4411 lines
148 KiB
Python
4411 lines
148 KiB
Python
import builtins
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import os
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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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import enum
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from abc import abstractmethod
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from types import TracebackType, MappingProxyType
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from contextlib import ContextDecorator
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from contextlib import contextmanager
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if sys.version_info >= (3, 9):
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from types import GenericAlias
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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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_FiniteNestedSequence,
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_SupportsArray,
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_ArrayLikeBool_co,
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_ArrayLikeUInt_co,
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_ArrayLikeInt_co,
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_ArrayLikeFloat_co,
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_ArrayLikeComplex_co,
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_ArrayLikeNumber_co,
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_ArrayLikeTD64_co,
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_ArrayLikeDT64_co,
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_ArrayLikeObject_co,
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_ArrayLikeStr_co,
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_ArrayLikeBytes_co,
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_ArrayLikeUnknown,
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_UnknownType,
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# DTypes
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DTypeLike,
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_DTypeLike,
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_DTypeLikeVoid,
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_SupportsDType,
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_VoidDTypeLike,
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# Shapes
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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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_BoolLike_co,
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_IntLike_co,
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_FloatLike_co,
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_ComplexLike_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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_256Bit,
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_128Bit,
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_96Bit,
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_80Bit,
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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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_NBitInt,
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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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_IntCodes,
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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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_UIntCodes,
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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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# 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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_BoolOp,
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_BoolBitOp,
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_BoolSub,
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_BoolTrueDiv,
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_BoolMod,
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_BoolDivMod,
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_TD64Div,
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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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_ComplexOp,
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_NumberOp,
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_ComparisonOp,
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)
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# NOTE: Numpy's mypy plugin is used for removing the types unavailable
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# to the specific platform
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from numpy._typing._extended_precision import (
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uint128 as uint128,
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uint256 as uint256,
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int128 as int128,
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int256 as int256,
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float80 as float80,
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float96 as float96,
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float128 as float128,
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float256 as float256,
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complex160 as complex160,
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complex192 as complex192,
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complex256 as complex256,
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complex512 as complex512,
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)
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from collections.abc import (
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Callable,
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Container,
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Iterable,
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Iterator,
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Mapping,
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Sequence,
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Sized,
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)
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from typing import (
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Literal as L,
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Any,
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Generator,
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Generic,
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IO,
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NoReturn,
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overload,
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SupportsComplex,
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SupportsFloat,
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SupportsInt,
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TypeVar,
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Union,
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Protocol,
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SupportsIndex,
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Final,
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final,
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ClassVar,
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)
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# Ensures that the stubs are picked up
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from numpy import (
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ctypeslib as ctypeslib,
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fft as fft,
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lib as lib,
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linalg as linalg,
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ma as ma,
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polynomial as polynomial,
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random as random,
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testing as testing,
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version as version,
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)
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from numpy.core import defchararray, records
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char = defchararray
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rec = records
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from numpy.core.function_base import (
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linspace as linspace,
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logspace as logspace,
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geomspace as geomspace,
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)
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from numpy.core.fromnumeric import (
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take as take,
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reshape as reshape,
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choose as choose,
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repeat as repeat,
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put as put,
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swapaxes as swapaxes,
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transpose as transpose,
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partition as partition,
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argpartition as argpartition,
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sort as sort,
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argsort as argsort,
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argmax as argmax,
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argmin as argmin,
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searchsorted as searchsorted,
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resize as resize,
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squeeze as squeeze,
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diagonal as diagonal,
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trace as trace,
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ravel as ravel,
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nonzero as nonzero,
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shape as shape,
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compress as compress,
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clip as clip,
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sum as sum,
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all as all,
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any as any,
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cumsum as cumsum,
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ptp as ptp,
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amax as amax,
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amin as amin,
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prod as prod,
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cumprod as cumprod,
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ndim as ndim,
