: builtins.bool = False) -> _BoolOrIntArrayT: ...
|
|
|
|
@overload # axis=None (default), out=None (default), keepdims=False (default)
|
|
def argmin(self, /, axis: None = None, out: None = None, *, keepdims: L[False] = False) -> intp: ...
|
|
@overload # axis=index, out=None (default)
|
|
def argmin(self, /, axis: SupportsIndex, out: None = None, *, keepdims: builtins.bool = False) -> Any: ...
|
|
@overload # axis=index, out=ndarray
|
|
def argmin(self, /, axis: SupportsIndex | None, out: _BoolOrIntArrayT, *, keepdims: builtins.bool = False) -> _BoolOrIntArrayT: ...
|
|
@overload
|
|
def argmin(self, /, axis: SupportsIndex | None = None, *, out: _BoolOrIntArrayT, keepdims: builtins.bool = False) -> _BoolOrIntArrayT: ...
|
|
|
|
@overload # out=None (default)
|
|
def round(self, /, decimals: SupportsIndex = 0, out: None = None) -> Self: ...
|
|
@overload # out=ndarray
|
|
def round(self, /, decimals: SupportsIndex, out: _ArrayT) -> _ArrayT: ...
|
|
@overload
|
|
def round(self, /, decimals: SupportsIndex = 0, *, out: _ArrayT) -> _ArrayT: ...
|
|
|
|
@overload # out=None (default)
|
|
def choose(self, /, choices: ArrayLike, out: None = None, mode: _ModeKind = "raise") -> NDArray[Any]: ...
|
|
@overload # out=ndarray
|
|
def choose(self, /, choices: ArrayLike, out: _ArrayT, mode: _ModeKind = "raise") -> _ArrayT: ...
|
|
|
|
# TODO: Annotate kwargs with an unpacked `TypedDict`
|
|
@overload # out: None (default)
|
|
def clip(self, /, min: ArrayLike, max: ArrayLike | None = None, out: None = None, **kwargs: Any) -> NDArray[Any]: ...
|
|
@overload
|
|
def clip(self, /, min: None, max: ArrayLike, out: None = None, **kwargs: Any) -> NDArray[Any]: ...
|
|
@overload
|
|
def clip(self, /, min: None = None, *, max: ArrayLike, out: None = None, **kwargs: Any) -> NDArray[Any]: ...
|
|
@overload # out: ndarray
|
|
def clip(self, /, min: ArrayLike, max: ArrayLike | None, out: _ArrayT, **kwargs: Any) -> _ArrayT: ...
|
|
@overload
|
|
def clip(self, /, min: ArrayLike, max: ArrayLike | None = None, *, out: _ArrayT, **kwargs: Any) -> _ArrayT: ...
|
|
@overload
|
|
def clip(self, /, min: None, max: ArrayLike, out: _ArrayT, **kwargs: Any) -> _ArrayT: ...
|
|
@overload
|
|
def clip(self, /, min: None = None, *, max: ArrayLike, out: _ArrayT, **kwargs: Any) -> _ArrayT: ...
|
|
|
|
@overload
|
|
def compress(self, /, condition: _ArrayLikeInt_co, axis: SupportsIndex | None = None, out: None = None) -> NDArray[Any]: ...
|
|
@overload
|
|
def compress(self, /, condition: _ArrayLikeInt_co, axis: SupportsIndex | None, out: _ArrayT) -> _ArrayT: ...
|
|
@overload
|
|
def compress(self, /, condition: _ArrayLikeInt_co, axis: SupportsIndex | None = None, *, out: _ArrayT) -> _ArrayT: ...
|
|
|
|
@overload # out: None (default)
|
|
def cumprod(self, /, axis: SupportsIndex | None = None, dtype: DTypeLike | None = None, out: None = None) -> NDArray[Any]: ...
|
|
@overload # out: ndarray
|
|
def cumprod(self, /, axis: SupportsIndex | None, dtype: DTypeLike | None, out: _ArrayT) -> _ArrayT: ...
|
|
@overload
|
|
def cumprod(self, /, axis: SupportsIndex | None = None, dtype: DTypeLike | None = None, *, out: _ArrayT) -> _ArrayT: ...
|
|
|
|
@overload # out: None (default)
|
|
def cumsum(self, /, axis: SupportsIndex | None = None, dtype: DTypeLike | None = None, out: None = None) -> NDArray[Any]: ...
|
|
@overload # out: ndarray
|
|
def cumsum(self, /, axis: SupportsIndex | None, dtype: DTypeLike | None, out: _ArrayT) -> _ArrayT: ...
|
|
@overload
|
|
def cumsum(self, /, axis: SupportsIndex | None = None, dtype: DTypeLike | None = None, *, out: _ArrayT) -> _ArrayT: ...
|
|
|
|
@overload
|
|
def max(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None = None,
|
|
out: None = None,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> Any: ...
|
|
@overload
|
|
def max(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None,
|
|
out: _ArrayT,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
@overload
|
|
def max(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None = None,
|
|
*,
|
|
out: _ArrayT,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
|
|
@overload
|
|
def min(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None = None,
|
|
out: None = None,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> Any: ...
|
|
@overload
|
|
def min(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None,
|
|
out: _ArrayT,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
@overload
|
|
def min(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None = None,
|
|
*,
|
|
out: _ArrayT,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
|
|
@overload
|
|
def sum(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None = None,
|
|
dtype: DTypeLike | None = None,
|
|
out: None = None,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = 0,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> Any: ...
|
|
@overload
|
|
def sum(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None,
|
|
dtype: DTypeLike | None,
|
|
out: _ArrayT,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = 0,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
@overload
|
|
def sum(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None = None,
|
|
dtype: DTypeLike | None = None,
|
|
*,
|
|
out: _ArrayT,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = 0,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
|
|
@overload
|
|
def prod(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None = None,
|
|
dtype: DTypeLike | None = None,
|
|
out: None = None,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = 1,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> Any: ...
|
|
@overload
|
|
def prod(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None,
|
|
dtype: DTypeLike | None,
|
|
out: _ArrayT,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = 1,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
@overload
|
|
def prod(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None = None,
|
|
dtype: DTypeLike | None = None,
|
|
*,
|
|
out: _ArrayT,
|
|
keepdims: builtins.bool = False,
|
|
initial: _NumberLike_co = 1,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
|
|
@overload
|
|
def mean(
|
|
self,
|
|
axis: _ShapeLike | None = None,
|
|
dtype: DTypeLike | None = None,
|
|
out: None = None,
|
|
keepdims: builtins.bool = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> Any: ...
|
|
@overload
|
|
def mean(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None,
|
|
dtype: DTypeLike | None,
|
|
out: _ArrayT,
|
|
keepdims: builtins.bool = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
@overload
|
|
def mean(
|
|
self,
|
|
/,
|
|
axis: _ShapeLike | None = None,
|
|
dtype: DTypeLike | None = None,
|
|
*,
|
|
out: _ArrayT,
|
|
keepdims: builtins.bool = False,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
|
|
@overload
|
|
def std(
|
|
self,
|
|
axis: _ShapeLike | None = None,
|
|
dtype: DTypeLike | None = None,
|
|
out: None = None,
|
|
ddof: float = 0,
|
|
keepdims: builtins.bool = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True,
|
|
mean: _ArrayLikeNumber_co = ...,
|
|
correction: float = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def std(
|
|
self,
|
|
axis: _ShapeLike | None,
|
|
dtype: DTypeLike | None,
|
|
out: _ArrayT,
|
|
ddof: float = 0,
|
|
keepdims: builtins.bool = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True,
|
|
mean: _ArrayLikeNumber_co = ...,
|
|
correction: float = ...,
|
|
) -> _ArrayT: ...
|
|
@overload
|
|
def std(
|
|
self,
|
|
axis: _ShapeLike | None = None,
|
|
dtype: DTypeLike | None = None,
|
|
*,
|
|
out: _ArrayT,
|
|
ddof: float = 0,
|
|
keepdims: builtins.bool = False,
|
|
where: _ArrayLikeBool_co = True,
|
|
mean: _ArrayLikeNumber_co = ...,
|
|
correction: float = ...,
|
|
) -> _ArrayT: ...
|
|
|
|
@overload
|
|
def var(
|
|
self,
|
|
axis: _ShapeLike | None = None,
|
|
dtype: DTypeLike | None = None,
|
|
out: None = None,
|
|
ddof: float = 0,
|
|
keepdims: builtins.bool = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True,
|
|
mean: _ArrayLikeNumber_co = ...,
|
|
correction: float = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def var(
|
|
self,
|
|
axis: _ShapeLike | None,
|
|
dtype: DTypeLike | None,
|
|
out: _ArrayT,
|
|
ddof: float = 0,
|
|
keepdims: builtins.bool = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True,
|
|
mean: _ArrayLikeNumber_co = ...,
|
|
correction: float = ...,
|
|
) -> _ArrayT: ...
|
|
@overload
|
|
def var(
|
|
self,
|
|
axis: _ShapeLike | None = None,
|
|
dtype: DTypeLike | None = None,
|
|
*,
|
|
out: _ArrayT,
|
|
ddof: float = 0,
|
|
keepdims: builtins.bool = False,
|
|
where: _ArrayLikeBool_co = True,
|
|
mean: _ArrayLikeNumber_co = ...,
|
|
correction: float = ...,
|
|
) -> _ArrayT: ...
|
|
|
|
class ndarray(_ArrayOrScalarCommon, Generic[_ShapeT_co, _DTypeT_co]):
|
|
__hash__: ClassVar[None] # type: ignore[assignment] # pyright: ignore[reportIncompatibleMethodOverride]
|
|
@property
|
|
def base(self) -> NDArray[Any] | None: ...
|
|
@property
|
|
def ndim(self) -> int: ...
|
|
@property
|
|
def size(self) -> int: ...
|
|
@property
|
|
def real(self: _HasDTypeWithRealAndImag[_ScalarT, object], /) -> ndarray[_ShapeT_co, dtype[_ScalarT]]: ...
|
|
@real.setter
|
|
def real(self, value: ArrayLike, /) -> None: ...
|
|
@property
|
|
def imag(self: _HasDTypeWithRealAndImag[object, _ScalarT], /) -> ndarray[_ShapeT_co, dtype[_ScalarT]]: ...
|
|
@imag.setter
|
|
def imag(self, value: ArrayLike, /) -> None: ...
|
|
|
|
def __new__(
|
|
cls,
|
|
shape: _ShapeLike,
|
|
dtype: DTypeLike = ...,
|
|
buffer: _SupportsBuffer | None = ...,
|
|
offset: SupportsIndex = ...,
|
|
strides: _ShapeLike | None = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> Self: ...
|
|
|
|
if sys.version_info >= (3, 12):
|
|
def __buffer__(self, flags: int, /) -> memoryview: ...
|
|
|
|
def __class_getitem__(cls, item: Any, /) -> GenericAlias: ...
|
|
|
|
@overload
|
|
def __array__(self, dtype: None = None, /, *, copy: builtins.bool | None = None) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __array__(self, dtype: _DTypeT, /, *, copy: builtins.bool | None = None) -> ndarray[_ShapeT_co, _DTypeT]: ...
|
|
|
|
def __array_ufunc__(
|
|
self,
|
|
ufunc: ufunc,
|
|
method: L["__call__", "reduce", "reduceat", "accumulate", "outer", "at"],
|
|
*inputs: Any,
|
|
**kwargs: Any,
|
|
) -> Any: ...
|
|
|
|
def __array_function__(
|
|
self,
|
|
func: Callable[..., Any],
|
|
types: Iterable[type],
|
|
args: Iterable[Any],
|
|
kwargs: Mapping[str, Any],
|
|
) -> Any: ...
|
|
|
|
# NOTE: In practice any object is accepted by `obj`, but as `__array_finalize__`
|
|
# is a pseudo-abstract method the type has been narrowed down in order to
|
|
# grant subclasses a bit more flexibility
|
|
def __array_finalize__(self, obj: NDArray[Any] | None, /) -> None: ...
|
|
|
|
def __array_wrap__(
|
|
self,
|
|
array: ndarray[_ShapeT, _DTypeT],
|
|
context: tuple[ufunc, tuple[Any, ...], int] | None = ...,
|
|
return_scalar: builtins.bool = ...,
|
|
/,
|
|
) -> ndarray[_ShapeT, _DTypeT]: ...
|
|
|
|
@overload
|
|
def __getitem__(self, key: _ArrayInt_co | tuple[_ArrayInt_co, ...], /) -> ndarray[_AnyShape, _DTypeT_co]: ...
|
|
@overload
|
|
def __getitem__(self, key: SupportsIndex | tuple[SupportsIndex, ...], /) -> Any: ...
|
|
@overload
|
|
def __getitem__(self, key: _ToIndices, /) -> ndarray[_AnyShape, _DTypeT_co]: ...
|
|
@overload
|
|
def __getitem__(self: NDArray[void], key: str, /) -> ndarray[_ShapeT_co, np.dtype]: ...
|
|
@overload
|
|
def __getitem__(self: NDArray[void], key: list[str], /) -> ndarray[_ShapeT_co, _dtype[void]]: ...
|
|
|
|
@overload # flexible | object_ | bool
|
|
def __setitem__(
|
|
self: ndarray[Any, dtype[flexible | object_ | np.bool] | dtypes.StringDType],
|
|
key: _ToIndices,
|
|
value: object,
|
|
/,
|
|
) -> None: ...
|
|
@overload # integer
|
|
def __setitem__(
|
|
self: NDArray[integer],
|
|
key: _ToIndices,
|
|
value: _ConvertibleToInt | _NestedSequence[_ConvertibleToInt] | _ArrayLikeInt_co,
|
|
/,
|
|
) -> None: ...
|
|
@overload # floating
|
|
def __setitem__(
|
|
self: NDArray[floating],
|
|
key: _ToIndices,
|
|
value: _ConvertibleToFloat | _NestedSequence[_ConvertibleToFloat | None] | _ArrayLikeFloat_co | None,
|
|
/,
|
|
) -> None: ...
|
|
@overload # complexfloating
|
|
def __setitem__(
|
|
self: NDArray[complexfloating],
|
|
key: _ToIndices,
|
|
value: _ConvertibleToComplex | _NestedSequence[_ConvertibleToComplex | None] | _ArrayLikeNumber_co | None,
|
|
/,
|
|
) -> None: ...
|
|
@overload # timedelta64
|
|
def __setitem__(
|
|
self: NDArray[timedelta64],
|
|
key: _ToIndices,
|
|
value: _ConvertibleToTD64 | _NestedSequence[_ConvertibleToTD64],
|
|
/,
|
|
) -> None: ...
|
|
@overload # datetime64
|
|
def __setitem__(
|
|
self: NDArray[datetime64],
|
|
key: _ToIndices,
|
|
value: _ConvertibleToDT64 | _NestedSequence[_ConvertibleToDT64],
|
|
/,
|
|
) -> None: ...
|
|
@overload # void
|
|
def __setitem__(self: NDArray[void], key: str | list[str], value: object, /) -> None: ...
|
|
@overload # catch-all
|
|
def __setitem__(self, key: _ToIndices, value: ArrayLike, /) -> None: ...
|
|
|
|
@property
|
|
def ctypes(self) -> _ctypes[int]: ...
|
|
@property
|
|
def shape(self) -> _ShapeT_co: ...
|
|
@shape.setter
|
|
def shape(self, value: _ShapeLike) -> None: ...
|
|
@property
|
|
def strides(self) -> _Shape: ...
|
|
@strides.setter
|
|
def strides(self, value: _ShapeLike) -> None: ...
|
|
def byteswap(self, inplace: builtins.bool = ...) -> Self: ...
|
|
def fill(self, value: Any, /) -> None: ...
|
|
@property
|
|
def flat(self) -> flatiter[Self]: ...
|
|
|
|
@overload # use the same output type as that of the underlying `generic`
|
|
def item(self: NDArray[generic[_T]], i0: SupportsIndex | tuple[SupportsIndex, ...] = ..., /, *args: SupportsIndex) -> _T: ...
|
|
@overload # special casing for `StringDType`, which has no scalar type
|
|
def item(
|
|
self: ndarray[Any, dtypes.StringDType],
|
|
arg0: SupportsIndex | tuple[SupportsIndex, ...] = ...,
|
|
/,
|
|
*args: SupportsIndex,
|
|
) -> str: ...
|
|
|
|
@overload # this first overload prevents mypy from over-eagerly selecting `tuple[()]` in case of `_AnyShape`
|
|
def tolist(self: ndarray[tuple[Never], dtype[generic[_T]]], /) -> Any: ...
|
|
@overload
|
|
def tolist(self: ndarray[tuple[()], dtype[generic[_T]]], /) -> _T: ...
|
|
@overload
|
|
def tolist(self: ndarray[tuple[int], dtype[generic[_T]]], /) -> list[_T]: ...
|
|
@overload
|
|
def tolist(self: ndarray[tuple[int, int], dtype[generic[_T]]], /) -> list[list[_T]]: ...
|
|
@overload
|
|
def tolist(self: ndarray[tuple[int, int, int], dtype[generic[_T]]], /) -> list[list[list[_T]]]: ...
|
|
@overload
|
|
def tolist(self, /) -> Any: ...
|
|
|
|
@overload
|
|
def resize(self, new_shape: _ShapeLike, /, *, refcheck: builtins.bool = ...) -> None: ...
|
|
@overload
|
|
def resize(self, /, *new_shape: SupportsIndex, refcheck: builtins.bool = ...) -> None: ...
|
|
|
|
def setflags(self, write: builtins.bool = ..., align: builtins.bool = ..., uic: builtins.bool = ...) -> None: ...
|
|
|
|
def squeeze(
|
|
self,
|
|
axis: SupportsIndex | tuple[SupportsIndex, ...] | None = ...,
|
|
) -> ndarray[_AnyShape, _DTypeT_co]: ...
|
|
|
|
def swapaxes(
|
|
self,
|
|
axis1: SupportsIndex,
|
|
axis2: SupportsIndex,
|
|
) -> ndarray[_AnyShape, _DTypeT_co]: ...
|
|
|
|
@overload
|
|
def transpose(self, axes: _ShapeLike | None, /) -> Self: ...
|
|
@overload
|
|
def transpose(self, *axes: SupportsIndex) -> Self: ...
|
|
|
|
@overload
|
|
def all(
|
|
self,
|
|
axis: None = None,
|
|
out: None = None,
|
|
keepdims: L[False, 0] = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True
|
|
) -> np.bool: ...
|
|
@overload
|
|
def all(
|
|
self,
|
|
axis: int | tuple[int, ...] | None = None,
|
|
out: None = None,
|
|
keepdims: SupportsIndex = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> np.bool | NDArray[np.bool]: ...
|
|
@overload
|
|
def all(
|
|
self,
|
|
axis: int | tuple[int, ...] | None,
|
|
out: _ArrayT,
|
|
keepdims: SupportsIndex = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
@overload
|
|
def all(
|
|
self,
|
|
axis: int | tuple[int, ...] | None = None,
|
|
*,
|
|
out: _ArrayT,
|
|
keepdims: SupportsIndex = False,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
|
|
@overload
|
|
def any(
|
|
self,
|
|
axis: None = None,
|
|
out: None = None,
|
|
keepdims: L[False, 0] = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True
|
|
) -> np.bool: ...
|
|
@overload
|
|
def any(
|
|
self,
|
|
axis: int | tuple[int, ...] | None = None,
|
|
out: None = None,
|
|
keepdims: SupportsIndex = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> np.bool | NDArray[np.bool]: ...
|
|
@overload
|
|
def any(
|
|
self,
|
|
axis: int | tuple[int, ...] | None,
|
|
out: _ArrayT,
|
|
keepdims: SupportsIndex = False,
|
|
*,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
@overload
|
|
def any(
|
|
self,
|
|
axis: int | tuple[int, ...] | None = None,
|
|
*,
|
|
out: _ArrayT,
|
|
keepdims: SupportsIndex = False,
|
|
where: _ArrayLikeBool_co = True,
|
|
) -> _ArrayT: ...
|
|
|
|
#
|
|
@overload
|
|
def partition(
|
|
self,
|
|
/,
|
|
kth: _ArrayLikeInt,
|
|
axis: SupportsIndex = -1,
|
|
kind: _PartitionKind = "introselect",
|
|
order: None = None,
|
|
) -> None: ...
|
|
@overload
|
|
def partition(
|
|
self: NDArray[void],
|
|
/,
|
|
kth: _ArrayLikeInt,
|
|
axis: SupportsIndex = -1,
|
|
kind: _PartitionKind = "introselect",
|
|
order: str | Sequence[str] | None = None,
|
|
) -> None: ...
|
|
|
|
#
|
|
@overload
|
|
def argpartition(
|
|
self,
|
|
/,
|
|
kth: _ArrayLikeInt,
|
|
axis: SupportsIndex | None = -1,
|
|
kind: _PartitionKind = "introselect",
|
|
order: None = None,
|
|
) -> NDArray[intp]: ...
|
|
@overload
|
|
def argpartition(
|
|
self: NDArray[void],
|
|
/,
|
|
kth: _ArrayLikeInt,
|
|
axis: SupportsIndex | None = -1,
|
|
kind: _PartitionKind = "introselect",
|
|
order: str | Sequence[str] | None = None,
|
|
) -> NDArray[intp]: ...