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size as size,
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around as around,
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mean as mean,
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std as std,
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var as var,
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)
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from numpy.core._asarray import (
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require as require,
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)
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from numpy.core._type_aliases import (
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sctypes as sctypes,
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sctypeDict as sctypeDict,
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)
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from numpy.core._ufunc_config import (
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seterr as seterr,
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geterr as geterr,
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setbufsize as setbufsize,
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getbufsize as getbufsize,
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seterrcall as seterrcall,
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geterrcall as geterrcall,
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_ErrKind,
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_ErrFunc,
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_ErrDictOptional,
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)
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from numpy.core.arrayprint import (
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set_printoptions as set_printoptions,
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get_printoptions as get_printoptions,
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array2string as array2string,
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format_float_scientific as format_float_scientific,
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format_float_positional as format_float_positional,
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array_repr as array_repr,
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array_str as array_str,
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set_string_function as set_string_function,
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printoptions as printoptions,
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)
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from numpy.core.einsumfunc import (
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einsum as einsum,
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einsum_path as einsum_path,
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)
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from numpy.core.multiarray import (
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ALLOW_THREADS as ALLOW_THREADS,
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BUFSIZE as BUFSIZE,
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CLIP as CLIP,
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MAXDIMS as MAXDIMS,
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MAY_SHARE_BOUNDS as MAY_SHARE_BOUNDS,
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MAY_SHARE_EXACT as MAY_SHARE_EXACT,
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RAISE as RAISE,
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WRAP as WRAP,
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tracemalloc_domain as tracemalloc_domain,
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array as array,
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empty_like as empty_like,
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empty as empty,
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zeros as zeros,
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concatenate as concatenate,
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inner as inner,
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where as where,
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lexsort as lexsort,
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can_cast as can_cast,
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min_scalar_type as min_scalar_type,
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result_type as result_type,
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dot as dot,
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vdot as vdot,
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bincount as bincount,
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copyto as copyto,
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putmask as putmask,
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packbits as packbits,
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unpackbits as unpackbits,
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shares_memory as shares_memory,
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may_share_memory as may_share_memory,
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asarray as asarray,
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asanyarray as asanyarray,
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ascontiguousarray as ascontiguousarray,
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asfortranarray as asfortranarray,
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arange as arange,
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busday_count as busday_count,
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busday_offset as busday_offset,
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compare_chararrays as compare_chararrays,
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datetime_as_string as datetime_as_string,
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datetime_data as datetime_data,
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frombuffer as frombuffer,
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fromfile as fromfile,
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fromiter as fromiter,
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is_busday as is_busday,
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promote_types as promote_types,
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seterrobj as seterrobj,
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geterrobj as geterrobj,
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fromstring as fromstring,
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frompyfunc as frompyfunc,
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nested_iters as 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 as zeros_like,
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ones as ones,
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ones_like as ones_like,
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full as full,
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full_like as full_like,
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count_nonzero as count_nonzero,
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isfortran as isfortran,
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argwhere as argwhere,
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flatnonzero as flatnonzero,
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correlate as correlate,
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convolve as convolve,
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outer as outer,
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tensordot as tensordot,
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roll as roll,
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rollaxis as rollaxis,
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moveaxis as moveaxis,
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cross as cross,
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indices as indices,
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fromfunction as fromfunction,
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isscalar as isscalar,
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binary_repr as binary_repr,
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base_repr as base_repr,
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identity as identity,
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allclose as allclose,
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isclose as isclose,
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array_equal as array_equal,
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array_equiv as array_equiv,
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)
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from numpy.core.numerictypes import (
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maximum_sctype as maximum_sctype,
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issctype as issctype,
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obj2sctype as obj2sctype,
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issubclass_ as issubclass_,
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issubsctype as issubsctype,
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issubdtype as issubdtype,
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sctype2char as sctype2char,
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find_common_type as find_common_type,
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nbytes as nbytes,
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cast as cast,
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ScalarType as ScalarType,
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typecodes as typecodes,
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)
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from numpy.core.shape_base import (
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atleast_1d as atleast_1d,
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atleast_2d as atleast_2d,
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atleast_3d as atleast_3d,
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block as block,
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hstack as hstack,
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stack as stack,
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vstack as vstack,
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)
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from numpy.lib import (
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emath as emath,
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)
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from numpy.lib.arraypad import (
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pad as pad,
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)
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from numpy.lib.arraysetops import (
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ediff1d as ediff1d,
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intersect1d as intersect1d,
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setxor1d as setxor1d,
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union1d as union1d,
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setdiff1d as setdiff1d,
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unique as unique,
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in1d as in1d,
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isin as isin,
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)
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from numpy.lib.arrayterator import (
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Arrayterator as Arrayterator,
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)
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from numpy.lib.function_base import (
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select as select,
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piecewise as piecewise,
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trim_zeros as trim_zeros,
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copy as copy,
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iterable as iterable,
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percentile as percentile,
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diff as diff,
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gradient as gradient,
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angle as angle,
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unwrap as unwrap,
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sort_complex as sort_complex,
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disp as disp,
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flip as flip,
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rot90 as rot90,
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extract as extract,
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place as place,
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asarray_chkfinite as asarray_chkfinite,
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average as average,
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bincount as bincount,
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digitize as digitize,
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cov as cov,
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corrcoef as corrcoef,
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median as median,
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sinc as sinc,
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hamming as hamming,
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hanning as hanning,
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bartlett as bartlett,
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blackman as blackman,
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kaiser as kaiser,
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trapz as trapz,
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i0 as i0,
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add_newdoc as add_newdoc,
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add_docstring as add_docstring,
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meshgrid as meshgrid,
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delete as delete,
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insert as insert,
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append as append,
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interp as interp,
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add_newdoc_ufunc as add_newdoc_ufunc,
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quantile as quantile,
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)
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from numpy.lib.histograms import (
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histogram_bin_edges as histogram_bin_edges,
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histogram as histogram,
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histogramdd as histogramdd,
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)
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from numpy.lib.index_tricks import (
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ravel_multi_index as ravel_multi_index,
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unravel_index as unravel_index,
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mgrid as mgrid,
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ogrid as ogrid,
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r_ as r_,
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c_ as c_,
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s_ as s_,
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index_exp as index_exp,
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ix_ as ix_,
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fill_diagonal as fill_diagonal,
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diag_indices as diag_indices,
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diag_indices_from as diag_indices_from,
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)
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from numpy.lib.nanfunctions import (
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nansum as nansum,
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nanmax as nanmax,
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nanmin as nanmin,
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nanargmax as nanargmax,
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nanargmin as nanargmin,
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nanmean as nanmean,
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nanmedian as nanmedian,
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nanpercentile as nanpercentile,
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nanvar as nanvar,
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nanstd as nanstd,
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|
nanprod as nanprod,
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nancumsum as nancumsum,
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nancumprod as nancumprod,
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nanquantile as nanquantile,
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)
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|
from numpy.lib.npyio import (
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savetxt as savetxt,
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loadtxt as loadtxt,
|
|
genfromtxt as genfromtxt,
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|
recfromtxt as recfromtxt,
|
|
recfromcsv as recfromcsv,
|
|
load as load,
|
|
save as save,
|
|
savez as savez,
|
|
savez_compressed as savez_compressed,
|
|
packbits as packbits,
|
|
unpackbits as unpackbits,
|
|
fromregex as fromregex,
|
|
)
|
|
|
|
from numpy.lib.polynomial import (
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|
poly as poly,
|
|
roots as roots,
|
|
polyint as polyint,
|
|
polyder as polyder,
|
|
polyadd as polyadd,
|
|
polysub as polysub,
|
|
polymul as polymul,
|
|
polydiv as polydiv,
|
|
polyval as polyval,
|
|
polyfit as polyfit,
|
|
)
|
|
|
|
from numpy.lib.shape_base import (
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|
column_stack as column_stack,
|
|
row_stack as row_stack,
|
|
dstack as dstack,
|
|
array_split as array_split,
|
|
split as split,
|
|
hsplit as hsplit,
|
|
vsplit as vsplit,
|
|
dsplit as dsplit,
|
|
apply_over_axes as apply_over_axes,
|
|
expand_dims as expand_dims,
|
|
apply_along_axis as apply_along_axis,
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|
kron as kron,
|
|
tile as tile,
|
|
get_array_wrap as get_array_wrap,
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|
take_along_axis as take_along_axis,
|
|
put_along_axis as put_along_axis,
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|
)
|
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|
|
from numpy.lib.stride_tricks import (
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broadcast_to as broadcast_to,
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|
broadcast_arrays as broadcast_arrays,
|
|
broadcast_shapes as broadcast_shapes,
|
|
)
|
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|
|
from numpy.lib.twodim_base import (
|
|
diag as diag,
|
|
diagflat as diagflat,
|
|
eye as eye,
|
|
fliplr as fliplr,
|
|
flipud as flipud,
|
|
tri as tri,
|
|
triu as triu,
|
|
tril as tril,
|
|
vander as vander,
|
|
histogram2d as histogram2d,
|
|
mask_indices as mask_indices,
|
|
tril_indices as tril_indices,
|
|
tril_indices_from as tril_indices_from,
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|
triu_indices as triu_indices,
|
|
triu_indices_from as triu_indices_from,
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|
)
|
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|
|
from numpy.lib.type_check import (
|
|
mintypecode as mintypecode,
|
|
asfarray as asfarray,
|
|
real as real,
|
|
imag as imag,
|
|
iscomplex as iscomplex,
|
|
isreal as isreal,
|
|
iscomplexobj as iscomplexobj,
|
|
isrealobj as isrealobj,
|
|
nan_to_num as nan_to_num,
|
|
real_if_close as real_if_close,
|
|
typename as typename,
|
|
common_type as common_type,
|
|
)
|
|
|
|
from numpy.lib.ufunclike import (
|
|
fix as fix,
|
|
isposinf as isposinf,
|
|
isneginf as isneginf,
|
|
)
|
|
|
|
from numpy.lib.utils import (
|
|
issubclass_ as issubclass_,
|
|
issubsctype as issubsctype,
|
|
issubdtype as issubdtype,
|
|
deprecate as deprecate,
|
|
deprecate_with_doc as deprecate_with_doc,
|
|
get_include as get_include,
|
|
info as info,
|
|
source as source,
|
|
who as who,
|
|
lookfor as lookfor,
|
|
byte_bounds as byte_bounds,
|
|
safe_eval as safe_eval,
|
|
show_runtime as show_runtime,
|
|
)
|
|
|
|
from numpy.matrixlib import (
|
|
asmatrix as asmatrix,
|
|
mat as mat,
|
|
bmat as bmat,
|
|
)
|
|
|
|
_AnyStr_contra = TypeVar("_AnyStr_contra", str, bytes, contravariant=True)
|
|
|
|
# Protocol for representing file-like-objects accepted
|
|
# by `ndarray.tofile` and `fromfile`
|
|
class _IOProtocol(Protocol):
|
|
def flush(self) -> object: ...
|
|
def fileno(self) -> int: ...
|
|
def tell(self) -> SupportsIndex: ...
|
|
def seek(self, offset: int, whence: int, /) -> object: ...
|
|
|
|
# NOTE: `seek`, `write` and `flush` are technically only required
|
|
# for `readwrite`/`write` modes
|
|
class _MemMapIOProtocol(Protocol):
|
|
def flush(self) -> object: ...
|
|
def fileno(self) -> SupportsIndex: ...
|
|
def tell(self) -> int: ...
|
|
def seek(self, offset: int, whence: int, /) -> object: ...
|
|
def write(self, s: bytes, /) -> object: ...
|
|
@property
|
|
def read(self) -> object: ...
|
|
|
|
class _SupportsWrite(Protocol[_AnyStr_contra]):
|
|
def write(self, s: _AnyStr_contra, /) -> object: ...
|
|
|
|
__all__: list[str]
|
|
__path__: list[str]
|
|
__version__: str
|
|
__git_version__: str
|
|
test: PytestTester
|
|
|
|
# TODO: Move placeholders to their respective module once
|
|
# their annotations are properly implemented
|
|
#
|
|
# Placeholders for classes
|
|
|
|
# Some of these are aliases; others are wrappers with an identical signature
|
|
round = around
|
|
round_ = around
|
|
max = amax
|
|
min = amin
|
|
product = prod
|
|
cumproduct = cumprod
|
|
sometrue = any
|
|
alltrue = all
|
|
|
|
def show_config() -> None: ...
|
|
|
|
_NdArraySubClass = TypeVar("_NdArraySubClass", bound=ndarray)
|
|
_DTypeScalar_co = TypeVar("_DTypeScalar_co", covariant=True, bound=generic)
|
|
_ByteOrder = L["S", "<", ">", "=", "|", "L", "B", "N", "I"]
|
|
|
|
@final
|
|
class dtype(Generic[_DTypeScalar_co]):
|
|
names: None | tuple[builtins.str, ...]