|
|
|
|
#
|
|
def diagonal(
|
|
self,
|
|
offset: SupportsIndex = ...,
|
|
axis1: SupportsIndex = ...,
|
|
axis2: SupportsIndex = ...,
|
|
) -> ndarray[_AnyShape, _DTypeT_co]: ...
|
|
|
|
# 1D + 1D returns a scalar;
|
|
# all other with at least 1 non-0D array return an ndarray.
|
|
@overload
|
|
def dot(self, b: _ScalarLike_co, out: None = ...) -> NDArray[Any]: ...
|
|
@overload
|
|
def dot(self, b: ArrayLike, out: None = ...) -> Any: ... # type: ignore[misc]
|
|
@overload
|
|
def dot(self, b: ArrayLike, out: _ArrayT) -> _ArrayT: ...
|
|
|
|
# `nonzero()` is deprecated for 0d arrays/generics
|
|
def nonzero(self) -> tuple[NDArray[intp], ...]: ...
|
|
|
|
# `put` is technically available to `generic`,
|
|
# but is pointless as `generic`s are immutable
|
|
def put(self, /, indices: _ArrayLikeInt_co, values: ArrayLike, mode: _ModeKind = "raise") -> None: ...
|
|
|
|
@overload
|
|
def searchsorted( # type: ignore[misc]
|
|
self, # >= 1D array
|
|
v: _ScalarLike_co, # 0D array-like
|
|
side: _SortSide = ...,
|
|
sorter: _ArrayLikeInt_co | None = ...,
|
|
) -> intp: ...
|
|
@overload
|
|
def searchsorted(
|
|
self, # >= 1D array
|
|
v: ArrayLike,
|
|
side: _SortSide = ...,
|
|
sorter: _ArrayLikeInt_co | None = ...,
|
|
) -> NDArray[intp]: ...
|
|
|
|
def sort(
|
|
self,
|
|
axis: SupportsIndex = ...,
|
|
kind: _SortKind | None = ...,
|
|
order: str | Sequence[str] | None = ...,
|
|
*,
|
|
stable: bool | None = ...,
|
|
) -> None: ...
|
|
|
|
@overload
|
|
def trace(
|
|
self, # >= 2D array
|
|
offset: SupportsIndex = ...,
|
|
axis1: SupportsIndex = ...,
|
|
axis2: SupportsIndex = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: None = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def trace(
|
|
self, # >= 2D array
|
|
offset: SupportsIndex = ...,
|
|
axis1: SupportsIndex = ...,
|
|
axis2: SupportsIndex = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: _ArrayT = ...,
|
|
) -> _ArrayT: ...
|
|
|
|
@overload
|
|
def take( # type: ignore[misc]
|
|
self: NDArray[_ScalarT],
|
|
indices: _IntLike_co,
|
|
axis: SupportsIndex | None = ...,
|
|
out: None = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> _ScalarT: ...
|
|
@overload
|
|
def take( # type: ignore[misc]
|
|
self,
|
|
indices: _ArrayLikeInt_co,
|
|
axis: SupportsIndex | None = ...,
|
|
out: None = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> ndarray[_AnyShape, _DTypeT_co]: ...
|
|
@overload
|
|
def take(
|
|
self,
|
|
indices: _ArrayLikeInt_co,
|
|
axis: SupportsIndex | None = ...,
|
|
out: _ArrayT = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> _ArrayT: ...
|
|
|
|
@overload
|
|
def repeat(
|
|
self,
|
|
repeats: _ArrayLikeInt_co,
|
|
axis: None = None,
|
|
) -> ndarray[tuple[int], _DTypeT_co]: ...
|
|
@overload
|
|
def repeat(
|
|
self,
|
|
repeats: _ArrayLikeInt_co,
|
|
axis: SupportsIndex,
|
|
) -> ndarray[_AnyShape, _DTypeT_co]: ...
|
|
|
|
def flatten(self, /, order: _OrderKACF = "C") -> ndarray[tuple[int], _DTypeT_co]: ...
|
|
def ravel(self, /, order: _OrderKACF = "C") -> ndarray[tuple[int], _DTypeT_co]: ...
|
|
|
|
# NOTE: reshape also accepts negative integers, so we can't use integer literals
|
|
@overload # (None)
|
|
def reshape(self, shape: None, /, *, order: _OrderACF = "C", copy: builtins.bool | None = None) -> Self: ...
|
|
@overload # (empty_sequence)
|
|
def reshape( # type: ignore[overload-overlap] # mypy false positive
|
|
self,
|
|
shape: Sequence[Never],
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[tuple[()], _DTypeT_co]: ...
|
|
@overload # (() | (int) | (int, int) | ....) # up to 8-d
|
|
def reshape(
|
|
self,
|
|
shape: _AnyShapeT,
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[_AnyShapeT, _DTypeT_co]: ...
|
|
@overload # (index)
|
|
def reshape(
|
|
self,
|
|
size1: SupportsIndex,
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[tuple[int], _DTypeT_co]: ...
|
|
@overload # (index, index)
|
|
def reshape(
|
|
self,
|
|
size1: SupportsIndex,
|
|
size2: SupportsIndex,
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[tuple[int, int], _DTypeT_co]: ...
|
|
@overload # (index, index, index)
|
|
def reshape(
|
|
self,
|
|
size1: SupportsIndex,
|
|
size2: SupportsIndex,
|
|
size3: SupportsIndex,
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[tuple[int, int, int], _DTypeT_co]: ...
|
|
@overload # (index, index, index, index)
|
|
def reshape(
|
|
self,
|
|
size1: SupportsIndex,
|
|
size2: SupportsIndex,
|
|
size3: SupportsIndex,
|
|
size4: SupportsIndex,
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[tuple[int, int, int, int], _DTypeT_co]: ...
|
|
@overload # (int, *(index, ...))
|
|
def reshape(
|
|
self,
|
|
size0: SupportsIndex,
|
|
/,
|
|
*shape: SupportsIndex,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[_AnyShape, _DTypeT_co]: ...
|
|
@overload # (sequence[index])
|
|
def reshape(
|
|
self,
|
|
shape: Sequence[SupportsIndex],
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[_AnyShape, _DTypeT_co]: ...
|
|
|
|
@overload
|
|
def astype(
|
|
self,
|
|
dtype: _DTypeLike[_ScalarT],
|
|
order: _OrderKACF = ...,
|
|
casting: _CastingKind = ...,
|
|
subok: builtins.bool = ...,
|
|
copy: builtins.bool | _CopyMode = ...,
|
|
) -> ndarray[_ShapeT_co, dtype[_ScalarT]]: ...
|
|
@overload
|
|
def astype(
|
|
self,
|
|
dtype: DTypeLike,
|
|
order: _OrderKACF = ...,
|
|
casting: _CastingKind = ...,
|
|
subok: builtins.bool = ...,
|
|
copy: builtins.bool | _CopyMode = ...,
|
|
) -> ndarray[_ShapeT_co, dtype]: ...
|
|
|
|
#
|
|
@overload # ()
|
|
def view(self, /) -> Self: ...
|
|
@overload # (dtype: T)
|
|
def view(self, /, dtype: _DTypeT | _HasDType[_DTypeT]) -> ndarray[_ShapeT_co, _DTypeT]: ...
|
|
@overload # (dtype: dtype[T])
|
|
def view(self, /, dtype: _DTypeLike[_ScalarT]) -> NDArray[_ScalarT]: ...
|
|
@overload # (type: T)
|
|
def view(self, /, *, type: type[_ArrayT]) -> _ArrayT: ...
|
|
@overload # (_: T)
|
|
def view(self, /, dtype: type[_ArrayT]) -> _ArrayT: ...
|
|
@overload # (dtype: ?)
|
|
def view(self, /, dtype: DTypeLike) -> ndarray[_ShapeT_co, dtype]: ...
|
|
@overload # (dtype: ?, type: type[T])
|
|
def view(self, /, dtype: DTypeLike, type: type[_ArrayT]) -> _ArrayT: ...
|
|
|
|
def setfield(self, /, val: ArrayLike, dtype: DTypeLike, offset: SupportsIndex = 0) -> None: ...
|
|
@overload
|
|
def getfield(self, dtype: _DTypeLike[_ScalarT], offset: SupportsIndex = 0) -> NDArray[_ScalarT]: ...
|
|
@overload
|
|
def getfield(self, dtype: DTypeLike, offset: SupportsIndex = 0) -> NDArray[Any]: ...
|
|
|
|
def __index__(self: NDArray[integer], /) -> int: ...
|
|
def __complex__(self: NDArray[number | np.bool | object_], /) -> complex: ...
|
|
|
|
def __len__(self) -> int: ...
|
|
def __contains__(self, value: object, /) -> builtins.bool: ...
|
|
|
|
# NOTE: This weird `Never` tuple works around a strange mypy issue where it assigns
|
|
# `tuple[int]` to `tuple[Never]` or `tuple[int, int]` to `tuple[Never, Never]`.
|
|
# This way the bug only occurs for 9-D arrays, which are probably not very common.
|
|
@overload
|
|
def __iter__(self: ndarray[tuple[Never, Never, Never, Never, Never, Never, Never, Never, Never]], /) -> Iterator[Any]: ...
|
|
@overload # == 1-d & dtype[T \ object_]
|
|
def __iter__(self: ndarray[tuple[int], dtype[_NonObjectScalarT]], /) -> Iterator[_NonObjectScalarT]: ...
|
|
@overload # >= 2-d
|
|
def __iter__(self: ndarray[tuple[int, int, *tuple[int, ...]], dtype[_ScalarT]], /) -> Iterator[NDArray[_ScalarT]]: ...
|
|
@overload # ?-d
|
|
def __iter__(self, /) -> Iterator[Any]: ...
|
|
|
|
#
|
|
@overload
|
|
def __lt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __lt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __lt__(self: NDArray[datetime64], other: _ArrayLikeDT64_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __lt__(self: NDArray[bytes_], other: _ArrayLikeBytes_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __lt__(
|
|
self: ndarray[Any, dtype[str_] | dtypes.StringDType], other: _ArrayLikeStr_co | _ArrayLikeString_co, /
|
|
) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __lt__(self: NDArray[object_], other: object, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __lt__(self, other: _ArrayLikeObject_co, /) -> NDArray[np.bool]: ...
|
|
|
|
#
|
|
@overload
|
|
def __le__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __le__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __le__(self: NDArray[datetime64], other: _ArrayLikeDT64_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __le__(self: NDArray[bytes_], other: _ArrayLikeBytes_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __le__(
|
|
self: ndarray[Any, dtype[str_] | dtypes.StringDType], other: _ArrayLikeStr_co | _ArrayLikeString_co, /
|
|
) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __le__(self: NDArray[object_], other: object, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __le__(self, other: _ArrayLikeObject_co, /) -> NDArray[np.bool]: ...
|
|
|
|
#
|
|
@overload
|
|
def __gt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __gt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __gt__(self: NDArray[datetime64], other: _ArrayLikeDT64_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __gt__(self: NDArray[bytes_], other: _ArrayLikeBytes_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __gt__(
|
|
self: ndarray[Any, dtype[str_] | dtypes.StringDType], other: _ArrayLikeStr_co | _ArrayLikeString_co, /
|
|
) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __gt__(self: NDArray[object_], other: object, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __gt__(self, other: _ArrayLikeObject_co, /) -> NDArray[np.bool]: ...
|
|
|
|
#
|
|
@overload
|
|
def __ge__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __ge__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __ge__(self: NDArray[datetime64], other: _ArrayLikeDT64_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __ge__(self: NDArray[bytes_], other: _ArrayLikeBytes_co, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __ge__(
|
|
self: ndarray[Any, dtype[str_] | dtypes.StringDType], other: _ArrayLikeStr_co | _ArrayLikeString_co, /
|
|
) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __ge__(self: NDArray[object_], other: object, /) -> NDArray[np.bool]: ...
|
|
@overload
|
|
def __ge__(self, other: _ArrayLikeObject_co, /) -> NDArray[np.bool]: ...
|
|
|
|
# Unary ops
|
|
|
|
# TODO: Uncomment once https://github.com/python/mypy/issues/14070 is fixed
|
|
# @overload
|
|
# def __abs__(self: ndarray[_ShapeT, dtypes.Complex64DType], /) -> ndarray[_ShapeT, dtypes.Float32DType]: ...
|
|
# @overload
|
|
# def __abs__(self: ndarray[_ShapeT, dtypes.Complex128DType], /) -> ndarray[_ShapeT, dtypes.Float64DType]: ...
|
|
# @overload
|
|
# def __abs__(self: ndarray[_ShapeT, dtypes.CLongDoubleDType], /) -> ndarray[_ShapeT, dtypes.LongDoubleDType]: ...
|
|
# @overload
|
|
# def __abs__(self: ndarray[_ShapeT, dtype[complex128]], /) -> ndarray[_ShapeT, dtype[float64]]: ...
|
|
@overload
|
|
def __abs__(self: ndarray[_ShapeT, dtype[complexfloating[_NBit]]], /) -> ndarray[_ShapeT, dtype[floating[_NBit]]]: ...
|
|
@overload
|
|
def __abs__(self: _RealArrayT, /) -> _RealArrayT: ...
|
|
|
|
def __invert__(self: _IntegralArrayT, /) -> _IntegralArrayT: ... # noqa: PYI019
|
|
def __neg__(self: _NumericArrayT, /) -> _NumericArrayT: ... # noqa: PYI019
|
|
def __pos__(self: _NumericArrayT, /) -> _NumericArrayT: ... # noqa: PYI019
|
|
|
|
# Binary ops
|
|
|
|
# TODO: Support the "1d @ 1d -> scalar" case
|
|
@overload
|
|
def __matmul__(self: NDArray[_NumberT], other: _ArrayLikeBool_co, /) -> NDArray[_NumberT]: ...
|
|
@overload
|
|
def __matmul__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __matmul__(self: NDArray[np.bool], other: _ArrayLike[_NumberT], /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __matmul__(self: NDArray[floating[_64Bit]], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __matmul__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __matmul__(self: NDArray[complexfloating[_64Bit]], other: _ArrayLikeComplex128_co, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __matmul__(self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __matmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __matmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __matmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __matmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co, /) -> NDArray[complexfloating]: ...
|
|
@overload
|
|
def __matmul__(self: NDArray[number], other: _ArrayLikeNumber_co, /) -> NDArray[number]: ...
|
|
@overload
|
|
def __matmul__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __matmul__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload # signature equivalent to __matmul__
|
|
def __rmatmul__(self: NDArray[_NumberT], other: _ArrayLikeBool_co, /) -> NDArray[_NumberT]: ...
|
|
@overload
|
|
def __rmatmul__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmatmul__(self: NDArray[np.bool], other: _ArrayLike[_NumberT], /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmatmul__(self: NDArray[floating[_64Bit]], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rmatmul__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rmatmul__(self: NDArray[complexfloating[_64Bit]], other: _ArrayLikeComplex128_co, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __rmatmul__(self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __rmatmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmatmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmatmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmatmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co, /) -> NDArray[complexfloating]: ...
|
|
@overload
|
|
def __rmatmul__(self: NDArray[number], other: _ArrayLikeNumber_co, /) -> NDArray[number]: ...
|
|
@overload
|
|
def __rmatmul__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __rmatmul__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __mod__(self: NDArray[_RealNumberT], other: int | np.bool, /) -> ndarray[_ShapeT_co, dtype[_RealNumberT]]: ...
|
|
@overload
|
|
def __mod__(self: NDArray[_RealNumberT], other: _ArrayLikeBool_co, /) -> NDArray[_RealNumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mod__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[int8]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mod__(self: NDArray[np.bool], other: _ArrayLike[_RealNumberT], /) -> NDArray[_RealNumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mod__(self: NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __mod__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __mod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mod__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ...
|
|
@overload
|
|
def __mod__(self: NDArray[timedelta64], other: _ArrayLike[timedelta64], /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __mod__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __mod__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload # signature equivalent to __mod__
|
|
def __rmod__(self: NDArray[_RealNumberT], other: int | np.bool, /) -> ndarray[_ShapeT_co, dtype[_RealNumberT]]: ...
|
|
@overload
|
|
def __rmod__(self: NDArray[_RealNumberT], other: _ArrayLikeBool_co, /) -> NDArray[_RealNumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmod__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[int8]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmod__(self: NDArray[np.bool], other: _ArrayLike[_RealNumberT], /) -> NDArray[_RealNumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmod__(self: NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rmod__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ...
|
|
@overload
|
|
def __rmod__(self: NDArray[timedelta64], other: _ArrayLike[timedelta64], /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __rmod__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __rmod__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __divmod__(self: NDArray[_RealNumberT], rhs: int | np.bool, /) -> _2Tuple[ndarray[_ShapeT_co, dtype[_RealNumberT]]]: ...
|
|
@overload
|
|
def __divmod__(self: NDArray[_RealNumberT], rhs: _ArrayLikeBool_co, /) -> _2Tuple[NDArray[_RealNumberT]]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __divmod__(self: NDArray[np.bool], rhs: _ArrayLikeBool_co, /) -> _2Tuple[NDArray[int8]]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __divmod__(self: NDArray[np.bool], rhs: _ArrayLike[_RealNumberT], /) -> _2Tuple[NDArray[_RealNumberT]]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __divmod__(self: NDArray[float64], rhs: _ArrayLikeFloat64_co, /) -> _2Tuple[NDArray[float64]]: ...
|
|
@overload
|
|
def __divmod__(self: _ArrayFloat64_co, rhs: _ArrayLike[floating[_64Bit]], /) -> _2Tuple[NDArray[float64]]: ...
|
|
@overload
|
|
def __divmod__(self: _ArrayUInt_co, rhs: _ArrayLikeUInt_co, /) -> _2Tuple[NDArray[unsignedinteger]]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __divmod__(self: _ArrayInt_co, rhs: _ArrayLikeInt_co, /) -> _2Tuple[NDArray[signedinteger]]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __divmod__(self: _ArrayFloat_co, rhs: _ArrayLikeFloat_co, /) -> _2Tuple[NDArray[floating]]: ...
|
|
@overload
|
|
def __divmod__(self: NDArray[timedelta64], rhs: _ArrayLike[timedelta64], /) -> tuple[NDArray[int64], NDArray[timedelta64]]: ...
|
|
|
|
@overload # signature equivalent to __divmod__
|
|
def __rdivmod__(self: NDArray[_RealNumberT], lhs: int | np.bool, /) -> _2Tuple[ndarray[_ShapeT_co, dtype[_RealNumberT]]]: ...
|
|
@overload
|
|
def __rdivmod__(self: NDArray[_RealNumberT], lhs: _ArrayLikeBool_co, /) -> _2Tuple[NDArray[_RealNumberT]]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rdivmod__(self: NDArray[np.bool], lhs: _ArrayLikeBool_co, /) -> _2Tuple[NDArray[int8]]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rdivmod__(self: NDArray[np.bool], lhs: _ArrayLike[_RealNumberT], /) -> _2Tuple[NDArray[_RealNumberT]]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rdivmod__(self: NDArray[float64], lhs: _ArrayLikeFloat64_co, /) -> _2Tuple[NDArray[float64]]: ...
|
|
@overload
|
|
def __rdivmod__(self: _ArrayFloat64_co, lhs: _ArrayLike[floating[_64Bit]], /) -> _2Tuple[NDArray[float64]]: ...
|
|
@overload
|
|
def __rdivmod__(self: _ArrayUInt_co, lhs: _ArrayLikeUInt_co, /) -> _2Tuple[NDArray[unsignedinteger]]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rdivmod__(self: _ArrayInt_co, lhs: _ArrayLikeInt_co, /) -> _2Tuple[NDArray[signedinteger]]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rdivmod__(self: _ArrayFloat_co, lhs: _ArrayLikeFloat_co, /) -> _2Tuple[NDArray[floating]]: ...
|
|
@overload
|
|
def __rdivmod__(self: NDArray[timedelta64], lhs: _ArrayLike[timedelta64], /) -> tuple[NDArray[int64], NDArray[timedelta64]]: ...
|
|
|
|
@overload
|
|
def __add__(self: NDArray[_NumberT], other: int | np.bool, /) -> ndarray[_ShapeT_co, dtype[_NumberT]]: ...
|
|
@overload
|
|
def __add__(self: NDArray[_NumberT], other: _ArrayLikeBool_co, /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __add__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __add__(self: NDArray[np.bool], other: _ArrayLike[_NumberT], /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __add__(self: NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __add__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __add__(self: NDArray[complex128], other: _ArrayLikeComplex128_co, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __add__(self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __add__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __add__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __add__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __add__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co, /) -> NDArray[complexfloating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __add__(self: NDArray[number], other: _ArrayLikeNumber_co, /) -> NDArray[number]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __add__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co, /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __add__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co, /) -> NDArray[datetime64]: ...
|
|
@overload
|
|
def __add__(self: NDArray[datetime64], other: _ArrayLikeTD64_co, /) -> NDArray[datetime64]: ...