|
|
# Overload for subclass of generic
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[_DTypeScalar_co],
|
|
align: bool = ...,
|
|
copy: bool = ...,
|
|
) -> dtype[_DTypeScalar_co]: ...
|
|
# Overloads for string aliases, Python types, and some assorted
|
|
# other special cases. Order is sometimes important because of the
|
|
# subtype relationships
|
|
#
|
|
# bool < int < float < complex < object
|
|
#
|
|
# so we have to make sure the overloads for the narrowest type is
|
|
# first.
|
|
# Builtin types
|
|
@overload
|
|
def __new__(cls, dtype: type[bool], align: bool = ..., copy: bool = ...) -> dtype[bool_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: type[int], align: bool = ..., copy: bool = ...) -> dtype[int_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: None | type[float], align: bool = ..., copy: bool = ...) -> dtype[float_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: type[complex], align: bool = ..., copy: bool = ...) -> dtype[complex_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: type[builtins.str], align: bool = ..., copy: bool = ...) -> dtype[str_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: type[bytes], align: bool = ..., copy: bool = ...) -> dtype[bytes_]: ...
|
|
|
|
# `unsignedinteger` string-based representations and ctypes
|
|
@overload
|
|
def __new__(cls, dtype: _UInt8Codes | type[ct.c_uint8], align: bool = ..., copy: bool = ...) -> dtype[uint8]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UInt16Codes | type[ct.c_uint16], align: bool = ..., copy: bool = ...) -> dtype[uint16]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UInt32Codes | type[ct.c_uint32], align: bool = ..., copy: bool = ...) -> dtype[uint32]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UInt64Codes | type[ct.c_uint64], align: bool = ..., copy: bool = ...) -> dtype[uint64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UByteCodes | type[ct.c_ubyte], align: bool = ..., copy: bool = ...) -> dtype[ubyte]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UShortCodes | type[ct.c_ushort], align: bool = ..., copy: bool = ...) -> dtype[ushort]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UIntCCodes | type[ct.c_uint], align: bool = ..., copy: bool = ...) -> 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: bool = ..., copy: bool = ...) -> dtype[uintp]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _UIntCodes | type[ct.c_ulong], align: bool = ..., copy: bool = ...) -> dtype[uint]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _ULongLongCodes | type[ct.c_ulonglong], align: bool = ..., copy: bool = ...) -> dtype[ulonglong]: ...
|
|
|
|
# `signedinteger` string-based representations and ctypes
|
|
@overload
|
|
def __new__(cls, dtype: _Int8Codes | type[ct.c_int8], align: bool = ..., copy: bool = ...) -> dtype[int8]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Int16Codes | type[ct.c_int16], align: bool = ..., copy: bool = ...) -> dtype[int16]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Int32Codes | type[ct.c_int32], align: bool = ..., copy: bool = ...) -> dtype[int32]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Int64Codes | type[ct.c_int64], align: bool = ..., copy: bool = ...) -> dtype[int64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _ByteCodes | type[ct.c_byte], align: bool = ..., copy: bool = ...) -> dtype[byte]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _ShortCodes | type[ct.c_short], align: bool = ..., copy: bool = ...) -> dtype[short]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _IntCCodes | type[ct.c_int], align: bool = ..., copy: bool = ...) -> dtype[intc]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _IntPCodes | type[ct.c_ssize_t], align: bool = ..., copy: bool = ...) -> dtype[intp]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _IntCodes | type[ct.c_long], align: bool = ..., copy: bool = ...) -> dtype[int_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _LongLongCodes | type[ct.c_longlong], align: bool = ..., copy: bool = ...) -> dtype[longlong]: ...
|
|
|
|
# `floating` string-based representations and ctypes
|
|
@overload
|
|
def __new__(cls, dtype: _Float16Codes, align: bool = ..., copy: bool = ...) -> dtype[float16]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Float32Codes, align: bool = ..., copy: bool = ...) -> dtype[float32]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Float64Codes, align: bool = ..., copy: bool = ...) -> dtype[float64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _HalfCodes, align: bool = ..., copy: bool = ...) -> dtype[half]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _SingleCodes | type[ct.c_float], align: bool = ..., copy: bool = ...) -> dtype[single]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _DoubleCodes | type[ct.c_double], align: bool = ..., copy: bool = ...) -> dtype[double]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _LongDoubleCodes | type[ct.c_longdouble], align: bool = ..., copy: bool = ...) -> dtype[longdouble]: ...
|
|
|
|
# `complexfloating` string-based representations
|
|
@overload
|
|
def __new__(cls, dtype: _Complex64Codes, align: bool = ..., copy: bool = ...) -> dtype[complex64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _Complex128Codes, align: bool = ..., copy: bool = ...) -> dtype[complex128]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _CSingleCodes, align: bool = ..., copy: bool = ...) -> dtype[csingle]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _CDoubleCodes, align: bool = ..., copy: bool = ...) -> dtype[cdouble]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _CLongDoubleCodes, align: bool = ..., copy: bool = ...) -> dtype[clongdouble]: ...
|
|
|
|
# Miscellaneous string-based representations and ctypes
|
|
@overload
|
|
def __new__(cls, dtype: _BoolCodes | type[ct.c_bool], align: bool = ..., copy: bool = ...) -> dtype[bool_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _TD64Codes, align: bool = ..., copy: bool = ...) -> dtype[timedelta64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _DT64Codes, align: bool = ..., copy: bool = ...) -> dtype[datetime64]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _StrCodes, align: bool = ..., copy: bool = ...) -> dtype[str_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _BytesCodes | type[ct.c_char], align: bool = ..., copy: bool = ...) -> dtype[bytes_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _VoidCodes, align: bool = ..., copy: bool = ...) -> dtype[void]: ...