|
|
@overload
|
|
def __add__(self: NDArray[bytes_], other: _ArrayLikeBytes_co, /) -> NDArray[bytes_]: ...
|
|
@overload
|
|
def __add__(self: NDArray[str_], other: _ArrayLikeStr_co, /) -> NDArray[str_]: ...
|
|
@overload
|
|
def __add__(
|
|
self: ndarray[Any, dtypes.StringDType],
|
|
other: _ArrayLikeStr_co | _ArrayLikeString_co,
|
|
/,
|
|
) -> ndarray[tuple[Any, ...], dtypes.StringDType]: ...
|
|
@overload
|
|
def __add__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __add__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload # signature equivalent to __add__
|
|
def __radd__(self: NDArray[_NumberT], other: int | np.bool, /) -> ndarray[_ShapeT_co, dtype[_NumberT]]: ...
|
|
@overload
|
|
def __radd__(self: NDArray[_NumberT], other: _ArrayLikeBool_co, /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __radd__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __radd__(self: NDArray[np.bool], other: _ArrayLike[_NumberT], /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __radd__(self: NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __radd__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __radd__(self: NDArray[complex128], other: _ArrayLikeComplex128_co, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __radd__(self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __radd__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __radd__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __radd__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __radd__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co, /) -> NDArray[complexfloating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __radd__(self: NDArray[number], other: _ArrayLikeNumber_co, /) -> NDArray[number]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __radd__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co, /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __radd__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co, /) -> NDArray[datetime64]: ...
|
|
@overload
|
|
def __radd__(self: NDArray[datetime64], other: _ArrayLikeTD64_co, /) -> NDArray[datetime64]: ...
|
|
@overload
|
|
def __radd__(self: NDArray[bytes_], other: _ArrayLikeBytes_co, /) -> NDArray[bytes_]: ...
|
|
@overload
|
|
def __radd__(self: NDArray[str_], other: _ArrayLikeStr_co, /) -> NDArray[str_]: ...
|
|
@overload
|
|
def __radd__(
|
|
self: ndarray[Any, dtypes.StringDType],
|
|
other: _ArrayLikeStr_co | _ArrayLikeString_co,
|
|
/,
|
|
) -> ndarray[tuple[Any, ...], dtypes.StringDType]: ...
|
|
@overload
|
|
def __radd__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __radd__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __sub__(self: NDArray[_NumberT], other: int | np.bool, /) -> ndarray[_ShapeT_co, dtype[_NumberT]]: ...
|
|
@overload
|
|
def __sub__(self: NDArray[_NumberT], other: _ArrayLikeBool_co, /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __sub__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NoReturn: ...
|
|
@overload
|
|
def __sub__(self: NDArray[np.bool], other: _ArrayLike[_NumberT], /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __sub__(self: NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __sub__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __sub__(self: NDArray[complex128], other: _ArrayLikeComplex128_co, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __sub__(self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __sub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __sub__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __sub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __sub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co, /) -> NDArray[complexfloating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __sub__(self: NDArray[number], other: _ArrayLikeNumber_co, /) -> NDArray[number]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __sub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co, /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __sub__(self: NDArray[datetime64], other: _ArrayLikeTD64_co, /) -> NDArray[datetime64]: ...
|
|
@overload
|
|
def __sub__(self: NDArray[datetime64], other: _ArrayLikeDT64_co, /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __sub__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __sub__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __rsub__(self: NDArray[_NumberT], other: int | np.bool, /) -> ndarray[_ShapeT_co, dtype[_NumberT]]: ...
|
|
@overload
|
|
def __rsub__(self: NDArray[_NumberT], other: _ArrayLikeBool_co, /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rsub__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NoReturn: ...
|
|
@overload
|
|
def __rsub__(self: NDArray[np.bool], other: _ArrayLike[_NumberT], /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rsub__(self: NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rsub__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rsub__(self: NDArray[complex128], other: _ArrayLikeComplex128_co, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __rsub__(self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __rsub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rsub__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rsub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rsub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co, /) -> NDArray[complexfloating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rsub__(self: NDArray[number], other: _ArrayLikeNumber_co, /) -> NDArray[number]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co, /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co, /) -> NDArray[datetime64]: ...
|
|
@overload
|
|
def __rsub__(self: NDArray[datetime64], other: _ArrayLikeDT64_co, /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __rsub__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __rsub__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __mul__(self: NDArray[_NumberT], other: int | np.bool, /) -> ndarray[_ShapeT_co, dtype[_NumberT]]: ...
|
|
@overload
|
|
def __mul__(self: NDArray[_NumberT], other: _ArrayLikeBool_co, /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mul__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mul__(self: NDArray[np.bool], other: _ArrayLike[_NumberT], /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mul__(self: NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __mul__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __mul__(self: NDArray[complex128], other: _ArrayLikeComplex128_co, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __mul__(self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __mul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mul__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co, /) -> NDArray[complexfloating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __mul__(self: NDArray[number], other: _ArrayLikeNumber_co, /) -> NDArray[number]: ...
|
|
@overload
|
|
def __mul__(self: NDArray[timedelta64], other: _ArrayLikeFloat_co, /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __mul__(self: _ArrayFloat_co, other: _ArrayLike[timedelta64], /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __mul__(
|
|
self: ndarray[Any, dtype[character] | dtypes.StringDType],
|
|
other: _ArrayLikeInt,
|
|
/,
|
|
) -> ndarray[tuple[Any, ...], _DTypeT_co]: ...
|
|
@overload
|
|
def __mul__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __mul__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload # signature equivalent to __mul__
|
|
def __rmul__(self: NDArray[_NumberT], other: int | np.bool, /) -> ndarray[_ShapeT_co, dtype[_NumberT]]: ...
|
|
@overload
|
|
def __rmul__(self: NDArray[_NumberT], other: _ArrayLikeBool_co, /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmul__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmul__(self: NDArray[np.bool], other: _ArrayLike[_NumberT], /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmul__(self: NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rmul__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rmul__(self: NDArray[complex128], other: _ArrayLikeComplex128_co, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __rmul__(self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __rmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co, /) -> NDArray[complexfloating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rmul__(self: NDArray[number], other: _ArrayLikeNumber_co, /) -> NDArray[number]: ...
|
|
@overload
|
|
def __rmul__(self: NDArray[timedelta64], other: _ArrayLikeFloat_co, /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __rmul__(self: _ArrayFloat_co, other: _ArrayLike[timedelta64], /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __rmul__(
|
|
self: ndarray[Any, dtype[character] | dtypes.StringDType],
|
|
other: _ArrayLikeInt,
|
|
/,
|
|
) -> ndarray[tuple[Any, ...], _DTypeT_co]: ...
|
|
@overload
|
|
def __rmul__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __rmul__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __truediv__(self: _ArrayInt_co | NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __truediv__(self: _ArrayFloat64_co, other: _ArrayLikeInt_co | _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __truediv__(self: NDArray[complex128], other: _ArrayLikeComplex128_co, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __truediv__(self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __truediv__(self: NDArray[floating], other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ...
|
|
@overload
|
|
def __truediv__(self: _ArrayFloat_co, other: _ArrayLike[floating], /) -> NDArray[floating]: ...
|
|
@overload
|
|
def __truediv__(self: NDArray[complexfloating], other: _ArrayLikeNumber_co, /) -> NDArray[complexfloating]: ...
|
|
@overload
|
|
def __truediv__(self: _ArrayNumber_co, other: _ArrayLike[complexfloating], /) -> NDArray[complexfloating]: ...
|
|
@overload
|
|
def __truediv__(self: NDArray[inexact], other: _ArrayLikeNumber_co, /) -> NDArray[inexact]: ...
|
|
@overload
|
|
def __truediv__(self: NDArray[number], other: _ArrayLikeNumber_co, /) -> NDArray[number]: ...
|
|
@overload
|
|
def __truediv__(self: NDArray[timedelta64], other: _ArrayLike[timedelta64], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __truediv__(self: NDArray[timedelta64], other: _ArrayLikeBool_co, /) -> NoReturn: ...
|
|
@overload
|
|
def __truediv__(self: NDArray[timedelta64], other: _ArrayLikeFloat_co, /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __truediv__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __truediv__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __rtruediv__(self: _ArrayInt_co | NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rtruediv__(self: _ArrayFloat64_co, other: _ArrayLikeInt_co | _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[complex128], other: _ArrayLikeComplex128_co, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __rtruediv__(self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[floating], other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ...
|
|
@overload
|
|
def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLike[floating], /) -> NDArray[floating]: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[complexfloating], other: _ArrayLikeNumber_co, /) -> NDArray[complexfloating]: ...
|
|
@overload
|
|
def __rtruediv__(self: _ArrayNumber_co, other: _ArrayLike[complexfloating], /) -> NDArray[complexfloating]: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[inexact], other: _ArrayLikeNumber_co, /) -> NDArray[inexact]: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[number], other: _ArrayLikeNumber_co, /) -> NDArray[number]: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[timedelta64], other: _ArrayLike[timedelta64], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[integer | floating], other: _ArrayLike[timedelta64], /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __floordiv__(self: NDArray[_RealNumberT], other: int | np.bool, /) -> ndarray[_ShapeT_co, dtype[_RealNumberT]]: ...
|
|
@overload
|
|
def __floordiv__(self: NDArray[_RealNumberT], other: _ArrayLikeBool_co, /) -> NDArray[_RealNumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __floordiv__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[int8]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __floordiv__(self: NDArray[np.bool], other: _ArrayLike[_RealNumberT], /) -> NDArray[_RealNumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __floordiv__(self: NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __floordiv__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __floordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __floordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __floordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ...
|
|
@overload
|
|
def __floordiv__(self: NDArray[timedelta64], other: _ArrayLike[timedelta64], /) -> NDArray[int64]: ...
|
|
@overload
|
|
def __floordiv__(self: NDArray[timedelta64], other: _ArrayLikeBool_co, /) -> NoReturn: ...
|
|
@overload
|
|
def __floordiv__(self: NDArray[timedelta64], other: _ArrayLikeFloat_co, /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __floordiv__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __floordiv__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __rfloordiv__(self: NDArray[_RealNumberT], other: int | np.bool, /) -> ndarray[_ShapeT_co, dtype[_RealNumberT]]: ...
|
|
@overload
|
|
def __rfloordiv__(self: NDArray[_RealNumberT], other: _ArrayLikeBool_co, /) -> NDArray[_RealNumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rfloordiv__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[int8]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rfloordiv__(self: NDArray[np.bool], other: _ArrayLike[_RealNumberT], /) -> NDArray[_RealNumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rfloordiv__(self: NDArray[float64], other: _ArrayLikeFloat64_co, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rfloordiv__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rfloordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rfloordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, /) -> NDArray[floating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rfloordiv__(self: NDArray[timedelta64], other: _ArrayLike[timedelta64], /) -> NDArray[int64]: ...
|
|
@overload
|
|
def __rfloordiv__(self: NDArray[floating | integer], other: _ArrayLike[timedelta64], /) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __rfloordiv__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __rfloordiv__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __pow__(self: NDArray[_NumberT], other: int | np.bool, mod: None = None, /) -> ndarray[_ShapeT_co, dtype[_NumberT]]: ...
|
|
@overload
|
|
def __pow__(self: NDArray[_NumberT], other: _ArrayLikeBool_co, mod: None = None, /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __pow__(self: NDArray[np.bool], other: _ArrayLikeBool_co, mod: None = None, /) -> NDArray[int8]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __pow__(self: NDArray[np.bool], other: _ArrayLike[_NumberT], mod: None = None, /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __pow__(self: NDArray[float64], other: _ArrayLikeFloat64_co, mod: None = None, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __pow__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], mod: None = None, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __pow__(self: NDArray[complex128], other: _ArrayLikeComplex128_co, mod: None = None, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __pow__(
|
|
self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], mod: None = None, /
|
|
) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __pow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, mod: None = None, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __pow__(self: _ArrayInt_co, other: _ArrayLikeInt_co, mod: None = None, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __pow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, mod: None = None, /) -> NDArray[floating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __pow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co, mod: None = None, /) -> NDArray[complexfloating]: ...
|
|
@overload
|
|
def __pow__(self: NDArray[number], other: _ArrayLikeNumber_co, mod: None = None, /) -> NDArray[number]: ...
|
|
@overload
|
|
def __pow__(self: NDArray[object_], other: Any, mod: None = None, /) -> Any: ...
|
|
@overload
|
|
def __pow__(self: NDArray[Any], other: _ArrayLikeObject_co, mod: None = None, /) -> Any: ...
|
|
|
|
@overload
|
|
def __rpow__(self: NDArray[_NumberT], other: int | np.bool, mod: None = None, /) -> ndarray[_ShapeT_co, dtype[_NumberT]]: ...
|
|
@overload
|
|
def __rpow__(self: NDArray[_NumberT], other: _ArrayLikeBool_co, mod: None = None, /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rpow__(self: NDArray[np.bool], other: _ArrayLikeBool_co, mod: None = None, /) -> NDArray[int8]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rpow__(self: NDArray[np.bool], other: _ArrayLike[_NumberT], mod: None = None, /) -> NDArray[_NumberT]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rpow__(self: NDArray[float64], other: _ArrayLikeFloat64_co, mod: None = None, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rpow__(self: _ArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], mod: None = None, /) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rpow__(self: NDArray[complex128], other: _ArrayLikeComplex128_co, mod: None = None, /) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __rpow__(
|
|
self: _ArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], mod: None = None, /
|
|
) -> NDArray[complex128]: ...
|
|
@overload
|
|
def __rpow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, mod: None = None, /) -> NDArray[unsignedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rpow__(self: _ArrayInt_co, other: _ArrayLikeInt_co, mod: None = None, /) -> NDArray[signedinteger]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rpow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co, mod: None = None, /) -> NDArray[floating]: ... # type: ignore[overload-overlap]
|
|
@overload
|
|
def __rpow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co, mod: None = None, /) -> NDArray[complexfloating]: ...
|
|
@overload
|
|
def __rpow__(self: NDArray[number], other: _ArrayLikeNumber_co, mod: None = None, /) -> NDArray[number]: ...
|
|
@overload
|
|
def __rpow__(self: NDArray[object_], other: Any, mod: None = None, /) -> Any: ...
|
|
@overload
|
|
def __rpow__(self: NDArray[Any], other: _ArrayLikeObject_co, mod: None = None, /) -> Any: ...
|
|
|
|
@overload
|
|
def __lshift__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __lshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[misc]
|
|
@overload
|
|
def __lshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ...
|
|
@overload
|
|
def __lshift__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __lshift__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __rlshift__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rlshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rlshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ...
|
|
@overload
|
|
def __rlshift__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __rlshift__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __rshift__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ...
|
|
@overload
|
|
def __rshift__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __rshift__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __rrshift__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rrshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rrshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ...
|
|
@overload
|
|
def __rrshift__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __rrshift__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __and__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[misc]
|
|
@overload
|
|
def __and__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[misc]
|
|
@overload
|
|
def __and__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ...
|
|
@overload
|
|
def __and__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __and__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __rand__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rand__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rand__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ...
|
|
@overload
|
|
def __rand__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __rand__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __xor__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[misc]
|
|
@overload
|
|
def __xor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[misc]
|
|
@overload
|
|
def __xor__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ...
|
|
@overload
|
|
def __xor__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __xor__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __rxor__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rxor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rxor__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ...
|
|
@overload
|
|
def __rxor__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __rxor__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __or__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[misc]
|
|
@overload
|
|
def __or__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[misc]
|
|
@overload
|
|
def __or__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ...
|
|
@overload
|
|
def __or__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __or__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
@overload
|
|
def __ror__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> NDArray[np.bool]: ... # type: ignore[misc]
|
|
@overload
|
|
def __ror__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co, /) -> NDArray[unsignedinteger]: ... # type: ignore[misc]
|
|
@overload
|
|
def __ror__(self: _ArrayInt_co, other: _ArrayLikeInt_co, /) -> NDArray[signedinteger]: ...
|
|
@overload
|
|
def __ror__(self: NDArray[object_], other: Any, /) -> Any: ...
|
|
@overload
|
|
def __ror__(self: NDArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
|
|
|
|
# `np.generic` does not support inplace operations
|
|
|
|
# NOTE: Inplace ops generally use "same_kind" casting w.r.t. to the left
|
|
# operand. An exception to this rule are unsigned integers though, which
|
|
# also accepts a signed integer for the right operand as long it is a 0D
|
|
# object and its value is >= 0
|
|
# NOTE: Due to a mypy bug, overloading on e.g. `self: NDArray[SCT_floating]` won't
|
|
# work, as this will lead to `false negatives` when using these inplace ops.
|
|
@overload
|
|
def __iadd__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[integer], other: _ArrayLikeInt_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[floating], other: _ArrayLikeFloat_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[complexfloating], other: _ArrayLikeComplex_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[timedelta64 | datetime64], other: _ArrayLikeTD64_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[bytes_], other: _ArrayLikeBytes_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __iadd__(
|
|
self: ndarray[Any, dtype[str_] | dtypes.StringDType],
|
|
other: _ArrayLikeStr_co | _ArrayLikeString_co,
|
|
/,
|
|
) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
#
|
|
@overload
|
|
def __isub__(self: NDArray[integer], other: _ArrayLikeInt_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __isub__(self: NDArray[floating], other: _ArrayLikeFloat_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __isub__(self: NDArray[complexfloating], other: _ArrayLikeComplex_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __isub__(self: NDArray[timedelta64 | datetime64], other: _ArrayLikeTD64_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __isub__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
#
|
|
@overload
|
|
def __imul__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __imul__(
|
|
self: ndarray[Any, dtype[integer | character] | dtypes.StringDType], other: _ArrayLikeInt_co, /
|
|
) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __imul__(self: NDArray[floating | timedelta64], other: _ArrayLikeFloat_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __imul__(self: NDArray[complexfloating], other: _ArrayLikeComplex_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __imul__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
@overload
|
|
def __ipow__(self: NDArray[integer], other: _ArrayLikeInt_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __ipow__(self: NDArray[floating], other: _ArrayLikeFloat_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __ipow__(self: NDArray[complexfloating], other: _ArrayLikeComplex_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __ipow__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
#
|
|
@overload
|
|
def __itruediv__(self: NDArray[floating | timedelta64], other: _ArrayLikeFloat_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __itruediv__(self: NDArray[complexfloating], other: _ArrayLikeComplex_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __itruediv__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
# keep in sync with `__imod__`
|
|
@overload
|
|
def __ifloordiv__(self: NDArray[integer], other: _ArrayLikeInt_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __ifloordiv__(self: NDArray[floating | timedelta64], other: _ArrayLikeFloat_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __ifloordiv__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
# keep in sync with `__ifloordiv__`
|
|
@overload
|
|
def __imod__(self: NDArray[integer], other: _ArrayLikeInt_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __imod__(self: NDArray[floating], other: _ArrayLikeFloat_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __imod__(
|
|
self: NDArray[timedelta64],
|
|
other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]],
|
|
/,
|
|
) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __imod__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
# keep in sync with `__irshift__`
|
|
@overload
|
|
def __ilshift__(self: NDArray[integer], other: _ArrayLikeInt_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __ilshift__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
# keep in sync with `__ilshift__`
|
|
@overload
|
|
def __irshift__(self: NDArray[integer], other: _ArrayLikeInt_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __irshift__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
# keep in sync with `__ixor__` and `__ior__`
|
|
@overload
|
|
def __iand__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __iand__(self: NDArray[integer], other: _ArrayLikeInt_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __iand__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
# keep in sync with `__iand__` and `__ior__`
|
|
@overload
|
|
def __ixor__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __ixor__(self: NDArray[integer], other: _ArrayLikeInt_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __ixor__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
# keep in sync with `__iand__` and `__ixor__`
|
|
@overload
|
|
def __ior__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __ior__(self: NDArray[integer], other: _ArrayLikeInt_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __ior__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
#
|
|
@overload
|
|
def __imatmul__(self: NDArray[np.bool], other: _ArrayLikeBool_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __imatmul__(self: NDArray[integer], other: _ArrayLikeInt_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __imatmul__(self: NDArray[floating], other: _ArrayLikeFloat_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __imatmul__(self: NDArray[complexfloating], other: _ArrayLikeComplex_co, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
@overload
|
|
def __imatmul__(self: NDArray[object_], other: Any, /) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
|
|
|
|
#
|
|
def __dlpack__(
|
|
self: NDArray[number],
|
|
/,
|
|
*,
|
|
stream: int | Any | None = None,
|
|
max_version: tuple[int, int] | None = None,
|
|
dl_device: tuple[int, int] | None = None,
|
|
copy: builtins.bool | None = None,
|
|
) -> CapsuleType: ...
|
|
def __dlpack_device__(self, /) -> tuple[L[1], L[0]]: ...
|
|
|
|
# Keep `dtype` at the bottom to avoid name conflicts with `np.dtype`
|
|
@property
|
|
def dtype(self) -> _DTypeT_co: ...
|
|
|
|
# NOTE: while `np.generic` is not technically an instance of `ABCMeta`,
|
|
# the `@abstractmethod` decorator is herein used to (forcefully) deny
|
|
# the creation of `np.generic` instances.
|
|
# The `# type: ignore` comments are necessary to silence mypy errors regarding
|
|
# the missing `ABCMeta` metaclass.
|
|
# See https://github.com/numpy/numpy-stubs/pull/80 for more details.
|
|
class generic(_ArrayOrScalarCommon, Generic[_ItemT_co]):
|
|
@abstractmethod
|
|
def __init__(self, *args: Any, **kwargs: Any) -> None: ...
|
|
def __hash__(self) -> int: ...
|
|
@overload
|
|
def __array__(self, dtype: None = None, /) -> ndarray[tuple[()], dtype[Self]]: ...
|
|
@overload
|
|
def __array__(self, dtype: _DTypeT, /) -> ndarray[tuple[()], _DTypeT]: ...
|
|
if sys.version_info >= (3, 12):
|
|
def __buffer__(self, flags: int, /) -> memoryview: ...
|
|
|
|
@property
|
|
def base(self) -> None: ...
|
|
@property
|
|
def ndim(self) -> L[0]: ...
|
|
@property
|
|
def size(self) -> L[1]: ...
|
|
@property
|
|
def shape(self) -> tuple[()]: ...
|
|
@property
|
|
def strides(self) -> tuple[()]: ...
|
|
@property
|
|
def flat(self) -> flatiter[ndarray[tuple[int], dtype[Self]]]: ...
|
|
|
|
@overload
|
|
def item(self, /) -> _ItemT_co: ...
|
|
@overload
|
|
def item(self, arg0: L[0, -1] | tuple[L[0, -1]] | tuple[()] = ..., /) -> _ItemT_co: ...
|
|
def tolist(self, /) -> _ItemT_co: ...
|
|
|
|
def byteswap(self, inplace: L[False] = ...) -> Self: ...
|
|
|
|
@overload
|
|
def astype(
|
|
self,
|
|
dtype: _DTypeLike[_ScalarT],
|
|
order: _OrderKACF = ...,
|
|
casting: _CastingKind = ...,
|
|
subok: builtins.bool = ...,
|
|
copy: builtins.bool | _CopyMode = ...,
|
|
) -> _ScalarT: ...
|
|
@overload
|
|
def astype(
|
|
self,
|
|
dtype: DTypeLike,
|
|
order: _OrderKACF = ...,
|
|
casting: _CastingKind = ...,
|
|
subok: builtins.bool = ...,
|
|
copy: builtins.bool | _CopyMode = ...,
|
|
) -> Any: ...
|
|
|
|
# NOTE: `view` will perform a 0D->scalar cast,
|
|
# thus the array `type` is irrelevant to the output type
|
|
@overload
|
|
def view(self, type: type[NDArray[Any]] = ...) -> Self: ...
|
|
@overload
|
|
def view(
|
|
self,
|
|
dtype: _DTypeLike[_ScalarT],
|
|
type: type[NDArray[Any]] = ...,
|
|
) -> _ScalarT: ...
|
|
@overload
|
|
def view(
|
|
self,
|
|
dtype: DTypeLike,
|
|
type: type[NDArray[Any]] = ...,
|
|
) -> Any: ...
|
|
|
|
@overload
|
|
def getfield(
|
|
self,
|
|
dtype: _DTypeLike[_ScalarT],
|
|
offset: SupportsIndex = ...
|
|
) -> _ScalarT: ...
|
|
@overload
|
|
def getfield(
|
|
self,
|
|
dtype: DTypeLike,
|
|
offset: SupportsIndex = ...
|
|
) -> Any: ...
|
|
|
|
@overload
|
|
def take( # type: ignore[misc]
|
|
self,
|
|
indices: _IntLike_co,
|
|
axis: SupportsIndex | None = ...,
|
|
out: None = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> Self: ...
|
|
@overload
|
|
def take( # type: ignore[misc]
|
|
self,
|
|
indices: _ArrayLikeInt_co,
|
|
axis: SupportsIndex | None = ...,
|
|
out: None = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> NDArray[Self]: ...
|
|
@overload
|
|
def take(
|
|
self,
|
|
indices: _ArrayLikeInt_co,
|
|
axis: SupportsIndex | None = ...,
|
|
out: _ArrayT = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> _ArrayT: ...
|
|
|
|
def repeat(self, repeats: _ArrayLikeInt_co, axis: SupportsIndex | None = None) -> ndarray[tuple[int], dtype[Self]]: ...
|
|
def flatten(self, /, order: _OrderKACF = "C") -> ndarray[tuple[int], dtype[Self]]: ...
|
|
def ravel(self, /, order: _OrderKACF = "C") -> ndarray[tuple[int], dtype[Self]]: ...
|
|
|
|
@overload # (() | [])
|
|
def reshape(
|
|
self,
|
|
shape: tuple[()] | list[Never],
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> Self: ...
|
|
@overload # ((1, *(1, ...))@_ShapeT)
|
|
def reshape(
|
|
self,
|
|
shape: _1NShapeT,
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[_1NShapeT, dtype[Self]]: ...
|
|
@overload # (Sequence[index, ...]) # not recommended
|
|
def reshape(
|
|
self,
|
|
shape: Sequence[SupportsIndex],
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> Self | ndarray[tuple[L[1], ...], dtype[Self]]: ...
|
|
@overload # _(index)
|
|
def reshape(
|
|
self,
|
|
size1: SupportsIndex,
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[tuple[L[1]], dtype[Self]]: ...
|
|
@overload # _(index, index)
|
|
def reshape(
|
|
self,
|
|
size1: SupportsIndex,
|
|
size2: SupportsIndex,
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[tuple[L[1], L[1]], dtype[Self]]: ...
|
|
@overload # _(index, index, index)
|
|
def reshape(
|
|
self,
|
|
size1: SupportsIndex,
|
|
size2: SupportsIndex,
|
|
size3: SupportsIndex,
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[tuple[L[1], L[1], L[1]], dtype[Self]]: ...
|
|
@overload # _(index, index, index, index)
|
|
def reshape(
|
|
self,
|
|
size1: SupportsIndex,
|
|
size2: SupportsIndex,
|
|
size3: SupportsIndex,
|
|
size4: SupportsIndex,
|
|
/,
|
|
*,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[tuple[L[1], L[1], L[1], L[1]], dtype[Self]]: ...
|
|
@overload # _(index, index, index, index, index, *index) # ndim >= 5
|
|
def reshape(
|
|
self,
|
|
size1: SupportsIndex,
|
|
size2: SupportsIndex,
|
|
size3: SupportsIndex,
|
|
size4: SupportsIndex,
|
|
size5: SupportsIndex,
|
|
/,
|
|
*sizes6_: SupportsIndex,
|
|
order: _OrderACF = "C",
|
|
copy: builtins.bool | None = None,
|
|
) -> ndarray[tuple[L[1], L[1], L[1], L[1], L[1], *tuple[L[1], ...]], dtype[Self]]: ...
|
|
|
|
def squeeze(self, axis: L[0] | tuple[()] | None = ...) -> Self: ...
|
|
def transpose(self, axes: tuple[()] | None = ..., /) -> Self: ...
|
|
|
|
@overload
|
|
def all(
|
|
self,
|
|
/,
|
|
axis: L[0, -1] | tuple[()] | None = None,
|
|
out: None = None,
|
|
keepdims: SupportsIndex = False,
|
|
*,
|
|
where: builtins.bool | np.bool | ndarray[tuple[()], dtype[np.bool]] = True
|
|
) -> np.bool: ...
|
|
@overload
|
|
def all(
|
|
self,
|
|
/,
|
|
axis: L[0, -1] | tuple[()] | None,
|
|
out: ndarray[tuple[()], dtype[_ScalarT]],
|
|
keepdims: SupportsIndex = False,
|
|
*,
|
|
where: builtins.bool | np.bool | ndarray[tuple[()], dtype[np.bool]] = True,
|
|
) -> _ScalarT: ...
|
|
@overload
|
|
def all(
|
|
self,
|
|
/,
|
|
axis: L[0, -1] | tuple[()] | None = None,
|
|
*,
|
|
out: ndarray[tuple[()], dtype[_ScalarT]],
|
|
keepdims: SupportsIndex = False,
|
|
where: builtins.bool | np.bool | ndarray[tuple[()], dtype[np.bool]] = True,
|
|
) -> _ScalarT: ...
|
|
|
|
@overload
|
|
def any(
|
|
self,
|
|
/,
|
|
axis: L[0, -1] | tuple[()] | None = None,
|
|
out: None = None,
|
|
keepdims: SupportsIndex = False,
|
|
*,
|
|
where: builtins.bool | np.bool | ndarray[tuple[()], dtype[np.bool]] = True
|
|
) -> np.bool: ...
|
|
@overload
|
|
def any(
|
|
self,
|
|
/,
|
|
axis: L[0, -1] | tuple[()] | None,
|
|
out: ndarray[tuple[()], dtype[_ScalarT]],
|
|
keepdims: SupportsIndex = False,
|
|
*,
|
|
where: builtins.bool | np.bool | ndarray[tuple[()], dtype[np.bool]] = True,
|
|
) -> _ScalarT: ...
|
|
@overload
|
|
def any(
|
|
self,
|
|
/,
|
|
axis: L[0, -1] | tuple[()] | None = None,
|
|
*,
|
|
out: ndarray[tuple[()], dtype[_ScalarT]],
|
|
keepdims: SupportsIndex = False,
|
|
where: builtins.bool | np.bool | ndarray[tuple[()], dtype[np.bool]] = True,
|
|
) -> _ScalarT: ...
|
|
|
|
# Keep `dtype` at the bottom to avoid name conflicts with `np.dtype`
|
|
@property
|
|
def dtype(self) -> _dtype[Self]: ...
|
|
|
|
class number(generic[_NumberItemT_co], Generic[_NBit, _NumberItemT_co]):
|
|
@abstractmethod
|
|
def __init__(self, value: _NumberItemT_co, /) -> None: ...
|
|
def __class_getitem__(cls, item: Any, /) -> GenericAlias: ...
|
|
|
|
def __neg__(self) -> Self: ...
|
|
def __pos__(self) -> Self: ...
|
|
def __abs__(self) -> Self: ...
|
|
|
|
__add__: _NumberOp
|
|
__radd__: _NumberOp
|
|
__sub__: _NumberOp
|
|
__rsub__: _NumberOp
|
|
__mul__: _NumberOp
|
|
__rmul__: _NumberOp
|
|
__floordiv__: _NumberOp
|
|
__rfloordiv__: _NumberOp
|
|
__pow__: _NumberOp
|
|
__rpow__: _NumberOp
|
|
__truediv__: _NumberOp
|
|
__rtruediv__: _NumberOp
|
|
|
|
__lt__: _ComparisonOpLT[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__le__: _ComparisonOpLE[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__gt__: _ComparisonOpGT[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__ge__: _ComparisonOpGE[_NumberLike_co, _ArrayLikeNumber_co]
|
|
|
|
class bool(generic[_BoolItemT_co], Generic[_BoolItemT_co]):
|
|
@property
|
|
def itemsize(self) -> L[1]: ...
|
|
@property
|
|
def nbytes(self) -> L[1]: ...
|
|
@property
|
|
def real(self) -> Self: ...
|
|
@property
|
|
def imag(self) -> np.bool[L[False]]: ...
|
|
|
|
@overload # mypy bug workaround: https://github.com/numpy/numpy/issues/29245
|
|
def __init__(self: np.bool[builtins.bool], value: Never, /) -> None: ...
|
|
@overload
|
|
def __init__(self: np.bool[L[False]], value: _Falsy = ..., /) -> None: ...
|
|
@overload
|
|
def __init__(self: np.bool[L[True]], value: _Truthy, /) -> None: ...
|
|
@overload
|
|
def __init__(self: np.bool[builtins.bool], value: object, /) -> None: ...
|
|
|
|
def __bool__(self, /) -> _BoolItemT_co: ...
|
|
|
|
@overload
|
|
def __int__(self: np.bool[L[False]], /) -> L[0]: ...
|
|
@overload
|
|
def __int__(self: np.bool[L[True]], /) -> L[1]: ...
|
|
@overload
|
|
def __int__(self, /) -> L[0, 1]: ...
|
|
|
|
def __abs__(self) -> Self: ...
|
|
|
|
@overload
|
|
def __invert__(self: np.bool[L[False]], /) -> np.bool[L[True]]: ...
|
|
@overload
|
|
def __invert__(self: np.bool[L[True]], /) -> np.bool[L[False]]: ...
|
|
@overload
|
|
def __invert__(self, /) -> np.bool: ...
|
|
|
|
@overload
|
|
def __add__(self, other: _NumberT, /) -> _NumberT: ...
|
|
@overload
|
|
def __add__(self, other: builtins.bool | bool_, /) -> bool_: ...
|
|
@overload
|
|
def __add__(self, other: int, /) -> int_: ...
|
|
@overload
|
|
def __add__(self, other: float, /) -> float64: ...
|
|
@overload
|
|
def __add__(self, other: complex, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __radd__(self, other: _NumberT, /) -> _NumberT: ...
|
|
@overload
|
|
def __radd__(self, other: builtins.bool, /) -> bool_: ...
|
|
@overload
|
|
def __radd__(self, other: int, /) -> int_: ...
|
|
@overload
|
|
def __radd__(self, other: float, /) -> float64: ...
|
|
@overload
|
|
def __radd__(self, other: complex, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __sub__(self, other: _NumberT, /) -> _NumberT: ...
|
|
@overload
|
|
def __sub__(self, other: int, /) -> int_: ...
|
|
@overload
|
|
def __sub__(self, other: float, /) -> float64: ...
|
|
@overload
|
|
def __sub__(self, other: complex, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __rsub__(self, other: _NumberT, /) -> _NumberT: ...
|
|
@overload
|
|
def __rsub__(self, other: int, /) -> int_: ...
|
|
@overload
|
|
def __rsub__(self, other: float, /) -> float64: ...
|
|
@overload
|
|
def __rsub__(self, other: complex, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __mul__(self, other: _NumberT, /) -> _NumberT: ...
|
|
@overload
|
|
def __mul__(self, other: builtins.bool | bool_, /) -> bool_: ...
|
|
@overload
|
|
def __mul__(self, other: int, /) -> int_: ...
|
|
@overload
|
|
def __mul__(self, other: float, /) -> float64: ...
|
|
@overload
|
|
def __mul__(self, other: complex, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __rmul__(self, other: _NumberT, /) -> _NumberT: ...
|
|
@overload
|
|
def __rmul__(self, other: builtins.bool, /) -> bool_: ...
|
|
@overload
|
|
def __rmul__(self, other: int, /) -> int_: ...
|
|
@overload
|
|
def __rmul__(self, other: float, /) -> float64: ...
|
|
@overload
|
|
def __rmul__(self, other: complex, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __pow__(self, other: _NumberT, mod: None = None, /) -> _NumberT: ...
|
|
@overload
|
|
def __pow__(self, other: builtins.bool | bool_, mod: None = None, /) -> int8: ...
|
|
@overload
|
|
def __pow__(self, other: int, mod: None = None, /) -> int_: ...
|
|
@overload
|
|
def __pow__(self, other: float, mod: None = None, /) -> float64: ...
|
|
@overload
|
|
def __pow__(self, other: complex, mod: None = None, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __rpow__(self, other: _NumberT, mod: None = None, /) -> _NumberT: ...
|
|
@overload
|
|
def __rpow__(self, other: builtins.bool, mod: None = None, /) -> int8: ...
|
|
@overload
|
|
def __rpow__(self, other: int, mod: None = None, /) -> int_: ...
|
|
@overload
|
|
def __rpow__(self, other: float, mod: None = None, /) -> float64: ...
|
|
@overload
|
|
def __rpow__(self, other: complex, mod: None = None, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __truediv__(self, other: _InexactT, /) -> _InexactT: ...
|
|
@overload
|
|
def __truediv__(self, other: float | integer | bool_, /) -> float64: ...
|
|
@overload
|
|
def __truediv__(self, other: complex, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __rtruediv__(self, other: _InexactT, /) -> _InexactT: ...
|
|
@overload
|
|
def __rtruediv__(self, other: float | integer, /) -> float64: ...
|
|
@overload
|
|
def __rtruediv__(self, other: complex, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __floordiv__(self, other: _RealNumberT, /) -> _RealNumberT: ...
|
|
@overload
|
|
def __floordiv__(self, other: builtins.bool | bool_, /) -> int8: ...
|
|
@overload
|
|
def __floordiv__(self, other: int, /) -> int_: ...
|
|
@overload
|
|
def __floordiv__(self, other: float, /) -> float64: ...
|
|
|
|
@overload
|
|
def __rfloordiv__(self, other: _RealNumberT, /) -> _RealNumberT: ...
|
|
@overload
|
|
def __rfloordiv__(self, other: builtins.bool, /) -> int8: ...
|
|
@overload
|
|
def __rfloordiv__(self, other: int, /) -> int_: ...
|
|
@overload
|
|
def __rfloordiv__(self, other: float, /) -> float64: ...
|
|
|
|
# keep in sync with __floordiv__
|
|
@overload
|
|
def __mod__(self, other: _RealNumberT, /) -> _RealNumberT: ...
|
|
@overload
|
|
def __mod__(self, other: builtins.bool | bool_, /) -> int8: ...
|
|
@overload
|
|
def __mod__(self, other: int, /) -> int_: ...
|
|
@overload
|
|
def __mod__(self, other: float, /) -> float64: ...
|
|
|
|
# keep in sync with __rfloordiv__
|
|
@overload
|
|
def __rmod__(self, other: _RealNumberT, /) -> _RealNumberT: ...
|
|
@overload
|
|
def __rmod__(self, other: builtins.bool, /) -> int8: ...
|
|
@overload
|
|
def __rmod__(self, other: int, /) -> int_: ...
|
|
@overload
|
|
def __rmod__(self, other: float, /) -> float64: ...
|
|
|
|
# keep in sync with __mod__
|
|
@overload
|
|
def __divmod__(self, other: _RealNumberT, /) -> _2Tuple[_RealNumberT]: ...
|
|
@overload
|
|
def __divmod__(self, other: builtins.bool | bool_, /) -> _2Tuple[int8]: ...
|
|
@overload
|
|
def __divmod__(self, other: int, /) -> _2Tuple[int_]: ...
|
|
@overload
|
|
def __divmod__(self, other: float, /) -> _2Tuple[float64]: ...
|
|
|
|
# keep in sync with __rmod__
|
|
@overload
|
|
def __rdivmod__(self, other: _RealNumberT, /) -> _2Tuple[_RealNumberT]: ...
|
|
@overload
|
|
def __rdivmod__(self, other: builtins.bool, /) -> _2Tuple[int8]: ...
|
|
@overload
|
|
def __rdivmod__(self, other: int, /) -> _2Tuple[int_]: ...
|
|
@overload
|
|
def __rdivmod__(self, other: float, /) -> _2Tuple[float64]: ...
|
|
|
|
@overload
|
|
def __lshift__(self, other: _IntegerT, /) -> _IntegerT: ...
|
|
@overload
|
|
def __lshift__(self, other: builtins.bool | bool_, /) -> int8: ...
|
|
@overload
|
|
def __lshift__(self, other: int, /) -> int_: ...
|
|
|
|
@overload
|
|
def __rlshift__(self, other: _IntegerT, /) -> _IntegerT: ...
|
|
@overload
|
|
def __rlshift__(self, other: builtins.bool, /) -> int8: ...
|
|
@overload
|
|
def __rlshift__(self, other: int, /) -> int_: ...
|
|
|
|
# keep in sync with __lshift__
|
|
@overload
|
|
def __rshift__(self, other: _IntegerT, /) -> _IntegerT: ...
|
|
@overload
|
|
def __rshift__(self, other: builtins.bool | bool_, /) -> int8: ...
|
|
@overload
|
|
def __rshift__(self, other: int, /) -> int_: ...
|
|
|
|
# keep in sync with __rlshift__
|
|
@overload
|
|
def __rrshift__(self, other: _IntegerT, /) -> _IntegerT: ...
|
|
@overload
|
|
def __rrshift__(self, other: builtins.bool, /) -> int8: ...
|
|
@overload
|
|
def __rrshift__(self, other: int, /) -> int_: ...
|
|
|
|
@overload
|
|
def __and__(self: np.bool[L[False]], other: builtins.bool | np.bool, /) -> np.bool[L[False]]: ...
|
|
@overload
|
|
def __and__(self, other: L[False] | np.bool[L[False]], /) -> np.bool[L[False]]: ...
|
|
@overload
|
|
def __and__(self, other: L[True] | np.bool[L[True]], /) -> Self: ...
|
|
@overload
|
|
def __and__(self, other: builtins.bool | np.bool, /) -> np.bool: ...
|
|
@overload
|
|
def __and__(self, other: _IntegerT, /) -> _IntegerT: ...
|
|
@overload
|
|
def __and__(self, other: int, /) -> np.bool | intp: ...
|
|
__rand__ = __and__
|
|
|
|
@overload
|
|
def __xor__(self: np.bool[L[False]], other: _BoolItemT | np.bool[_BoolItemT], /) -> np.bool[_BoolItemT]: ...
|
|
@overload
|
|
def __xor__(self: np.bool[L[True]], other: L[True] | np.bool[L[True]], /) -> np.bool[L[False]]: ...