|
|
@overload
|
|
def __new__(cls, dtype: _ObjectCodes | type[ct.py_object], align: bool = ..., copy: bool = ...) -> dtype[object_]: ...
|
|
|
|
# dtype of a dtype is the same dtype
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: dtype[_DTypeScalar_co],
|
|
align: bool = ...,
|
|
copy: bool = ...,
|
|
) -> dtype[_DTypeScalar_co]: ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _SupportsDType[dtype[_DTypeScalar_co]],
|
|
align: bool = ...,
|
|
copy: bool = ...,
|
|
) -> dtype[_DTypeScalar_co]: ...
|
|
# Handle strings that can't be expressed as literals; i.e. s1, s2, ...
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: builtins.str,
|
|
align: bool = ...,
|
|
copy: bool = ...,
|
|
) -> dtype[Any]: ...
|
|
# Catchall overload for void-likes
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: _VoidDTypeLike,
|
|
align: bool = ...,
|
|
copy: bool = ...,
|
|
) -> dtype[void]: ...
|
|
# Catchall overload for object-likes
|
|
@overload
|
|
def __new__(
|
|
cls,
|
|
dtype: type[object],
|
|
align: bool = ...,
|
|
copy: bool = ...,
|
|
) -> dtype[object_]: ...
|
|
|
|
if sys.version_info >= (3, 9):
|
|
def __class_getitem__(self, 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[Any]: ...
|
|
|
|
# NOTE: In the future 1-based multiplications will also yield `flexible` dtypes
|
|
@overload
|
|
def __mul__(self: _DType, value: L[1]) -> _DType: ...
|
|
@overload
|
|
def __mul__(self: _FlexDType, value: SupportsIndex) -> _FlexDType: ...
|
|
@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[Any]` for
|
|
# now for non-flexible dtypes.
|
|
@overload
|
|
def __rmul__(self: _FlexDType, value: SupportsIndex) -> _FlexDType: ...
|
|
@overload
|
|
def __rmul__(self, value: SupportsIndex) -> dtype[Any]: ...
|
|
|
|
def __gt__(self, other: DTypeLike) -> bool: ...
|
|
def __ge__(self, other: DTypeLike) -> bool: ...
|
|
def __lt__(self, other: DTypeLike) -> bool: ...
|
|
def __le__(self, other: DTypeLike) -> 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) -> bool: ...
|
|
def __ne__(self, other: Any) -> bool: ...
|
|
|
|
@property
|
|
def alignment(self) -> int: ...
|
|
@property
|
|
def base(self) -> dtype[Any]: ...
|
|
@property
|
|
def byteorder(self) -> builtins.str: ...
|
|
@property
|
|
def char(self) -> builtins.str: ...
|
|
@property
|
|
def descr(self) -> list[tuple[builtins.str, builtins.str] | tuple[builtins.str, builtins.str, _Shape]]: ...
|
|
@property
|
|
def fields(
|
|
self,
|
|
) -> None | MappingProxyType[builtins.str, tuple[dtype[Any], int] | tuple[dtype[Any], int, Any]]: ...
|
|
@property
|
|
def flags(self) -> int: ...
|
|
@property
|
|
def hasobject(self) -> bool: ...
|
|
@property
|
|
def isbuiltin(self) -> int: ...
|
|
@property
|
|
def isnative(self) -> bool: ...
|
|
@property
|
|
def isalignedstruct(self) -> bool: ...
|
|
@property
|
|
def itemsize(self) -> int: ...
|
|
@property
|
|
def kind(self) -> builtins.str: ...
|
|
@property
|
|
def metadata(self) -> None | MappingProxyType[builtins.str, Any]: ...
|
|
@property
|
|
def name(self) -> builtins.str: ...
|
|
@property
|
|
def num(self) -> int: ...
|
|
@property
|
|
def shape(self) -> _Shape: ...
|
|
@property
|
|
def ndim(self) -> int: ...
|
|
@property
|
|
def subdtype(self) -> None | tuple[dtype[Any], _Shape]: ...
|
|
def newbyteorder(self: _DType, __new_order: _ByteOrder = ...) -> _DType: ...
|
|
@property
|
|
def str(self) -> builtins.str: ...
|
|
@property
|
|
def type(self) -> type[_DTypeScalar_co]: ...
|
|
|
|
_ArrayLikeInt = Union[
|
|
int,
|
|
integer,
|
|
Sequence[Union[int, integer]],
|
|
Sequence[Sequence[Any]], # TODO: wait for support for recursive types
|
|
ndarray
|
|
]
|
|
|
|
_FlatIterSelf = TypeVar("_FlatIterSelf", bound=flatiter)
|
|
|
|
@final
|
|
class flatiter(Generic[_NdArraySubClass]):
|
|
__hash__: ClassVar[None]
|
|
@property
|
|
def base(self) -> _NdArraySubClass: ...
|
|
@property
|
|
def coords(self) -> _Shape: ...
|
|
@property
|
|
def index(self) -> int: ...
|
|
def copy(self) -> _NdArraySubClass: ...
|
|
def __iter__(self: _FlatIterSelf) -> _FlatIterSelf: ...
|
|
def __next__(self: flatiter[ndarray[Any, dtype[_ScalarType]]]) -> _ScalarType: ...
|
|
def __len__(self) -> int: ...
|
|
@overload
|
|
def __getitem__(
|
|
self: flatiter[ndarray[Any, dtype[_ScalarType]]],
|
|
key: int | integer | tuple[int | integer],
|
|
) -> _ScalarType: ...