|
|
@overload
|
|
def __xor__(self, other: L[False] | np.bool[L[False]], /) -> Self: ...
|
|
@overload
|
|
def __xor__(self, other: builtins.bool | np.bool, /) -> np.bool: ...
|
|
@overload
|
|
def __xor__(self, other: _IntegerT, /) -> _IntegerT: ...
|
|
@overload
|
|
def __xor__(self, other: int, /) -> np.bool | intp: ...
|
|
__rxor__ = __xor__
|
|
|
|
@overload
|
|
def __or__(self: np.bool[L[True]], other: builtins.bool | np.bool, /) -> np.bool[L[True]]: ...
|
|
@overload
|
|
def __or__(self, other: L[False] | np.bool[L[False]], /) -> Self: ...
|
|
@overload
|
|
def __or__(self, other: L[True] | np.bool[L[True]], /) -> np.bool[L[True]]: ...
|
|
@overload
|
|
def __or__(self, other: builtins.bool | np.bool, /) -> np.bool: ...
|
|
@overload
|
|
def __or__(self, other: _IntegerT, /) -> _IntegerT: ...
|
|
@overload
|
|
def __or__(self, other: int, /) -> np.bool | intp: ...
|
|
__ror__ = __or__
|
|
|
|
__lt__: _ComparisonOpLT[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__le__: _ComparisonOpLE[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__gt__: _ComparisonOpGT[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__ge__: _ComparisonOpGE[_NumberLike_co, _ArrayLikeNumber_co]
|
|
|
|
# NOTE: This should _not_ be `Final` or a `TypeAlias`
|
|
bool_ = bool
|
|
|
|
# NOTE: The `object_` constructor returns the passed object, so instances with type
|
|
# `object_` cannot exists (at runtime).
|
|
# NOTE: Because mypy has some long-standing bugs related to `__new__`, `object_` can't
|
|
# be made generic.
|
|
@final
|
|
class object_(_RealMixin, generic):
|
|
@overload
|
|
def __new__(cls, nothing_to_see_here: None = None, /) -> None: ... # type: ignore[misc]
|
|
@overload
|
|
def __new__(cls, stringy: _AnyStr, /) -> _AnyStr: ... # type: ignore[misc]
|
|
@overload
|
|
def __new__(cls, array: ndarray[_ShapeT, Any], /) -> ndarray[_ShapeT, dtype[Self]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __new__(cls, sequence: SupportsLenAndGetItem[object], /) -> NDArray[Self]: ... # type: ignore[misc]
|
|
@overload
|
|
def __new__(cls, value: _T, /) -> _T: ... # type: ignore[misc]
|
|
@overload # catch-all
|
|
def __new__(cls, value: Any = ..., /) -> object | NDArray[Self]: ... # type: ignore[misc]
|
|
def __init__(self, value: object = ..., /) -> None: ...
|
|
def __hash__(self, /) -> int: ...
|
|
def __abs__(self, /) -> object_: ... # this affects NDArray[object_].__abs__
|
|
def __call__(self, /, *args: object, **kwargs: object) -> Any: ...
|
|
|
|
if sys.version_info >= (3, 12):
|
|
def __release_buffer__(self, buffer: memoryview, /) -> None: ...
|
|
|
|
class integer(_IntegralMixin, _RoundMixin, number[_NBit, int]):
|
|
@abstractmethod
|
|
def __init__(self, value: _ConvertibleToInt = ..., /) -> None: ...
|
|
|
|
# NOTE: `bit_count` and `__index__` are technically defined in the concrete subtypes
|
|
def bit_count(self, /) -> int: ...
|
|
def __index__(self, /) -> int: ...
|
|
def __invert__(self, /) -> Self: ...
|
|
|
|
__truediv__: _IntTrueDiv[_NBit]
|
|
__rtruediv__: _IntTrueDiv[_NBit]
|
|
def __mod__(self, value: _IntLike_co, /) -> integer: ...
|
|
def __rmod__(self, value: _IntLike_co, /) -> integer: ...
|
|
# Ensure that objects annotated as `integer` support bit-wise operations
|
|
def __lshift__(self, other: _IntLike_co, /) -> integer: ...
|
|
def __rlshift__(self, other: _IntLike_co, /) -> integer: ...
|
|
def __rshift__(self, other: _IntLike_co, /) -> integer: ...
|
|
def __rrshift__(self, other: _IntLike_co, /) -> integer: ...
|
|
def __and__(self, other: _IntLike_co, /) -> integer: ...
|
|
def __rand__(self, other: _IntLike_co, /) -> integer: ...
|
|
def __or__(self, other: _IntLike_co, /) -> integer: ...
|
|
def __ror__(self, other: _IntLike_co, /) -> integer: ...
|
|
def __xor__(self, other: _IntLike_co, /) -> integer: ...
|
|
def __rxor__(self, other: _IntLike_co, /) -> integer: ...
|
|
|
|
class signedinteger(integer[_NBit1]):
|
|
def __init__(self, value: _ConvertibleToInt = ..., /) -> None: ...
|
|
|
|
__add__: _SignedIntOp[_NBit1]
|
|
__radd__: _SignedIntOp[_NBit1]
|
|
__sub__: _SignedIntOp[_NBit1]
|
|
__rsub__: _SignedIntOp[_NBit1]
|
|
__mul__: _SignedIntOp[_NBit1]
|
|
__rmul__: _SignedIntOp[_NBit1]
|
|
__floordiv__: _SignedIntOp[_NBit1]
|
|
__rfloordiv__: _SignedIntOp[_NBit1]
|
|
__pow__: _SignedIntOp[_NBit1]
|
|
__rpow__: _SignedIntOp[_NBit1]
|
|
__lshift__: _SignedIntBitOp[_NBit1]
|
|
__rlshift__: _SignedIntBitOp[_NBit1]
|
|
__rshift__: _SignedIntBitOp[_NBit1]
|
|
__rrshift__: _SignedIntBitOp[_NBit1]
|
|
__and__: _SignedIntBitOp[_NBit1]
|
|
__rand__: _SignedIntBitOp[_NBit1]
|
|
__xor__: _SignedIntBitOp[_NBit1]
|
|
__rxor__: _SignedIntBitOp[_NBit1]
|
|
__or__: _SignedIntBitOp[_NBit1]
|
|
__ror__: _SignedIntBitOp[_NBit1]
|
|
__mod__: _SignedIntMod[_NBit1]
|
|
__rmod__: _SignedIntMod[_NBit1]
|
|
__divmod__: _SignedIntDivMod[_NBit1]
|
|
__rdivmod__: _SignedIntDivMod[_NBit1]
|
|
|
|
int8 = signedinteger[_8Bit]
|
|
int16 = signedinteger[_16Bit]
|
|
int32 = signedinteger[_32Bit]
|
|
int64 = signedinteger[_64Bit]
|
|
|
|
byte = signedinteger[_NBitByte]
|
|
short = signedinteger[_NBitShort]
|
|
intc = signedinteger[_NBitIntC]
|
|
intp = signedinteger[_NBitIntP]
|
|
int_ = intp
|
|
long = signedinteger[_NBitLong]
|
|
longlong = signedinteger[_NBitLongLong]
|
|
|
|
class unsignedinteger(integer[_NBit1]):
|
|
# NOTE: `uint64 + signedinteger -> float64`
|
|
def __init__(self, value: _ConvertibleToInt = ..., /) -> None: ...
|
|
|
|
__add__: _UnsignedIntOp[_NBit1]
|
|
__radd__: _UnsignedIntOp[_NBit1]
|
|
__sub__: _UnsignedIntOp[_NBit1]
|
|
__rsub__: _UnsignedIntOp[_NBit1]
|
|
__mul__: _UnsignedIntOp[_NBit1]
|
|
__rmul__: _UnsignedIntOp[_NBit1]
|
|
__floordiv__: _UnsignedIntOp[_NBit1]
|
|
__rfloordiv__: _UnsignedIntOp[_NBit1]
|
|
__pow__: _UnsignedIntOp[_NBit1]
|
|
__rpow__: _UnsignedIntOp[_NBit1]
|
|
__lshift__: _UnsignedIntBitOp[_NBit1]
|
|
__rlshift__: _UnsignedIntBitOp[_NBit1]
|
|
__rshift__: _UnsignedIntBitOp[_NBit1]
|
|
__rrshift__: _UnsignedIntBitOp[_NBit1]
|
|
__and__: _UnsignedIntBitOp[_NBit1]
|
|
__rand__: _UnsignedIntBitOp[_NBit1]
|
|
__xor__: _UnsignedIntBitOp[_NBit1]
|
|
__rxor__: _UnsignedIntBitOp[_NBit1]
|
|
__or__: _UnsignedIntBitOp[_NBit1]
|
|
__ror__: _UnsignedIntBitOp[_NBit1]
|
|
__mod__: _UnsignedIntMod[_NBit1]
|
|
__rmod__: _UnsignedIntMod[_NBit1]
|
|
__divmod__: _UnsignedIntDivMod[_NBit1]
|
|
__rdivmod__: _UnsignedIntDivMod[_NBit1]
|
|
|
|
uint8: TypeAlias = unsignedinteger[_8Bit]
|
|
uint16: TypeAlias = unsignedinteger[_16Bit]
|
|
uint32: TypeAlias = unsignedinteger[_32Bit]
|
|
uint64: TypeAlias = unsignedinteger[_64Bit]
|
|
|
|
ubyte: TypeAlias = unsignedinteger[_NBitByte]
|
|
ushort: TypeAlias = unsignedinteger[_NBitShort]
|
|
uintc: TypeAlias = unsignedinteger[_NBitIntC]
|
|
uintp: TypeAlias = unsignedinteger[_NBitIntP]
|
|
uint: TypeAlias = uintp
|
|
ulong: TypeAlias = unsignedinteger[_NBitLong]
|
|
ulonglong: TypeAlias = unsignedinteger[_NBitLongLong]
|
|
|
|
class inexact(number[_NBit, _InexactItemT_co], Generic[_NBit, _InexactItemT_co]):
|
|
@abstractmethod
|
|
def __init__(self, value: _InexactItemT_co | None = ..., /) -> None: ...
|
|
|
|
class floating(_RealMixin, _RoundMixin, inexact[_NBit1, float]):
|
|
def __init__(self, value: _ConvertibleToFloat | None = ..., /) -> None: ...
|
|
|
|
__add__: _FloatOp[_NBit1]
|
|
__radd__: _FloatOp[_NBit1]
|
|
__sub__: _FloatOp[_NBit1]
|
|
__rsub__: _FloatOp[_NBit1]
|
|
__mul__: _FloatOp[_NBit1]
|
|
__rmul__: _FloatOp[_NBit1]
|
|
__truediv__: _FloatOp[_NBit1]
|
|
__rtruediv__: _FloatOp[_NBit1]
|
|
__floordiv__: _FloatOp[_NBit1]
|
|
__rfloordiv__: _FloatOp[_NBit1]
|
|
__pow__: _FloatOp[_NBit1]
|
|
__rpow__: _FloatOp[_NBit1]
|
|
__mod__: _FloatMod[_NBit1]
|
|
__rmod__: _FloatMod[_NBit1]
|
|
__divmod__: _FloatDivMod[_NBit1]
|
|
__rdivmod__: _FloatDivMod[_NBit1]
|
|
|
|
# NOTE: `is_integer` and `as_integer_ratio` are technically defined in the concrete subtypes
|
|
def is_integer(self, /) -> builtins.bool: ...
|
|
def as_integer_ratio(self, /) -> tuple[int, int]: ...
|
|
|
|
float16: TypeAlias = floating[_16Bit]
|
|
float32: TypeAlias = floating[_32Bit]
|
|
|
|
# either a C `double`, `float`, or `longdouble`
|
|
class float64(floating[_64Bit], float): # type: ignore[misc]
|
|
def __new__(cls, x: _ConvertibleToFloat | None = ..., /) -> Self: ...
|
|
|
|
#
|
|
@property
|
|
def itemsize(self) -> L[8]: ...
|
|
@property
|
|
def nbytes(self) -> L[8]: ...
|
|
|
|
# overrides for `floating` and `builtins.float` compatibility (`_RealMixin` doesn't work)
|
|
@property
|
|
def real(self) -> Self: ...
|
|
@property
|
|
def imag(self) -> Self: ...
|
|
def conjugate(self) -> Self: ...
|
|
def __getformat__(self, typestr: L["double", "float"], /) -> str: ...
|
|
def __getnewargs__(self, /) -> tuple[float]: ...
|
|
|
|
# float64-specific operator overrides
|
|
@overload
|
|
def __add__(self, other: _Float64_co, /) -> float64: ...
|
|
@overload
|
|
def __add__(self, other: complexfloating[_64Bit, _64Bit], /) -> complex128: ...
|
|
@overload
|
|
def __add__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __add__(self, other: complex, /) -> float64 | complex128: ...
|
|
@overload
|
|
def __radd__(self, other: _Float64_co, /) -> float64: ...
|
|
@overload
|
|
def __radd__(self, other: complexfloating[_64Bit, _64Bit], /) -> complex128: ...
|
|
@overload
|
|
def __radd__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __radd__(self, other: complex, /) -> float64 | complex128: ...
|
|
|
|
@overload
|
|
def __sub__(self, other: _Float64_co, /) -> float64: ...
|
|
@overload
|
|
def __sub__(self, other: complexfloating[_64Bit, _64Bit], /) -> complex128: ...
|
|
@overload
|
|
def __sub__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __sub__(self, other: complex, /) -> float64 | complex128: ...
|
|
@overload
|
|
def __rsub__(self, other: _Float64_co, /) -> float64: ...
|
|
@overload
|
|
def __rsub__(self, other: complexfloating[_64Bit, _64Bit], /) -> complex128: ...
|
|
@overload
|
|
def __rsub__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __rsub__(self, other: complex, /) -> float64 | complex128: ...
|
|
|
|
@overload
|
|
def __mul__(self, other: _Float64_co, /) -> float64: ...
|
|
@overload
|
|
def __mul__(self, other: complexfloating[_64Bit, _64Bit], /) -> complex128: ...
|
|
@overload
|
|
def __mul__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __mul__(self, other: complex, /) -> float64 | complex128: ...
|
|
@overload
|
|
def __rmul__(self, other: _Float64_co, /) -> float64: ...
|
|
@overload
|
|
def __rmul__(self, other: complexfloating[_64Bit, _64Bit], /) -> complex128: ...
|
|
@overload
|
|
def __rmul__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __rmul__(self, other: complex, /) -> float64 | complex128: ...
|
|
|
|
@overload
|
|
def __truediv__(self, other: _Float64_co, /) -> float64: ...
|
|
@overload
|
|
def __truediv__(self, other: complexfloating[_64Bit, _64Bit], /) -> complex128: ...
|
|
@overload
|
|
def __truediv__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __truediv__(self, other: complex, /) -> float64 | complex128: ...
|
|
@overload
|
|
def __rtruediv__(self, other: _Float64_co, /) -> float64: ...
|
|
@overload
|
|
def __rtruediv__(self, other: complexfloating[_64Bit, _64Bit], /) -> complex128: ...
|
|
@overload
|
|
def __rtruediv__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __rtruediv__(self, other: complex, /) -> float64 | complex128: ...
|
|
|
|
@overload
|
|
def __floordiv__(self, other: _Float64_co, /) -> float64: ...
|
|
@overload
|
|
def __floordiv__(self, other: complexfloating[_64Bit, _64Bit], /) -> complex128: ...
|
|
@overload
|
|
def __floordiv__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __floordiv__(self, other: complex, /) -> float64 | complex128: ...
|
|
@overload
|
|
def __rfloordiv__(self, other: _Float64_co, /) -> float64: ...
|
|
@overload
|
|
def __rfloordiv__(self, other: complexfloating[_64Bit, _64Bit], /) -> complex128: ...
|
|
@overload
|
|
def __rfloordiv__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __rfloordiv__(self, other: complex, /) -> float64 | complex128: ...
|
|
|
|
@overload
|
|
def __pow__(self, other: _Float64_co, mod: None = None, /) -> float64: ...
|
|
@overload
|
|
def __pow__(self, other: complexfloating[_64Bit, _64Bit], mod: None = None, /) -> complex128: ...
|
|
@overload
|
|
def __pow__(
|
|
self, other: complexfloating[_NBit1, _NBit2], mod: None = None, /
|
|
) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __pow__(self, other: complex, mod: None = None, /) -> float64 | complex128: ...
|
|
@overload
|
|
def __rpow__(self, other: _Float64_co, mod: None = None, /) -> float64: ...
|
|
@overload
|
|
def __rpow__(self, other: complexfloating[_64Bit, _64Bit], mod: None = None, /) -> complex128: ...
|
|
@overload
|
|
def __rpow__(
|
|
self, other: complexfloating[_NBit1, _NBit2], mod: None = None, /
|
|
) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
@overload
|
|
def __rpow__(self, other: complex, mod: None = None, /) -> float64 | complex128: ...
|
|
|
|
def __mod__(self, other: _Float64_co, /) -> float64: ... # type: ignore[override]
|
|
def __rmod__(self, other: _Float64_co, /) -> float64: ... # type: ignore[override]
|
|
|
|
def __divmod__(self, other: _Float64_co, /) -> _2Tuple[float64]: ... # type: ignore[override]
|
|
def __rdivmod__(self, other: _Float64_co, /) -> _2Tuple[float64]: ... # type: ignore[override]
|
|
|
|
half: TypeAlias = floating[_NBitHalf]
|
|
single: TypeAlias = floating[_NBitSingle]
|
|
double: TypeAlias = floating[_NBitDouble]
|
|
longdouble: TypeAlias = floating[_NBitLongDouble]
|
|
|
|
# The main reason for `complexfloating` having two typevars is cosmetic.
|
|
# It is used to clarify why `complex128`s precision is `_64Bit`, the latter
|
|
# describing the two 64 bit floats representing its real and imaginary component
|
|
|
|
class complexfloating(inexact[_NBit1, complex], Generic[_NBit1, _NBit2]):
|
|
@overload
|
|
def __init__(
|
|
self,
|
|
real: complex | SupportsComplex | SupportsFloat | SupportsIndex = ...,
|
|
imag: complex | SupportsFloat | SupportsIndex = ...,
|
|
/,
|
|
) -> None: ...
|
|
@overload
|
|
def __init__(self, real: _ConvertibleToComplex | None = ..., /) -> None: ...
|
|
|
|
@property
|
|
def real(self) -> floating[_NBit1]: ... # type: ignore[override]
|
|
@property
|
|
def imag(self) -> floating[_NBit2]: ... # type: ignore[override]
|
|
|
|
# NOTE: `__complex__` is technically defined in the concrete subtypes
|
|
def __complex__(self, /) -> complex: ...
|
|
def __abs__(self, /) -> floating[_NBit1 | _NBit2]: ... # type: ignore[override]
|
|
|
|
@overload
|
|
def __add__(self, other: _Complex64_co, /) -> complexfloating[_NBit1, _NBit2]: ...
|
|
@overload
|
|
def __add__(self, other: complex | float64 | complex128, /) -> complexfloating[_NBit1, _NBit2] | complex128: ...
|
|
@overload
|
|
def __add__(self, other: number[_NBit], /) -> complexfloating[_NBit1, _NBit2] | complexfloating[_NBit, _NBit]: ...
|
|
@overload
|
|
def __radd__(self, other: _Complex64_co, /) -> complexfloating[_NBit1, _NBit2]: ...
|
|
@overload
|
|
def __radd__(self, other: complex, /) -> complexfloating[_NBit1, _NBit2] | complex128: ...
|
|
@overload
|
|
def __radd__(self, other: number[_NBit], /) -> complexfloating[_NBit1, _NBit2] | complexfloating[_NBit, _NBit]: ...
|
|
|
|
@overload
|
|
def __sub__(self, other: _Complex64_co, /) -> complexfloating[_NBit1, _NBit2]: ...
|
|
@overload
|
|
def __sub__(self, other: complex | float64 | complex128, /) -> complexfloating[_NBit1, _NBit2] | complex128: ...
|
|
@overload
|
|
def __sub__(self, other: number[_NBit], /) -> complexfloating[_NBit1, _NBit2] | complexfloating[_NBit, _NBit]: ...
|
|
@overload
|
|
def __rsub__(self, other: _Complex64_co, /) -> complexfloating[_NBit1, _NBit2]: ...
|
|
@overload
|
|
def __rsub__(self, other: complex, /) -> complexfloating[_NBit1, _NBit2] | complex128: ...
|
|
@overload
|
|
def __rsub__(self, other: number[_NBit], /) -> complexfloating[_NBit1, _NBit2] | complexfloating[_NBit, _NBit]: ...
|
|
|
|
@overload
|
|
def __mul__(self, other: _Complex64_co, /) -> complexfloating[_NBit1, _NBit2]: ...
|
|
@overload
|
|
def __mul__(self, other: complex | float64 | complex128, /) -> complexfloating[_NBit1, _NBit2] | complex128: ...
|
|
@overload
|
|
def __mul__(self, other: number[_NBit], /) -> complexfloating[_NBit1, _NBit2] | complexfloating[_NBit, _NBit]: ...
|
|
@overload
|
|
def __rmul__(self, other: _Complex64_co, /) -> complexfloating[_NBit1, _NBit2]: ...
|
|
@overload
|
|
def __rmul__(self, other: complex, /) -> complexfloating[_NBit1, _NBit2] | complex128: ...
|
|
@overload
|
|
def __rmul__(self, other: number[_NBit], /) -> complexfloating[_NBit1, _NBit2] | complexfloating[_NBit, _NBit]: ...
|
|
|
|
@overload
|
|
def __truediv__(self, other: _Complex64_co, /) -> complexfloating[_NBit1, _NBit2]: ...
|
|
@overload
|
|
def __truediv__(self, other: complex | float64 | complex128, /) -> complexfloating[_NBit1, _NBit2] | complex128: ...
|
|
@overload
|
|
def __truediv__(self, other: number[_NBit], /) -> complexfloating[_NBit1, _NBit2] | complexfloating[_NBit, _NBit]: ...
|
|
@overload
|
|
def __rtruediv__(self, other: _Complex64_co, /) -> complexfloating[_NBit1, _NBit2]: ...
|
|
@overload
|
|
def __rtruediv__(self, other: complex, /) -> complexfloating[_NBit1, _NBit2] | complex128: ...
|
|
@overload
|
|
def __rtruediv__(self, other: number[_NBit], /) -> complexfloating[_NBit1, _NBit2] | complexfloating[_NBit, _NBit]: ...
|
|
|
|
@overload
|
|
def __pow__(self, other: _Complex64_co, mod: None = None, /) -> complexfloating[_NBit1, _NBit2]: ...
|
|
@overload
|
|
def __pow__(
|
|
self, other: complex | float64 | complex128, mod: None = None, /
|
|
) -> complexfloating[_NBit1, _NBit2] | complex128: ...
|
|
@overload
|
|
def __pow__(
|
|
self, other: number[_NBit], mod: None = None, /
|
|
) -> complexfloating[_NBit1, _NBit2] | complexfloating[_NBit, _NBit]: ...
|
|
@overload
|
|
def __rpow__(self, other: _Complex64_co, mod: None = None, /) -> complexfloating[_NBit1, _NBit2]: ...
|
|
@overload
|
|
def __rpow__(self, other: complex, mod: None = None, /) -> complexfloating[_NBit1, _NBit2] | complex128: ...
|
|
@overload
|
|
def __rpow__(
|
|
self, other: number[_NBit], mod: None = None, /
|
|
) -> complexfloating[_NBit1, _NBit2] | complexfloating[_NBit, _NBit]: ...
|
|
|
|
complex64: TypeAlias = complexfloating[_32Bit, _32Bit]
|
|
|
|
class complex128(complexfloating[_64Bit, _64Bit], complex): # type: ignore[misc]
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
real: complex | SupportsComplex | SupportsFloat | SupportsIndex = ...,
|
|
imag: complex | SupportsFloat | SupportsIndex = ...,
|
|
/,
|
|
) -> Self: ...
|
|
@overload
|
|
def __new__(cls, real: _ConvertibleToComplex | None = ..., /) -> Self: ...
|
|
|
|
#
|
|
@property
|
|
def itemsize(self) -> L[16]: ...
|
|
@property
|
|
def nbytes(self) -> L[16]: ...
|
|
|
|
# overrides for `floating` and `builtins.float` compatibility
|
|
@property
|
|
def real(self) -> float64: ...
|
|
@property
|
|
def imag(self) -> float64: ...
|
|
def conjugate(self) -> Self: ...
|
|
def __abs__(self) -> float64: ... # type: ignore[override]
|
|
def __getnewargs__(self, /) -> tuple[float, float]: ...
|
|
|
|
# complex128-specific operator overrides
|
|
@overload
|
|
def __add__(self, other: _Complex128_co, /) -> complex128: ...
|
|
@overload
|
|
def __add__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
def __radd__(self, other: _Complex128_co, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __sub__(self, other: _Complex128_co, /) -> complex128: ...
|
|
@overload
|
|
def __sub__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
def __rsub__(self, other: _Complex128_co, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __mul__(self, other: _Complex128_co, /) -> complex128: ...
|
|
@overload
|
|
def __mul__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
def __rmul__(self, other: _Complex128_co, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __truediv__(self, other: _Complex128_co, /) -> complex128: ...
|
|
@overload
|
|
def __truediv__(self, other: complexfloating[_NBit1, _NBit2], /) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
def __rtruediv__(self, other: _Complex128_co, /) -> complex128: ...
|
|
|
|
@overload
|
|
def __pow__(self, other: _Complex128_co, mod: None = None, /) -> complex128: ...
|
|
@overload
|
|
def __pow__(
|
|
self, other: complexfloating[_NBit1, _NBit2], mod: None = None, /
|
|
) -> complexfloating[_NBit1 | _64Bit, _NBit2 | _64Bit]: ...
|
|
def __rpow__(self, other: _Complex128_co, mod: None = None, /) -> complex128: ...
|
|
|
|
csingle: TypeAlias = complexfloating[_NBitSingle, _NBitSingle]
|
|
cdouble: TypeAlias = complexfloating[_NBitDouble, _NBitDouble]
|
|
clongdouble: TypeAlias = complexfloating[_NBitLongDouble, _NBitLongDouble]
|
|
|
|
class timedelta64(_IntegralMixin, generic[_TD64ItemT_co], Generic[_TD64ItemT_co]):
|
|
@property
|
|
def itemsize(self) -> L[8]: ...
|
|
@property
|
|
def nbytes(self) -> L[8]: ...
|
|
|
|
@overload
|
|
def __init__(self, value: _TD64ItemT_co | timedelta64[_TD64ItemT_co], /) -> None: ...
|
|
@overload
|
|
def __init__(self: timedelta64[L[0]], /) -> None: ...
|
|
@overload
|
|
def __init__(self: timedelta64[None], value: _NaTValue | None, format: _TimeUnitSpec, /) -> None: ...
|
|
@overload
|
|
def __init__(self: timedelta64[L[0]], value: L[0], format: _TimeUnitSpec[_IntTD64Unit] = ..., /) -> None: ...
|
|
@overload
|
|
def __init__(self: timedelta64[int], value: _IntLike_co, format: _TimeUnitSpec[_IntTD64Unit] = ..., /) -> None: ...
|
|
@overload
|
|
def __init__(self: timedelta64[int], value: dt.timedelta, format: _TimeUnitSpec[_IntTimeUnit], /) -> None: ...
|
|
@overload
|
|
def __init__(
|
|
self: timedelta64[dt.timedelta],
|
|
value: dt.timedelta | _IntLike_co,
|
|
format: _TimeUnitSpec[_NativeTD64Unit] = ...,
|
|
/,
|
|
) -> None: ...
|
|
@overload
|
|
def __init__(self, value: _ConvertibleToTD64, format: _TimeUnitSpec = ..., /) -> None: ...
|
|
|
|
# inherited at runtime from `signedinteger`
|
|
def __class_getitem__(cls, type_arg: type | object, /) -> GenericAlias: ...
|
|
|
|
# NOTE: Only a limited number of units support conversion
|
|
# to builtin scalar types: `Y`, `M`, `ns`, `ps`, `fs`, `as`
|
|
def __int__(self: timedelta64[int], /) -> int: ...
|
|
def __float__(self: timedelta64[int], /) -> float: ...
|
|
|
|
def __neg__(self, /) -> Self: ...
|
|
def __pos__(self, /) -> Self: ...
|
|
def __abs__(self, /) -> Self: ...
|
|
|
|
@overload
|
|
def __add__(self: timedelta64[None], x: _TD64Like_co, /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __add__(self: timedelta64[int], x: timedelta64[int | dt.timedelta], /) -> timedelta64[int]: ...
|
|
@overload
|
|
def __add__(self: timedelta64[int], x: timedelta64, /) -> timedelta64[int | None]: ...
|
|
@overload
|
|
def __add__(self: timedelta64[dt.timedelta], x: _AnyDateOrTime, /) -> _AnyDateOrTime: ...
|
|
@overload
|
|
def __add__(self: timedelta64[_AnyTD64Item], x: timedelta64[_AnyTD64Item] | _IntLike_co, /) -> timedelta64[_AnyTD64Item]: ...
|
|
@overload
|
|
def __add__(self, x: timedelta64[None], /) -> timedelta64[None]: ...
|
|
__radd__ = __add__
|
|
|
|
@overload
|
|
def __mul__(self: timedelta64[_AnyTD64Item], x: int | np.integer | np.bool, /) -> timedelta64[_AnyTD64Item]: ...
|
|
@overload
|
|
def __mul__(self: timedelta64[_AnyTD64Item], x: float | np.floating, /) -> timedelta64[_AnyTD64Item | None]: ...
|
|
@overload
|
|
def __mul__(self, x: float | np.floating | np.integer | np.bool, /) -> timedelta64: ...
|
|
__rmul__ = __mul__
|
|
|
|
@overload
|
|
def __mod__(self, x: timedelta64[L[0] | None], /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __mod__(self: timedelta64[None], x: timedelta64, /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __mod__(self: timedelta64[int], x: timedelta64[int | dt.timedelta], /) -> timedelta64[int | None]: ...
|
|
@overload
|
|
def __mod__(self: timedelta64[dt.timedelta], x: timedelta64[_AnyTD64Item], /) -> timedelta64[_AnyTD64Item | None]: ...
|
|
@overload
|
|
def __mod__(self: timedelta64[dt.timedelta], x: dt.timedelta, /) -> dt.timedelta: ...
|
|
@overload
|
|
def __mod__(self, x: timedelta64[int], /) -> timedelta64[int | None]: ...
|
|
@overload
|
|
def __mod__(self, x: timedelta64, /) -> timedelta64: ...
|
|
|
|
# the L[0] makes __mod__ non-commutative, which the first two overloads reflect
|
|
@overload
|
|
def __rmod__(self, x: timedelta64[None], /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __rmod__(self: timedelta64[L[0] | None], x: timedelta64, /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __rmod__(self: timedelta64[int], x: timedelta64[int | dt.timedelta], /) -> timedelta64[int | None]: ...
|
|
@overload
|
|
def __rmod__(self: timedelta64[dt.timedelta], x: timedelta64[_AnyTD64Item], /) -> timedelta64[_AnyTD64Item | None]: ...
|
|
@overload
|
|
def __rmod__(self: timedelta64[dt.timedelta], x: dt.timedelta, /) -> dt.timedelta: ...
|
|
@overload
|
|
def __rmod__(self, x: timedelta64[int], /) -> timedelta64[int | None]: ...
|
|
@overload
|
|
def __rmod__(self, x: timedelta64, /) -> timedelta64: ...
|
|
|
|
# keep in sync with __mod__
|
|
@overload
|
|
def __divmod__(self, x: timedelta64[L[0] | None], /) -> tuple[int64, timedelta64[None]]: ...
|
|
@overload
|
|
def __divmod__(self: timedelta64[None], x: timedelta64, /) -> tuple[int64, timedelta64[None]]: ...
|
|
@overload
|
|
def __divmod__(self: timedelta64[int], x: timedelta64[int | dt.timedelta], /) -> tuple[int64, timedelta64[int | None]]: ...
|
|
@overload
|
|
def __divmod__(self: timedelta64[dt.timedelta], x: timedelta64[_AnyTD64Item], /) -> tuple[int64, timedelta64[_AnyTD64Item | None]]: ...
|
|
@overload
|
|
def __divmod__(self: timedelta64[dt.timedelta], x: dt.timedelta, /) -> tuple[int, dt.timedelta]: ...
|
|
@overload
|
|
def __divmod__(self, x: timedelta64[int], /) -> tuple[int64, timedelta64[int | None]]: ...
|
|
@overload
|
|
def __divmod__(self, x: timedelta64, /) -> tuple[int64, timedelta64]: ...
|
|
|
|
# keep in sync with __rmod__
|
|
@overload
|
|
def __rdivmod__(self, x: timedelta64[None], /) -> tuple[int64, timedelta64[None]]: ...
|
|
@overload
|
|
def __rdivmod__(self: timedelta64[L[0] | None], x: timedelta64, /) -> tuple[int64, timedelta64[None]]: ...
|
|
@overload
|
|
def __rdivmod__(self: timedelta64[int], x: timedelta64[int | dt.timedelta], /) -> tuple[int64, timedelta64[int | None]]: ...
|
|
@overload
|
|
def __rdivmod__(self: timedelta64[dt.timedelta], x: timedelta64[_AnyTD64Item], /) -> tuple[int64, timedelta64[_AnyTD64Item | None]]: ...
|
|
@overload
|
|
def __rdivmod__(self: timedelta64[dt.timedelta], x: dt.timedelta, /) -> tuple[int, dt.timedelta]: ...
|
|
@overload
|
|
def __rdivmod__(self, x: timedelta64[int], /) -> tuple[int64, timedelta64[int | None]]: ...
|
|
@overload
|
|
def __rdivmod__(self, x: timedelta64, /) -> tuple[int64, timedelta64]: ...
|
|
|
|
@overload
|
|
def __sub__(self: timedelta64[None], b: _TD64Like_co, /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __sub__(self: timedelta64[int], b: timedelta64[int | dt.timedelta], /) -> timedelta64[int]: ...
|
|
@overload
|
|
def __sub__(self: timedelta64[int], b: timedelta64, /) -> timedelta64[int | None]: ...
|
|
@overload
|
|
def __sub__(self: timedelta64[dt.timedelta], b: dt.timedelta, /) -> dt.timedelta: ...
|
|
@overload
|
|
def __sub__(self: timedelta64[_AnyTD64Item], b: timedelta64[_AnyTD64Item] | _IntLike_co, /) -> timedelta64[_AnyTD64Item]: ...
|
|
@overload
|
|
def __sub__(self, b: timedelta64[None], /) -> timedelta64[None]: ...
|
|
|
|
@overload
|
|
def __rsub__(self: timedelta64[None], a: _TD64Like_co, /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __rsub__(self: timedelta64[dt.timedelta], a: _AnyDateOrTime, /) -> _AnyDateOrTime: ...
|
|
@overload
|
|
def __rsub__(self: timedelta64[dt.timedelta], a: timedelta64[_AnyTD64Item], /) -> timedelta64[_AnyTD64Item]: ...
|
|
@overload
|
|
def __rsub__(self: timedelta64[_AnyTD64Item], a: timedelta64[_AnyTD64Item] | _IntLike_co, /) -> timedelta64[_AnyTD64Item]: ...
|
|
@overload
|
|
def __rsub__(self, a: timedelta64[None], /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __rsub__(self, a: datetime64[None], /) -> datetime64[None]: ...
|
|
|
|
@overload
|
|
def __truediv__(self: timedelta64[dt.timedelta], b: dt.timedelta, /) -> float: ...
|
|
@overload
|
|
def __truediv__(self, b: timedelta64, /) -> float64: ...
|
|
@overload
|
|
def __truediv__(self: timedelta64[_AnyTD64Item], b: int | integer, /) -> timedelta64[_AnyTD64Item]: ...
|
|
@overload
|
|
def __truediv__(self: timedelta64[_AnyTD64Item], b: float | floating, /) -> timedelta64[_AnyTD64Item | None]: ...
|
|
@overload
|
|
def __truediv__(self, b: float | floating | integer, /) -> timedelta64: ...
|
|
@overload
|
|
def __rtruediv__(self: timedelta64[dt.timedelta], a: dt.timedelta, /) -> float: ...
|
|
@overload
|
|
def __rtruediv__(self, a: timedelta64, /) -> float64: ...
|
|
|
|
@overload
|
|
def __floordiv__(self: timedelta64[dt.timedelta], b: dt.timedelta, /) -> int: ...
|
|
@overload
|
|
def __floordiv__(self, b: timedelta64, /) -> int64: ...
|
|
@overload
|
|
def __floordiv__(self: timedelta64[_AnyTD64Item], b: int | integer, /) -> timedelta64[_AnyTD64Item]: ...
|
|
@overload
|
|
def __floordiv__(self: timedelta64[_AnyTD64Item], b: float | floating, /) -> timedelta64[_AnyTD64Item | None]: ...
|
|
@overload
|
|
def __rfloordiv__(self: timedelta64[dt.timedelta], a: dt.timedelta, /) -> int: ...
|
|
@overload
|
|
def __rfloordiv__(self, a: timedelta64, /) -> int64: ...
|
|
|
|
__lt__: _ComparisonOpLT[_TD64Like_co, _ArrayLikeTD64_co]
|
|
__le__: _ComparisonOpLE[_TD64Like_co, _ArrayLikeTD64_co]
|
|
__gt__: _ComparisonOpGT[_TD64Like_co, _ArrayLikeTD64_co]
|
|
__ge__: _ComparisonOpGE[_TD64Like_co, _ArrayLikeTD64_co]
|
|
|
|
class datetime64(_RealMixin, generic[_DT64ItemT_co], Generic[_DT64ItemT_co]):
|
|
@property
|
|
def itemsize(self) -> L[8]: ...
|
|
@property
|
|
def nbytes(self) -> L[8]: ...
|
|
|
|
@overload
|
|
def __init__(self, value: datetime64[_DT64ItemT_co], /) -> None: ...
|
|
@overload
|
|
def __init__(self: datetime64[_AnyDT64Arg], value: _AnyDT64Arg, /) -> None: ...
|
|
@overload
|
|
def __init__(self: datetime64[None], value: _NaTValue | None = ..., format: _TimeUnitSpec = ..., /) -> None: ...
|
|
@overload
|
|
def __init__(self: datetime64[dt.datetime], value: _DT64Now, format: _TimeUnitSpec[_NativeTimeUnit] = ..., /) -> None: ...
|
|
@overload
|
|
def __init__(self: datetime64[dt.date], value: _DT64Date, format: _TimeUnitSpec[_DateUnit] = ..., /) -> None: ...
|
|
@overload
|
|
def __init__(self: datetime64[int], value: int | bytes | str | dt.date, format: _TimeUnitSpec[_IntTimeUnit], /) -> None: ...
|
|
@overload
|
|
def __init__(
|
|
self: datetime64[dt.datetime], value: int | bytes | str | dt.date, format: _TimeUnitSpec[_NativeTimeUnit], /
|
|
) -> None: ...
|
|
@overload
|
|
def __init__(self: datetime64[dt.date], value: int | bytes | str | dt.date, format: _TimeUnitSpec[_DateUnit], /) -> None: ...
|
|
@overload
|
|
def __init__(self, value: bytes | str | dt.date | None, format: _TimeUnitSpec = ..., /) -> None: ...
|
|
|
|
@overload
|
|
def __add__(self: datetime64[_AnyDT64Item], x: int | integer | np.bool, /) -> datetime64[_AnyDT64Item]: ...
|
|
@overload
|
|
def __add__(self: datetime64[None], x: _TD64Like_co, /) -> datetime64[None]: ...
|
|
@overload
|
|
def __add__(self: datetime64[int], x: timedelta64[int | dt.timedelta], /) -> datetime64[int]: ...
|
|
@overload
|
|
def __add__(self: datetime64[dt.datetime], x: timedelta64[dt.timedelta], /) -> datetime64[dt.datetime]: ...
|
|
@overload
|
|
def __add__(self: datetime64[dt.date], x: timedelta64[dt.timedelta], /) -> datetime64[dt.date]: ...
|
|
@overload
|
|
def __add__(self: datetime64[dt.date], x: timedelta64[int], /) -> datetime64[int]: ...
|
|
@overload
|
|
def __add__(self, x: datetime64[None], /) -> datetime64[None]: ...
|
|
@overload
|
|
def __add__(self, x: _TD64Like_co, /) -> datetime64: ...
|
|
__radd__ = __add__
|
|
|
|
@overload
|
|
def __sub__(self: datetime64[_AnyDT64Item], x: int | integer | np.bool, /) -> datetime64[_AnyDT64Item]: ...
|
|
@overload
|
|
def __sub__(self: datetime64[_AnyDate], x: _AnyDate, /) -> dt.timedelta: ...
|
|
@overload
|
|
def __sub__(self: datetime64[None], x: timedelta64, /) -> datetime64[None]: ...
|
|
@overload
|
|
def __sub__(self: datetime64[None], x: datetime64, /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __sub__(self: datetime64[int], x: timedelta64, /) -> datetime64[int]: ...
|
|
@overload
|
|
def __sub__(self: datetime64[int], x: datetime64, /) -> timedelta64[int]: ...
|
|
@overload
|
|
def __sub__(self: datetime64[dt.datetime], x: timedelta64[int], /) -> datetime64[int]: ...
|
|
@overload
|
|
def __sub__(self: datetime64[dt.datetime], x: timedelta64[dt.timedelta], /) -> datetime64[dt.datetime]: ...
|
|
@overload
|
|
def __sub__(self: datetime64[dt.datetime], x: datetime64[int], /) -> timedelta64[int]: ...
|
|
@overload
|
|
def __sub__(self: datetime64[dt.date], x: timedelta64[int], /) -> datetime64[dt.date | int]: ...
|
|
@overload
|
|
def __sub__(self: datetime64[dt.date], x: timedelta64[dt.timedelta], /) -> datetime64[dt.date]: ...