|
|
@overload
|
|
def __getitem__(
|
|
self,
|
|
key: _ArrayLikeInt | slice | ellipsis | tuple[_ArrayLikeInt | slice | ellipsis],
|
|
) -> _NdArraySubClass: ...
|
|
# 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 | ellipsis | tuple[_ArrayLikeInt | slice | ellipsis],
|
|
value: Any,
|
|
) -> None: ...
|
|
@overload
|
|
def __array__(self: flatiter[ndarray[Any, _DType]], dtype: None = ..., /) -> ndarray[Any, _DType]: ...
|
|
@overload
|
|
def __array__(self, dtype: _DType, /) -> ndarray[Any, _DType]: ...
|
|
|
|
_OrderKACF = L[None, "K", "A", "C", "F"]
|
|
_OrderACF = L[None, "A", "C", "F"]
|
|
_OrderCF = L[None, "C", "F"]
|
|
|
|
_ModeKind = L["raise", "wrap", "clip"]
|
|
_PartitionKind = L["introselect"]
|
|
_SortKind = L["quicksort", "mergesort", "heapsort", "stable"]
|
|
_SortSide = L["left", "right"]
|
|
|
|
_ArraySelf = TypeVar("_ArraySelf", bound=_ArrayOrScalarCommon)
|
|
|
|
class _ArrayOrScalarCommon:
|
|
@property
|
|
def T(self: _ArraySelf) -> _ArraySelf: ...
|
|
@property
|
|
def data(self) -> memoryview: ...
|
|
@property
|
|
def flags(self) -> flagsobj: ...
|
|
@property
|
|
def itemsize(self) -> int: ...
|
|
@property
|
|
def nbytes(self) -> int: ...
|
|
def __bool__(self) -> bool: ...
|
|
def __bytes__(self) -> bytes: ...
|
|
def __str__(self) -> str: ...
|
|
def __repr__(self) -> str: ...
|
|
def __copy__(self: _ArraySelf) -> _ArraySelf: ...
|
|
def __deepcopy__(self: _ArraySelf, memo: None | dict[int, Any], /) -> _ArraySelf: ...
|
|
|
|
# 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: _ArraySelf, order: _OrderKACF = ...) -> _ArraySelf: ...
|
|
def dump(self, file: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _SupportsWrite[bytes]) -> None: ...
|
|
def dumps(self) -> bytes: ...
|
|
def tobytes(self, order: _OrderKACF = ...) -> bytes: ...
|
|
# NOTE: `tostring()` is deprecated and therefore excluded
|
|
# def tostring(self, order=...): ...
|
|
def tofile(
|
|
self,
|
|
fid: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _IOProtocol,
|
|
sep: str = ...,
|
|
format: str = ...,
|
|
) -> None: ...
|
|
# generics and 0d arrays return builtin scalars
|
|
def tolist(self) -> Any: ...
|
|
|
|
@property
|
|
def __array_interface__(self) -> dict[str, Any]: ...
|
|
@property
|
|
def __array_priority__(self) -> float: ...
|
|
@property
|
|
def __array_struct__(self) -> Any: ... # builtins.PyCapsule
|
|
def __setstate__(self, state: tuple[
|
|
SupportsIndex, # version
|
|
_ShapeLike, # Shape
|
|
_DType_co, # DType
|
|
bool, # F-continuous
|
|
bytes | list[Any], # Data
|
|
], /) -> None: ...
|
|
# a `bool_` is returned when `keepdims=True` and `self` is a 0d array
|
|
|
|
@overload
|
|
def all(
|
|
self,
|
|
axis: None = ...,
|
|
out: None = ...,
|
|
keepdims: L[False] = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> bool_: ...
|
|
@overload
|
|
def all(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
out: None = ...,
|
|
keepdims: bool = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def all(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
keepdims: bool = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def any(
|
|
self,
|
|
axis: None = ...,
|
|
out: None = ...,
|
|
keepdims: L[False] = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> bool_: ...
|
|
@overload
|
|
def any(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
out: None = ...,
|
|
keepdims: bool = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def any(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
keepdims: bool = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def argmax(
|
|
self,
|
|
axis: None = ...,
|
|
out: None = ...,
|
|
*,
|
|
keepdims: L[False] = ...,
|
|
) -> intp: ...
|
|
@overload
|
|
def argmax(
|
|
self,
|
|
axis: SupportsIndex = ...,
|
|
out: None = ...,
|
|
*,
|
|
keepdims: bool = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def argmax(
|
|
self,
|
|
axis: None | SupportsIndex = ...,
|
|
out: _NdArraySubClass = ...,
|
|
*,
|
|
keepdims: bool = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def argmin(
|
|
self,
|
|
axis: None = ...,
|
|
out: None = ...,
|
|
*,
|
|
keepdims: L[False] = ...,
|
|
) -> intp: ...
|
|
@overload
|
|
def argmin(
|
|
self,
|
|
axis: SupportsIndex = ...,
|
|
out: None = ...,
|
|
*,
|
|
keepdims: bool = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def argmin(
|
|
self,
|
|
axis: None | SupportsIndex = ...,
|
|
out: _NdArraySubClass = ...,
|
|
*,
|
|
keepdims: bool = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
def argsort(
|
|
self,
|
|
axis: None | SupportsIndex = ...,
|
|
kind: None | _SortKind = ...,
|
|
order: None | str | Sequence[str] = ...,
|
|
) -> ndarray: ...
|
|
|
|
@overload
|
|
def choose(
|
|
self,
|
|
choices: ArrayLike,
|
|
out: None = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> ndarray: ...