|
|
@overload
|
|
def __sub__(self: datetime64[dt.date], x: datetime64[dt.date], /) -> timedelta64[dt.timedelta]: ...
|
|
@overload
|
|
def __sub__(self, x: timedelta64[None], /) -> datetime64[None]: ...
|
|
@overload
|
|
def __sub__(self, x: datetime64[None], /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __sub__(self, x: _TD64Like_co, /) -> datetime64: ...
|
|
@overload
|
|
def __sub__(self, x: datetime64, /) -> timedelta64: ...
|
|
|
|
@overload
|
|
def __rsub__(self: datetime64[_AnyDT64Item], x: int | integer | np.bool, /) -> datetime64[_AnyDT64Item]: ...
|
|
@overload
|
|
def __rsub__(self: datetime64[_AnyDate], x: _AnyDate, /) -> dt.timedelta: ...
|
|
@overload
|
|
def __rsub__(self: datetime64[None], x: datetime64, /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __rsub__(self: datetime64[int], x: datetime64, /) -> timedelta64[int]: ...
|
|
@overload
|
|
def __rsub__(self: datetime64[dt.datetime], x: datetime64[int], /) -> timedelta64[int]: ...
|
|
@overload
|
|
def __rsub__(self: datetime64[dt.datetime], x: datetime64[dt.date], /) -> timedelta64[dt.timedelta]: ...
|
|
@overload
|
|
def __rsub__(self, x: datetime64[None], /) -> timedelta64[None]: ...
|
|
@overload
|
|
def __rsub__(self, x: datetime64, /) -> timedelta64: ...
|
|
|
|
__lt__: _ComparisonOpLT[datetime64, _ArrayLikeDT64_co]
|
|
__le__: _ComparisonOpLE[datetime64, _ArrayLikeDT64_co]
|
|
__gt__: _ComparisonOpGT[datetime64, _ArrayLikeDT64_co]
|
|
__ge__: _ComparisonOpGE[datetime64, _ArrayLikeDT64_co]
|
|
|
|
class flexible(_RealMixin, generic[_FlexibleItemT_co], Generic[_FlexibleItemT_co]): ...
|
|
|
|
class void(flexible[bytes | tuple[Any, ...]]):
|
|
@overload
|
|
def __init__(self, value: _IntLike_co | bytes, /, dtype: None = None) -> None: ...
|
|
@overload
|
|
def __init__(self, value: Any, /, dtype: _DTypeLikeVoid) -> None: ...
|
|
|
|
@overload
|
|
def __getitem__(self, key: str | SupportsIndex, /) -> Any: ...
|
|
@overload
|
|
def __getitem__(self, key: list[str], /) -> void: ...
|
|
def __setitem__(self, key: str | list[str] | SupportsIndex, value: ArrayLike, /) -> None: ...
|
|
|
|
def setfield(self, val: ArrayLike, dtype: DTypeLike, offset: int = ...) -> None: ...
|
|
|
|
class character(flexible[_CharacterItemT_co], Generic[_CharacterItemT_co]):
|
|
@abstractmethod
|
|
def __init__(self, value: _CharacterItemT_co = ..., /) -> None: ...
|
|
|
|
# NOTE: Most `np.bytes_` / `np.str_` methods return their builtin `bytes` / `str` counterpart
|
|
|
|
class bytes_(character[bytes], bytes):
|
|
@overload
|
|
def __new__(cls, o: object = ..., /) -> Self: ...
|
|
@overload
|
|
def __new__(cls, s: str, /, encoding: str, errors: str = ...) -> Self: ...
|
|
|
|
#
|
|
@overload
|
|
def __init__(self, o: object = ..., /) -> None: ...
|
|
@overload
|
|
def __init__(self, s: str, /, encoding: str, errors: str = ...) -> None: ...
|
|
|
|
#
|
|
def __bytes__(self, /) -> bytes: ...
|
|
|
|
class str_(character[str], str):
|
|
@overload
|
|
def __new__(cls, value: object = ..., /) -> Self: ...
|
|
@overload
|
|
def __new__(cls, value: bytes, /, encoding: str = ..., errors: str = ...) -> Self: ...
|
|
|
|
#
|
|
@overload
|
|
def __init__(self, value: object = ..., /) -> None: ...
|
|
@overload
|
|
def __init__(self, value: bytes, /, encoding: str = ..., errors: str = ...) -> None: ...
|
|
|
|
# See `numpy._typing._ufunc` for more concrete nin-/nout-specific stubs
|
|
@final
|
|
class ufunc:
|
|
@property
|
|
def __name__(self) -> LiteralString: ...
|
|
@property
|
|
def __qualname__(self) -> LiteralString: ...
|
|
@property
|
|
def __doc__(self) -> str: ...
|
|
@property
|
|
def nin(self) -> int: ...
|
|
@property
|
|
def nout(self) -> int: ...
|
|
@property
|
|
def nargs(self) -> int: ...
|
|
@property
|
|
def ntypes(self) -> int: ...
|
|
@property
|
|
def types(self) -> list[LiteralString]: ...
|
|
# Broad return type because it has to encompass things like
|
|
#
|
|
# >>> np.logical_and.identity is True
|
|
# True
|
|
# >>> np.add.identity is 0
|
|
# True
|
|
# >>> np.sin.identity is None
|
|
# True
|
|
#
|
|
# and any user-defined ufuncs.
|
|
@property
|
|
def identity(self) -> Any: ...
|
|
# This is None for ufuncs and a string for gufuncs.
|
|
@property
|
|
def signature(self) -> LiteralString | None: ...
|
|
|
|
def __call__(self, *args: Any, **kwargs: Any) -> Any: ...
|
|
# The next four methods will always exist, but they will just
|
|
# raise a ValueError ufuncs with that don't accept two input
|
|
# arguments and return one output argument. Because of that we
|
|
# can't type them very precisely.
|
|
def reduce(self, /, *args: Any, **kwargs: Any) -> Any: ...
|
|
def accumulate(self, /, *args: Any, **kwargs: Any) -> NDArray[Any]: ...
|
|
def reduceat(self, /, *args: Any, **kwargs: Any) -> NDArray[Any]: ...
|
|
def outer(self, *args: Any, **kwargs: Any) -> Any: ...
|
|
# Similarly at won't be defined for ufuncs that return multiple
|
|
# outputs, so we can't type it very precisely.
|
|
def at(self, /, *args: Any, **kwargs: Any) -> None: ...
|
|
|
|
#
|
|
def resolve_dtypes(
|
|
self,
|
|
/,
|
|
dtypes: tuple[dtype | type | None, ...],
|
|
*,
|
|
signature: tuple[dtype | None, ...] | None = None,
|
|
casting: _CastingKind | None = None,
|
|
reduction: builtins.bool = False,
|
|
) -> tuple[dtype, ...]: ...
|
|
|
|
# Parameters: `__name__`, `ntypes` and `identity`
|
|
absolute: _UFunc_Nin1_Nout1[L['absolute'], L[20], None]
|
|
add: _UFunc_Nin2_Nout1[L['add'], L[22], L[0]]
|
|
arccos: _UFunc_Nin1_Nout1[L['arccos'], L[8], None]
|
|
arccosh: _UFunc_Nin1_Nout1[L['arccosh'], L[8], None]
|
|
arcsin: _UFunc_Nin1_Nout1[L['arcsin'], L[8], None]
|
|
arcsinh: _UFunc_Nin1_Nout1[L['arcsinh'], L[8], None]
|
|
arctan2: _UFunc_Nin2_Nout1[L['arctan2'], L[5], None]
|
|
arctan: _UFunc_Nin1_Nout1[L['arctan'], L[8], None]
|
|
arctanh: _UFunc_Nin1_Nout1[L['arctanh'], L[8], None]
|
|
bitwise_and: _UFunc_Nin2_Nout1[L['bitwise_and'], L[12], L[-1]]
|
|
bitwise_count: _UFunc_Nin1_Nout1[L['bitwise_count'], L[11], None]
|
|
bitwise_not: _UFunc_Nin1_Nout1[L['invert'], L[12], None]
|
|
bitwise_or: _UFunc_Nin2_Nout1[L['bitwise_or'], L[12], L[0]]
|
|
bitwise_xor: _UFunc_Nin2_Nout1[L['bitwise_xor'], L[12], L[0]]
|
|
cbrt: _UFunc_Nin1_Nout1[L['cbrt'], L[5], None]
|
|
ceil: _UFunc_Nin1_Nout1[L['ceil'], L[7], None]
|
|
conj: _UFunc_Nin1_Nout1[L['conjugate'], L[18], None]
|
|
conjugate: _UFunc_Nin1_Nout1[L['conjugate'], L[18], None]
|
|
copysign: _UFunc_Nin2_Nout1[L['copysign'], L[4], None]
|
|
cos: _UFunc_Nin1_Nout1[L['cos'], L[9], None]
|
|
cosh: _UFunc_Nin1_Nout1[L['cosh'], L[8], None]
|
|
deg2rad: _UFunc_Nin1_Nout1[L['deg2rad'], L[5], None]
|
|
degrees: _UFunc_Nin1_Nout1[L['degrees'], L[5], None]
|
|
divide: _UFunc_Nin2_Nout1[L['true_divide'], L[11], None]
|
|
divmod: _UFunc_Nin2_Nout2[L['divmod'], L[15], None]
|
|
equal: _UFunc_Nin2_Nout1[L['equal'], L[23], None]
|
|
exp2: _UFunc_Nin1_Nout1[L['exp2'], L[8], None]
|
|
exp: _UFunc_Nin1_Nout1[L['exp'], L[10], None]
|
|
expm1: _UFunc_Nin1_Nout1[L['expm1'], L[8], None]
|
|
fabs: _UFunc_Nin1_Nout1[L['fabs'], L[5], None]
|
|
float_power: _UFunc_Nin2_Nout1[L['float_power'], L[4], None]
|
|
floor: _UFunc_Nin1_Nout1[L['floor'], L[7], None]
|
|
floor_divide: _UFunc_Nin2_Nout1[L['floor_divide'], L[21], None]
|
|
fmax: _UFunc_Nin2_Nout1[L['fmax'], L[21], None]
|
|
fmin: _UFunc_Nin2_Nout1[L['fmin'], L[21], None]
|
|
fmod: _UFunc_Nin2_Nout1[L['fmod'], L[15], None]
|
|
frexp: _UFunc_Nin1_Nout2[L['frexp'], L[4], None]
|
|
gcd: _UFunc_Nin2_Nout1[L['gcd'], L[11], L[0]]
|
|
greater: _UFunc_Nin2_Nout1[L['greater'], L[23], None]
|
|
greater_equal: _UFunc_Nin2_Nout1[L['greater_equal'], L[23], None]
|
|
heaviside: _UFunc_Nin2_Nout1[L['heaviside'], L[4], None]
|
|
hypot: _UFunc_Nin2_Nout1[L['hypot'], L[5], L[0]]
|
|
invert: _UFunc_Nin1_Nout1[L['invert'], L[12], None]
|
|
isfinite: _UFunc_Nin1_Nout1[L['isfinite'], L[20], None]
|
|
isinf: _UFunc_Nin1_Nout1[L['isinf'], L[20], None]
|
|
isnan: _UFunc_Nin1_Nout1[L['isnan'], L[20], None]
|
|
isnat: _UFunc_Nin1_Nout1[L['isnat'], L[2], None]
|
|
lcm: _UFunc_Nin2_Nout1[L['lcm'], L[11], None]
|
|
ldexp: _UFunc_Nin2_Nout1[L['ldexp'], L[8], None]
|
|
left_shift: _UFunc_Nin2_Nout1[L['left_shift'], L[11], None]
|
|
less: _UFunc_Nin2_Nout1[L['less'], L[23], None]
|
|
less_equal: _UFunc_Nin2_Nout1[L['less_equal'], L[23], None]
|
|
log10: _UFunc_Nin1_Nout1[L['log10'], L[8], None]
|
|
log1p: _UFunc_Nin1_Nout1[L['log1p'], L[8], None]
|
|
log2: _UFunc_Nin1_Nout1[L['log2'], L[8], None]
|
|
log: _UFunc_Nin1_Nout1[L['log'], L[10], None]
|
|
logaddexp2: _UFunc_Nin2_Nout1[L['logaddexp2'], L[4], float]
|
|
logaddexp: _UFunc_Nin2_Nout1[L['logaddexp'], L[4], float]
|
|
logical_and: _UFunc_Nin2_Nout1[L['logical_and'], L[20], L[True]]
|
|
logical_not: _UFunc_Nin1_Nout1[L['logical_not'], L[20], None]
|
|
logical_or: _UFunc_Nin2_Nout1[L['logical_or'], L[20], L[False]]
|
|
logical_xor: _UFunc_Nin2_Nout1[L['logical_xor'], L[19], L[False]]
|
|
matmul: _GUFunc_Nin2_Nout1[L['matmul'], L[19], None, L["(n?,k),(k,m?)->(n?,m?)"]]
|
|
matvec: _GUFunc_Nin2_Nout1[L['matvec'], L[19], None, L["(m,n),(n)->(m)"]]
|
|
maximum: _UFunc_Nin2_Nout1[L['maximum'], L[21], None]
|
|
minimum: _UFunc_Nin2_Nout1[L['minimum'], L[21], None]
|
|
mod: _UFunc_Nin2_Nout1[L['remainder'], L[16], None]
|
|
modf: _UFunc_Nin1_Nout2[L['modf'], L[4], None]
|
|
multiply: _UFunc_Nin2_Nout1[L['multiply'], L[23], L[1]]
|
|
negative: _UFunc_Nin1_Nout1[L['negative'], L[19], None]
|
|
nextafter: _UFunc_Nin2_Nout1[L['nextafter'], L[4], None]
|
|
not_equal: _UFunc_Nin2_Nout1[L['not_equal'], L[23], None]
|
|
positive: _UFunc_Nin1_Nout1[L['positive'], L[19], None]
|
|
power: _UFunc_Nin2_Nout1[L['power'], L[18], None]
|
|
rad2deg: _UFunc_Nin1_Nout1[L['rad2deg'], L[5], None]
|
|
radians: _UFunc_Nin1_Nout1[L['radians'], L[5], None]
|
|
reciprocal: _UFunc_Nin1_Nout1[L['reciprocal'], L[18], None]
|
|
remainder: _UFunc_Nin2_Nout1[L['remainder'], L[16], None]
|
|
right_shift: _UFunc_Nin2_Nout1[L['right_shift'], L[11], None]
|
|
rint: _UFunc_Nin1_Nout1[L['rint'], L[10], None]
|
|
sign: _UFunc_Nin1_Nout1[L['sign'], L[19], None]
|
|
signbit: _UFunc_Nin1_Nout1[L['signbit'], L[4], None]
|
|
sin: _UFunc_Nin1_Nout1[L['sin'], L[9], None]
|
|
sinh: _UFunc_Nin1_Nout1[L['sinh'], L[8], None]
|
|
spacing: _UFunc_Nin1_Nout1[L['spacing'], L[4], None]
|
|
sqrt: _UFunc_Nin1_Nout1[L['sqrt'], L[10], None]
|
|
square: _UFunc_Nin1_Nout1[L['square'], L[18], None]
|
|
subtract: _UFunc_Nin2_Nout1[L['subtract'], L[21], None]
|
|
tan: _UFunc_Nin1_Nout1[L['tan'], L[8], None]
|
|
tanh: _UFunc_Nin1_Nout1[L['tanh'], L[8], None]
|
|
true_divide: _UFunc_Nin2_Nout1[L['true_divide'], L[11], None]
|
|
trunc: _UFunc_Nin1_Nout1[L['trunc'], L[7], None]
|
|
vecdot: _GUFunc_Nin2_Nout1[L['vecdot'], L[19], None, L["(n),(n)->()"]]
|
|
vecmat: _GUFunc_Nin2_Nout1[L['vecmat'], L[19], None, L["(n),(n,m)->(m)"]]
|
|
|
|
abs = absolute
|
|
acos = arccos
|
|
acosh = arccosh
|
|
asin = arcsin
|
|
asinh = arcsinh
|
|
atan = arctan
|
|
atanh = arctanh
|
|
atan2 = arctan2
|
|
concat = concatenate
|
|
bitwise_left_shift = left_shift
|
|
bitwise_invert = invert
|
|
bitwise_right_shift = right_shift
|
|
permute_dims = transpose
|
|
pow = power
|
|
|
|
class errstate:
|
|
def __init__(
|
|
self,
|
|
*,
|
|
call: _ErrCall = ...,
|
|
all: _ErrKind | None = ...,
|
|
divide: _ErrKind | None = ...,
|
|
over: _ErrKind | None = ...,
|
|
under: _ErrKind | None = ...,
|
|
invalid: _ErrKind | None = ...,
|
|
) -> None: ...
|
|
def __enter__(self) -> None: ...
|
|
def __exit__(
|
|
self,
|
|
exc_type: type[BaseException] | None,
|
|
exc_value: BaseException | None,
|
|
traceback: TracebackType | None,
|
|
/,
|
|
) -> None: ...
|
|
def __call__(self, func: _CallableT) -> _CallableT: ...
|
|
|
|
# TODO: The type of each `__next__` and `iters` return-type depends
|
|
# on the length and dtype of `args`; we can't describe this behavior yet
|
|
# as we lack variadics (PEP 646).
|
|
@final
|
|
class broadcast:
|
|
def __new__(cls, *args: ArrayLike) -> broadcast: ...
|
|
@property
|
|
def index(self) -> int: ...
|
|
@property
|
|
def iters(self) -> tuple[flatiter[Any], ...]: ...
|
|
@property
|
|
def nd(self) -> int: ...
|
|
@property
|
|
def ndim(self) -> int: ...
|
|
@property
|
|
def numiter(self) -> int: ...
|
|
@property
|
|
def shape(self) -> _AnyShape: ...
|
|
@property
|
|
def size(self) -> int: ...
|
|
def __next__(self) -> tuple[Any, ...]: ...
|
|
def __iter__(self) -> Self: ...
|
|
def reset(self) -> None: ...
|
|
|
|
@final
|
|
class busdaycalendar:
|
|
def __new__(
|
|
cls,
|
|
weekmask: ArrayLike = ...,
|
|
holidays: ArrayLike | dt.date | _NestedSequence[dt.date] = ...,
|
|
) -> busdaycalendar: ...
|
|
@property
|
|
def weekmask(self) -> NDArray[np.bool]: ...
|
|
@property
|
|
def holidays(self) -> NDArray[datetime64]: ...
|
|
|
|
class finfo(Generic[_FloatingT_co]):
|
|
dtype: Final[dtype[_FloatingT_co]]
|
|
bits: Final[int]
|
|
eps: Final[_FloatingT_co]
|
|
epsneg: Final[_FloatingT_co]
|
|
iexp: Final[int]
|
|
machep: Final[int]
|
|
max: Final[_FloatingT_co]
|
|
maxexp: Final[int]
|
|
min: Final[_FloatingT_co]
|
|
minexp: Final[int]
|
|
negep: Final[int]
|
|
nexp: Final[int]
|
|
nmant: Final[int]
|
|
precision: Final[int]
|
|
resolution: Final[_FloatingT_co]
|
|
smallest_subnormal: Final[_FloatingT_co]
|
|
@property
|
|
def smallest_normal(self) -> _FloatingT_co: ...
|
|
@property
|
|
def tiny(self) -> _FloatingT_co: ...
|
|
@overload
|
|
def __new__(cls, dtype: inexact[_NBit1] | _DTypeLike[inexact[_NBit1]]) -> finfo[floating[_NBit1]]: ...
|
|
@overload
|
|
def __new__(cls, dtype: complex | type[complex]) -> finfo[float64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: str) -> finfo[floating]: ...
|
|
|
|
class iinfo(Generic[_IntegerT_co]):
|
|
dtype: Final[dtype[_IntegerT_co]]
|
|
kind: Final[LiteralString]
|
|
bits: Final[int]
|
|
key: Final[LiteralString]
|
|
@property
|
|
def min(self) -> int: ...
|
|
@property
|
|
def max(self) -> int: ...
|
|
|
|
@overload
|
|
def __new__(
|
|
cls, dtype: _IntegerT_co | _DTypeLike[_IntegerT_co]
|
|
) -> iinfo[_IntegerT_co]: ...
|
|
@overload
|
|
def __new__(cls, dtype: int | type[int]) -> iinfo[int_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: str) -> iinfo[Any]: ...
|
|
|
|
@final
|
|
class nditer:
|
|
def __new__(
|
|
cls,
|
|
op: ArrayLike | Sequence[ArrayLike | None],
|
|
flags: Sequence[_NDIterFlagsKind] | None = ...,
|
|
op_flags: Sequence[Sequence[_NDIterFlagsOp]] | None = ...,
|
|
op_dtypes: DTypeLike | Sequence[DTypeLike] = ...,
|
|
order: _OrderKACF = ...,
|
|
casting: _CastingKind = ...,
|
|
op_axes: Sequence[Sequence[SupportsIndex]] | None = ...,
|
|
itershape: _ShapeLike | None = ...,
|
|
buffersize: SupportsIndex = ...,
|
|
) -> nditer: ...
|
|
def __enter__(self) -> nditer: ...
|
|
def __exit__(
|
|
self,
|
|
exc_type: type[BaseException] | None,
|
|
exc_value: BaseException | None,
|
|
traceback: TracebackType | None,
|
|
) -> None: ...
|
|
def __iter__(self) -> nditer: ...
|
|
def __next__(self) -> tuple[NDArray[Any], ...]: ...
|
|
def __len__(self) -> int: ...
|
|
def __copy__(self) -> nditer: ...
|
|
@overload
|
|
def __getitem__(self, index: SupportsIndex) -> NDArray[Any]: ...
|
|
@overload
|
|
def __getitem__(self, index: slice) -> tuple[NDArray[Any], ...]: ...
|
|
def __setitem__(self, index: slice | SupportsIndex, value: ArrayLike) -> None: ...
|
|
def close(self) -> None: ...
|
|
def copy(self) -> nditer: ...
|
|
def debug_print(self) -> None: ...
|
|
def enable_external_loop(self) -> None: ...
|
|
def iternext(self) -> builtins.bool: ...
|
|
def remove_axis(self, i: SupportsIndex, /) -> None: ...
|
|
def remove_multi_index(self) -> None: ...
|
|
def reset(self) -> None: ...
|
|
@property
|
|
def dtypes(self) -> tuple[dtype, ...]: ...
|
|
@property
|
|
def finished(self) -> builtins.bool: ...
|
|
@property
|
|
def has_delayed_bufalloc(self) -> builtins.bool: ...
|
|
@property
|
|
def has_index(self) -> builtins.bool: ...
|
|
@property
|
|
def has_multi_index(self) -> builtins.bool: ...
|
|
@property
|
|
def index(self) -> int: ...
|
|
@property
|
|
def iterationneedsapi(self) -> builtins.bool: ...
|
|
@property
|
|
def iterindex(self) -> int: ...
|
|
@property
|
|
def iterrange(self) -> tuple[int, ...]: ...
|
|
@property
|
|
def itersize(self) -> int: ...
|
|
@property
|
|
def itviews(self) -> tuple[NDArray[Any], ...]: ...
|
|
@property
|
|
def multi_index(self) -> tuple[int, ...]: ...
|
|
@property
|
|
def ndim(self) -> int: ...
|
|
@property
|
|
def nop(self) -> int: ...
|
|
@property
|
|
def operands(self) -> tuple[NDArray[Any], ...]: ...
|
|
@property
|
|
def shape(self) -> tuple[int, ...]: ...
|
|
@property
|
|
def value(self) -> tuple[NDArray[Any], ...]: ...
|
|
|
|
class memmap(ndarray[_ShapeT_co, _DTypeT_co]):
|
|
__array_priority__: ClassVar[float]
|
|
filename: str | None
|
|
offset: int
|
|
mode: str
|
|
@overload
|
|
def __new__(
|
|
subtype,
|
|
filename: StrOrBytesPath | _SupportsFileMethodsRW,
|
|
dtype: type[uint8] = ...,
|
|
mode: _MemMapModeKind = ...,
|
|
offset: int = ...,
|
|
shape: int | tuple[int, ...] | None = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> memmap[Any, dtype[uint8]]: ...
|
|
@overload
|
|
def __new__(
|
|
subtype,
|
|
filename: StrOrBytesPath | _SupportsFileMethodsRW,
|
|
dtype: _DTypeLike[_ScalarT],
|
|
mode: _MemMapModeKind = ...,
|
|
offset: int = ...,
|
|
shape: int | tuple[int, ...] | None = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> memmap[Any, dtype[_ScalarT]]: ...
|
|
@overload
|
|
def __new__(
|
|
subtype,
|
|
filename: StrOrBytesPath | _SupportsFileMethodsRW,
|
|
dtype: DTypeLike,
|
|
mode: _MemMapModeKind = ...,
|
|
offset: int = ...,
|
|
shape: int | tuple[int, ...] | None = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> memmap[Any, dtype]: ...
|
|
def __array_finalize__(self, obj: object) -> None: ...
|
|
def __array_wrap__(
|
|
self,
|
|
array: memmap[_ShapeT_co, _DTypeT_co],
|
|
context: tuple[ufunc, tuple[Any, ...], int] | None = ...,
|
|
return_scalar: builtins.bool = ...,
|
|
) -> Any: ...
|
|
def flush(self) -> None: ...
|
|
|
|
# TODO: Add a mypy plugin for managing functions whose output type is dependent
|
|
# on the literal value of some sort of signature (e.g. `einsum` and `vectorize`)
|
|
class vectorize:
|
|
pyfunc: Callable[..., Any]
|
|
cache: builtins.bool
|
|
signature: LiteralString | None
|
|
otypes: LiteralString | None
|
|
excluded: set[int | str]
|
|
__doc__: str | None
|
|
def __init__(
|
|
self,
|
|
pyfunc: Callable[..., Any],
|
|
otypes: str | Iterable[DTypeLike] | None = ...,
|
|
doc: str | None = ...,
|
|
excluded: Iterable[int | str] | None = ...,
|
|
cache: builtins.bool = ...,
|
|
signature: str | None = ...,
|
|
) -> None: ...
|
|
def __call__(self, *args: Any, **kwargs: Any) -> Any: ...
|
|
|
|
class poly1d:
|
|
@property
|
|
def variable(self) -> LiteralString: ...
|
|
@property
|
|
def order(self) -> int: ...
|
|
@property
|
|
def o(self) -> int: ...
|
|
@property
|
|
def roots(self) -> NDArray[Any]: ...
|
|
@property
|
|
def r(self) -> NDArray[Any]: ...
|
|
|
|
@property
|
|
def coeffs(self) -> NDArray[Any]: ...
|
|
@coeffs.setter
|
|
def coeffs(self, value: NDArray[Any]) -> None: ...
|
|
|
|
@property
|
|
def c(self) -> NDArray[Any]: ...
|
|
@c.setter
|
|
def c(self, value: NDArray[Any]) -> None: ...
|
|
|
|
@property
|
|
def coef(self) -> NDArray[Any]: ...
|
|
@coef.setter
|
|
def coef(self, value: NDArray[Any]) -> None: ...
|
|
|
|
@property
|
|
def coefficients(self) -> NDArray[Any]: ...
|
|
@coefficients.setter
|
|
def coefficients(self, value: NDArray[Any]) -> None: ...
|
|
|
|
__hash__: ClassVar[None] # type: ignore[assignment] # pyright: ignore[reportIncompatibleMethodOverride]
|
|
|
|
@overload
|
|
def __array__(self, /, t: None = None, copy: builtins.bool | None = None) -> ndarray[tuple[int], dtype]: ...
|
|
@overload
|
|
def __array__(self, /, t: _DTypeT, copy: builtins.bool | None = None) -> ndarray[tuple[int], _DTypeT]: ...
|
|
|
|
@overload
|
|
def __call__(self, val: _ScalarLike_co) -> Any: ...
|
|
@overload
|
|
def __call__(self, val: poly1d) -> poly1d: ...
|
|
@overload
|
|
def __call__(self, val: ArrayLike) -> NDArray[Any]: ...
|
|
|
|
def __init__(
|
|
self,
|
|
c_or_r: ArrayLike,
|
|
r: builtins.bool = ...,
|
|
variable: str | None = ...,
|
|
) -> None: ...
|
|
def __len__(self) -> int: ...
|
|
def __neg__(self) -> poly1d: ...
|
|
def __pos__(self) -> poly1d: ...
|
|
def __mul__(self, other: ArrayLike, /) -> poly1d: ...
|
|
def __rmul__(self, other: ArrayLike, /) -> poly1d: ...
|
|
def __add__(self, other: ArrayLike, /) -> poly1d: ...
|
|
def __radd__(self, other: ArrayLike, /) -> poly1d: ...
|
|
def __pow__(self, val: _FloatLike_co, /) -> poly1d: ... # Integral floats are accepted
|
|
def __sub__(self, other: ArrayLike, /) -> poly1d: ...
|
|
def __rsub__(self, other: ArrayLike, /) -> poly1d: ...
|
|
def __truediv__(self, other: ArrayLike, /) -> poly1d: ...
|
|
def __rtruediv__(self, other: ArrayLike, /) -> poly1d: ...
|
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def __getitem__(self, val: int, /) -> Any: ...
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def __setitem__(self, key: int, val: Any, /) -> None: ...
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def __iter__(self) -> Iterator[Any]: ...
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def deriv(self, m: SupportsInt | SupportsIndex = ...) -> poly1d: ...
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def integ(
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self,
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m: SupportsInt | SupportsIndex = ...,
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k: _ArrayLikeComplex_co | _ArrayLikeObject_co | None = ...,
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) -> poly1d: ...
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class matrix(ndarray[_2DShapeT_co, _DTypeT_co]):
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__array_priority__: ClassVar[float] = 10.0 # pyright: ignore[reportIncompatibleMethodOverride]
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def __new__(
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subtype, # pyright: ignore[reportSelfClsParameterName]
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data: ArrayLike,
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dtype: DTypeLike = ...,
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copy: builtins.bool = ...,
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) -> matrix[_2D, Incomplete]: ...
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def __array_finalize__(self, obj: object) -> None: ...
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@overload # type: ignore[override]
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def __getitem__(
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self, key: SupportsIndex | _ArrayLikeInt_co | tuple[SupportsIndex | _ArrayLikeInt_co, ...], /
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) -> Incomplete: ...
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@overload
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def __getitem__(self, key: _ToIndices, /) -> matrix[_2D, _DTypeT_co]: ...
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@overload
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def __getitem__(self: matrix[Any, dtype[void]], key: str, /) -> matrix[_2D, dtype]: ...
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@overload
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def __getitem__(self: matrix[Any, dtype[void]], key: list[str], /) -> matrix[_2DShapeT_co, _DTypeT_co]: ... # pyright: ignore[reportIncompatibleMethodOverride]
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#
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def __mul__(self, other: ArrayLike, /) -> matrix[_2D, Incomplete]: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]
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def __rmul__(self, other: ArrayLike, /) -> matrix[_2D, Incomplete]: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]
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def __imul__(self, other: ArrayLike, /) -> Self: ...
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#
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def __pow__(self, other: ArrayLike, mod: None = None, /) -> matrix[_2D, Incomplete]: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]
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def __rpow__(self, other: ArrayLike, mod: None = None, /) -> matrix[_2D, Incomplete]: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]
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def __ipow__(self, other: ArrayLike, /) -> Self: ... # type: ignore[misc, override]
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# keep in sync with `prod` and `mean`
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@overload # type: ignore[override]
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def sum(self, axis: None = None, dtype: DTypeLike | None = None, out: None = None) -> Incomplete: ...
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@overload
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def sum(self, axis: _ShapeLike, dtype: DTypeLike | None = None, out: None = None) -> matrix[_2D, Incomplete]: ...
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@overload
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def sum(self, axis: _ShapeLike | None, dtype: DTypeLike | None, out: _ArrayT) -> _ArrayT: ...
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@overload
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def sum(self, axis: _ShapeLike | None = None, dtype: DTypeLike | None = None, *, out: _ArrayT) -> _ArrayT: ... # pyright: ignore[reportIncompatibleMethodOverride]
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# keep in sync with `sum` and `mean`
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|
@overload # type: ignore[override]
|
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def prod(self, axis: None = None, dtype: DTypeLike | None = None, out: None = None) -> Incomplete: ...
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@overload
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|
def prod(self, axis: _ShapeLike, dtype: DTypeLike | None = None, out: None = None) -> matrix[_2D, Incomplete]: ...
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@overload
|
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def prod(self, axis: _ShapeLike | None, dtype: DTypeLike | None, out: _ArrayT) -> _ArrayT: ...
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@overload
|
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def prod(self, axis: _ShapeLike | None = None, dtype: DTypeLike | None = None, *, out: _ArrayT) -> _ArrayT: ... # pyright: ignore[reportIncompatibleMethodOverride]
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|
# keep in sync with `sum` and `prod`
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|
@overload # type: ignore[override]
|
|
def mean(self, axis: None = None, dtype: DTypeLike | None = None, out: None = None) -> Incomplete: ...
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@overload
|
|
def mean(self, axis: _ShapeLike, dtype: DTypeLike | None = None, out: None = None) -> matrix[_2D, Incomplete]: ...
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|
@overload
|
|
def mean(self, axis: _ShapeLike | None, dtype: DTypeLike | None, out: _ArrayT) -> _ArrayT: ...
|
|
@overload
|
|
def mean(self, axis: _ShapeLike | None = None, dtype: DTypeLike | None = None, *, out: _ArrayT) -> _ArrayT: ... # pyright: ignore[reportIncompatibleMethodOverride]
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|
|
# keep in sync with `var`
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|
@overload # type: ignore[override]
|
|
def std(self, axis: None = None, dtype: DTypeLike | None = None, out: None = None, ddof: float = 0) -> Incomplete: ...
|
|
@overload
|
|
def std(
|
|
self, axis: _ShapeLike, dtype: DTypeLike | None = None, out: None = None, ddof: float = 0
|
|
) -> matrix[_2D, Incomplete]: ...
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|
@overload
|
|
def std(self, axis: _ShapeLike | None, dtype: DTypeLike | None, out: _ArrayT, ddof: float = 0) -> _ArrayT: ...
|
|
@overload
|
|
def std( # pyright: ignore[reportIncompatibleMethodOverride]
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|
self, axis: _ShapeLike | None = None, dtype: DTypeLike | None = None, *, out: _ArrayT, ddof: float = 0
|
|
) -> _ArrayT: ...
|
|
|
|
# keep in sync with `std`
|
|
@overload # type: ignore[override]
|
|
def var(self, axis: None = None, dtype: DTypeLike | None = None, out: None = None, ddof: float = 0) -> Incomplete: ...
|
|
@overload
|
|
def var(
|
|
self, axis: _ShapeLike, dtype: DTypeLike | None = None, out: None = None, ddof: float = 0
|
|
) -> matrix[_2D, Incomplete]: ...
|
|
@overload
|
|
def var(self, axis: _ShapeLike | None, dtype: DTypeLike | None, out: _ArrayT, ddof: float = 0) -> _ArrayT: ...
|
|
@overload
|
|
def var( # pyright: ignore[reportIncompatibleMethodOverride]
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|
self, axis: _ShapeLike | None = None, dtype: DTypeLike | None = None, *, out: _ArrayT, ddof: float = 0
|
|
) -> _ArrayT: ...
|
|
|
|
# keep in sync with `all`
|
|
@overload # type: ignore[override]
|
|
def any(self, axis: None = None, out: None = None) -> np.bool: ...
|
|
@overload
|
|
def any(self, axis: _ShapeLike, out: None = None) -> matrix[_2D, dtype[np.bool]]: ...
|
|
@overload
|
|
def any(self, axis: _ShapeLike | None, out: _ArrayT) -> _ArrayT: ...
|
|
@overload
|
|
def any(self, axis: _ShapeLike | None = None, *, out: _ArrayT) -> _ArrayT: ... # pyright: ignore[reportIncompatibleMethodOverride]
|
|
|
|
# keep in sync with `any`
|
|
@overload # type: ignore[override]
|
|
def all(self, axis: None = None, out: None = None) -> np.bool: ...
|
|
@overload
|
|
def all(self, axis: _ShapeLike, out: None = None) -> matrix[_2D, dtype[np.bool]]: ...
|
|
@overload
|
|
def all(self, axis: _ShapeLike | None, out: _ArrayT) -> _ArrayT: ...
|
|
@overload
|
|
def all(self, axis: _ShapeLike | None = None, *, out: _ArrayT) -> _ArrayT: ... # pyright: ignore[reportIncompatibleMethodOverride]
|
|
|
|
# keep in sync with `min` and `ptp`
|
|
@overload # type: ignore[override]
|
|
def max(self: NDArray[_ScalarT], axis: None = None, out: None = None) -> _ScalarT: ...
|
|
@overload
|
|
def max(self, axis: _ShapeLike, out: None = None) -> matrix[_2D, _DTypeT_co]: ...
|
|
@overload
|
|
def max(self, axis: _ShapeLike | None, out: _ArrayT) -> _ArrayT: ...
|
|
@overload
|
|
def max(self, axis: _ShapeLike | None = None, *, out: _ArrayT) -> _ArrayT: ... # pyright: ignore[reportIncompatibleMethodOverride]
|
|
|
|
# keep in sync with `max` and `ptp`
|
|
@overload # type: ignore[override]
|
|
def min(self: NDArray[_ScalarT], axis: None = None, out: None = None) -> _ScalarT: ...
|
|
@overload
|
|
def min(self, axis: _ShapeLike, out: None = None) -> matrix[_2D, _DTypeT_co]: ...
|
|
@overload
|
|
def min(self, axis: _ShapeLike | None, out: _ArrayT) -> _ArrayT: ...
|
|
@overload
|
|
def min(self, axis: _ShapeLike | None = None, *, out: _ArrayT) -> _ArrayT: ... # pyright: ignore[reportIncompatibleMethodOverride]
|
|
|
|
# keep in sync with `max` and `min`
|
|
@overload
|
|
def ptp(self: NDArray[_ScalarT], axis: None = None, out: None = None) -> _ScalarT: ...
|
|
@overload
|
|
def ptp(self, axis: _ShapeLike, out: None = None) -> matrix[_2D, _DTypeT_co]: ...
|
|
@overload
|
|
def ptp(self, axis: _ShapeLike | None, out: _ArrayT) -> _ArrayT: ...
|
|
@overload
|
|
def ptp(self, axis: _ShapeLike | None = None, *, out: _ArrayT) -> _ArrayT: ... # pyright: ignore[reportIncompatibleMethodOverride]
|
|
|
|
# keep in sync with `argmin`
|
|
@overload # type: ignore[override]
|
|
def argmax(self: NDArray[_ScalarT], axis: None = None, out: None = None) -> intp: ...
|
|
@overload
|
|
def argmax(self, axis: _ShapeLike, out: None = None) -> matrix[_2D, dtype[intp]]: ...
|
|
@overload
|
|
def argmax(self, axis: _ShapeLike | None, out: _BoolOrIntArrayT) -> _BoolOrIntArrayT: ...
|
|
@overload
|
|
def argmax(self, axis: _ShapeLike | None = None, *, out: _BoolOrIntArrayT) -> _BoolOrIntArrayT: ... # pyright: ignore[reportIncompatibleMethodOverride]
|
|
|
|
# keep in sync with `argmax`
|
|
@overload # type: ignore[override]
|
|
def argmin(self: NDArray[_ScalarT], axis: None = None, out: None = None) -> intp: ...
|
|
@overload
|
|
def argmin(self, axis: _ShapeLike, out: None = None) -> matrix[_2D, dtype[intp]]: ...
|
|
@overload
|
|
def argmin(self, axis: _ShapeLike | None, out: _BoolOrIntArrayT) -> _BoolOrIntArrayT: ...
|
|
@overload
|
|
def argmin(self, axis: _ShapeLike | None = None, *, out: _BoolOrIntArrayT) -> _BoolOrIntArrayT: ... # pyright: ignore[reportIncompatibleMethodOverride]
|
|
|
|
#the second overload handles the (rare) case that the matrix is not 2-d
|
|
@overload
|
|
def tolist(self: matrix[_2D, dtype[generic[_T]]]) -> list[list[_T]]: ... # pyright: ignore[reportIncompatibleMethodOverride]
|
|
@overload
|
|
def tolist(self) -> Incomplete: ... # pyright: ignore[reportIncompatibleMethodOverride]
|
|
|
|
# these three methods will at least return a `2-d` array of shape (1, n)
|
|
def squeeze(self, axis: _ShapeLike | None = None) -> matrix[_2D, _DTypeT_co]: ...
|
|
def ravel(self, /, order: _OrderKACF = "C") -> matrix[_2D, _DTypeT_co]: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]
|
|
def flatten(self, /, order: _OrderKACF = "C") -> matrix[_2D, _DTypeT_co]: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]
|
|
|
|
# matrix.T is inherited from _ScalarOrArrayCommon
|
|
def getT(self) -> Self: ...
|
|
@property
|
|
def I(self) -> matrix[_2D, Incomplete]: ... # noqa: E743
|
|
def getI(self) -> matrix[_2D, Incomplete]: ...
|
|
@property
|
|
def A(self) -> ndarray[_2DShapeT_co, _DTypeT_co]: ...
|
|
def getA(self) -> ndarray[_2DShapeT_co, _DTypeT_co]: ...
|
|
@property
|
|
def A1(self) -> ndarray[_AnyShape, _DTypeT_co]: ...
|
|
def getA1(self) -> ndarray[_AnyShape, _DTypeT_co]: ...
|
|
@property
|
|
def H(self) -> matrix[_2D, _DTypeT_co]: ...
|
|
def getH(self) -> matrix[_2D, _DTypeT_co]: ...
|
|
|
|
def from_dlpack(
|
|
x: _SupportsDLPack[None],
|
|
/,
|
|
*,
|
|
device: L["cpu"] | None = None,
|
|
copy: builtins.bool | None = None,
|
|
) -> NDArray[number | np.bool]: ...
|