|
|
@overload
|
|
def choose(
|
|
self,
|
|
choices: ArrayLike,
|
|
out: _NdArraySubClass = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def clip(
|
|
self,
|
|
min: ArrayLike = ...,
|
|
max: None | ArrayLike = ...,
|
|
out: None = ...,
|
|
**kwargs: Any,
|
|
) -> ndarray: ...
|
|
@overload
|
|
def clip(
|
|
self,
|
|
min: None = ...,
|
|
max: ArrayLike = ...,
|
|
out: None = ...,
|
|
**kwargs: Any,
|
|
) -> ndarray: ...
|
|
@overload
|
|
def clip(
|
|
self,
|
|
min: ArrayLike = ...,
|
|
max: None | ArrayLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
**kwargs: Any,
|
|
) -> _NdArraySubClass: ...
|
|
@overload
|
|
def clip(
|
|
self,
|
|
min: None = ...,
|
|
max: ArrayLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
**kwargs: Any,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def compress(
|
|
self,
|
|
a: ArrayLike,
|
|
axis: None | SupportsIndex = ...,
|
|
out: None = ...,
|
|
) -> ndarray: ...
|
|
@overload
|
|
def compress(
|
|
self,
|
|
a: ArrayLike,
|
|
axis: None | SupportsIndex = ...,
|
|
out: _NdArraySubClass = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
def conj(self: _ArraySelf) -> _ArraySelf: ...
|
|
|
|
def conjugate(self: _ArraySelf) -> _ArraySelf: ...
|
|
|
|
@overload
|
|
def cumprod(
|
|
self,
|
|
axis: None | SupportsIndex = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: None = ...,
|
|
) -> ndarray: ...
|
|
@overload
|
|
def cumprod(
|
|
self,
|
|
axis: None | SupportsIndex = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def cumsum(
|
|
self,
|
|
axis: None | SupportsIndex = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: None = ...,
|
|
) -> ndarray: ...
|
|
@overload
|
|
def cumsum(
|
|
self,
|
|
axis: None | SupportsIndex = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def max(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
out: None = ...,
|
|
keepdims: bool = ...,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def max(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
keepdims: bool = ...,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def mean(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: None = ...,
|
|
keepdims: bool = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def mean(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
keepdims: bool = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def min(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
out: None = ...,
|
|
keepdims: bool = ...,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def min(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
keepdims: bool = ...,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
def newbyteorder(
|
|
self: _ArraySelf,
|
|
__new_order: _ByteOrder = ...,
|
|
) -> _ArraySelf: ...
|
|
|
|
@overload
|
|
def prod(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: None = ...,
|
|
keepdims: bool = ...,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def prod(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
keepdims: bool = ...,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def ptp(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
out: None = ...,
|
|
keepdims: bool = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def ptp(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
keepdims: bool = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def round(
|
|
self: _ArraySelf,
|
|
decimals: SupportsIndex = ...,
|
|
out: None = ...,
|
|
) -> _ArraySelf: ...
|
|
@overload
|
|
def round(
|
|
self,
|
|
decimals: SupportsIndex = ...,
|
|
out: _NdArraySubClass = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def std(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: None = ...,
|
|
ddof: float = ...,
|
|
keepdims: bool = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def std(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
ddof: float = ...,
|
|
keepdims: bool = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def sum(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: None = ...,
|
|
keepdims: bool = ...,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def sum(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
keepdims: bool = ...,
|
|
initial: _NumberLike_co = ...,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def var(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: None = ...,
|
|
ddof: float = ...,
|
|
keepdims: bool = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def var(
|
|
self,
|
|
axis: None | _ShapeLike = ...,
|
|
dtype: DTypeLike = ...,
|
|
out: _NdArraySubClass = ...,
|
|
ddof: float = ...,
|
|
keepdims: bool = ...,
|
|
*,
|
|
where: _ArrayLikeBool_co = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
_DType = TypeVar("_DType", bound=dtype[Any])
|
|
_DType_co = TypeVar("_DType_co", covariant=True, bound=dtype[Any])
|
|
_FlexDType = TypeVar("_FlexDType", bound=dtype[flexible])
|
|
|
|
# TODO: Set the `bound` to something more suitable once we
|
|
# have proper shape support
|
|
_ShapeType = TypeVar("_ShapeType", bound=Any)
|
|
_ShapeType2 = TypeVar("_ShapeType2", bound=Any)
|
|
_NumberType = TypeVar("_NumberType", bound=number[Any])
|
|
|
|
# There is currently no exhaustive way to type the buffer protocol,
|
|
# as it is implemented exclusively in the C API (python/typing#593)
|
|
_SupportsBuffer = Union[
|
|
bytes,
|
|
bytearray,
|
|
memoryview,
|
|
_array.array[Any],
|
|
mmap.mmap,
|
|
NDArray[Any],
|
|
generic,
|
|
]
|
|
|
|
_T = TypeVar("_T")
|
|
_T_co = TypeVar("_T_co", covariant=True)
|
|
_T_contra = TypeVar("_T_contra", contravariant=True)
|
|
_2Tuple = tuple[_T, _T]
|
|
_CastingKind = L["no", "equiv", "safe", "same_kind", "unsafe"]
|
|
|
|
_ArrayUInt_co = NDArray[Union[bool_, unsignedinteger[Any]]]
|
|
_ArrayInt_co = NDArray[Union[bool_, integer[Any]]]
|
|
_ArrayFloat_co = NDArray[Union[bool_, integer[Any], floating[Any]]]
|
|
_ArrayComplex_co = NDArray[Union[bool_, integer[Any], floating[Any], complexfloating[Any, Any]]]
|
|
_ArrayNumber_co = NDArray[Union[bool_, number[Any]]]
|
|
_ArrayTD64_co = NDArray[Union[bool_, integer[Any], timedelta64]]
|
|
|
|
# Introduce an alias for `dtype` to avoid naming conflicts.
|
|
_dtype = dtype
|
|
|
|
# `builtins.PyCapsule` unfortunately lacks annotations as of the moment;
|
|
# use `Any` as a stopgap measure
|
|
_PyCapsule = Any
|
|
|
|
class _SupportsItem(Protocol[_T_co]):
|
|
def item(self, args: Any, /) -> _T_co: ...
|
|
|
|
class _SupportsReal(Protocol[_T_co]):
|
|
@property
|
|
def real(self) -> _T_co: ...
|
|
|
|
class _SupportsImag(Protocol[_T_co]):
|
|
@property
|
|
def imag(self) -> _T_co: ...
|
|
|
|
class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]):
|
|
__hash__: ClassVar[None]
|
|
@property
|
|
def base(self) -> None | ndarray: ...
|
|
@property
|
|
def ndim(self) -> int: ...
|
|
@property
|
|
def size(self) -> int: ...
|
|
@property
|
|
def real(
|
|
self: ndarray[_ShapeType, dtype[_SupportsReal[_ScalarType]]], # type: ignore[type-var]
|
|
) -> ndarray[_ShapeType, _dtype[_ScalarType]]: ...
|
|
@real.setter
|
|
def real(self, value: ArrayLike) -> None: ...
|
|
@property
|
|
def imag(
|
|
self: ndarray[_ShapeType, dtype[_SupportsImag[_ScalarType]]], # type: ignore[type-var]
|
|
) -> ndarray[_ShapeType, _dtype[_ScalarType]]: ...
|
|
@imag.setter
|
|
def imag(self, value: ArrayLike) -> None: ...
|
|
def __new__(
|
|
cls: type[_ArraySelf],
|
|
shape: _ShapeLike,
|
|
dtype: DTypeLike = ...,
|
|
buffer: None | _SupportsBuffer = ...,
|
|
offset: SupportsIndex = ...,
|
|
strides: None | _ShapeLike = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> _ArraySelf: ...
|
|
|
|
if sys.version_info >= (3, 9):
|
|
def __class_getitem__(self, item: Any) -> GenericAlias: ...
|
|
|
|
@overload
|
|
def __array__(self, dtype: None = ..., /) -> ndarray[Any, _DType_co]: ...
|
|
@overload
|
|
def __array__(self, dtype: _DType, /) -> ndarray[Any, _DType]: ...
|
|
|
|
def __array_ufunc__(
|
|
self,
|
|
ufunc: ufunc,
|
|
method: L["__call__", "reduce", "reduceat", "accumulate", "outer", "inner"],
|
|
*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 flexiblity
|
|
def __array_finalize__(self, obj: None | NDArray[Any], /) -> None: ...
|
|
|
|
def __array_wrap__(
|
|
self,
|
|
array: ndarray[_ShapeType2, _DType],
|
|
context: None | tuple[ufunc, tuple[Any, ...], int] = ...,
|
|
/,
|
|
) -> ndarray[_ShapeType2, _DType]: ...
|
|
|
|
def __array_prepare__(
|
|
self,
|
|
array: ndarray[_ShapeType2, _DType],
|
|
context: None | tuple[ufunc, tuple[Any, ...], int] = ...,
|
|
/,
|
|
) -> ndarray[_ShapeType2, _DType]: ...
|
|
|
|
@overload
|
|
def __getitem__(self, key: (
|
|
NDArray[integer[Any]]
|
|
| NDArray[bool_]
|
|
| tuple[NDArray[integer[Any]] | NDArray[bool_], ...]
|
|
)) -> ndarray[Any, _DType_co]: ...
|
|
@overload
|
|
def __getitem__(self, key: SupportsIndex | tuple[SupportsIndex, ...]) -> Any: ...
|
|
@overload
|
|
def __getitem__(self, key: (
|
|
None
|
|
| slice
|
|
| ellipsis
|
|
| SupportsIndex
|
|
| _ArrayLikeInt_co
|
|
| tuple[None | slice | ellipsis | _ArrayLikeInt_co | SupportsIndex, ...]
|
|
)) -> ndarray[Any, _DType_co]: ...
|
|
@overload
|
|
def __getitem__(self: NDArray[void], key: str) -> NDArray[Any]: ...
|
|
@overload
|
|
def __getitem__(self: NDArray[void], key: list[str]) -> ndarray[_ShapeType, _dtype[void]]: ...
|
|
|
|
@property
|
|
def ctypes(self) -> _ctypes[int]: ...
|
|
@property
|
|
def shape(self) -> _Shape: ...
|
|
@shape.setter
|
|
def shape(self, value: _ShapeLike) -> None: ...
|
|
@property
|
|
def strides(self) -> _Shape: ...
|
|
@strides.setter
|
|
def strides(self, value: _ShapeLike) -> None: ...
|
|
def byteswap(self: _ArraySelf, inplace: bool = ...) -> _ArraySelf: ...
|
|
def fill(self, value: Any) -> None: ...
|
|
@property
|
|
def flat(self: _NdArraySubClass) -> flatiter[_NdArraySubClass]: ...
|
|
|
|
# Use the same output type as that of the underlying `generic`
|
|
@overload
|
|
def item(
|
|
self: ndarray[Any, _dtype[_SupportsItem[_T]]], # type: ignore[type-var]
|
|
*args: SupportsIndex,
|
|
) -> _T: ...
|
|
@overload
|
|
def item(
|
|
self: ndarray[Any, _dtype[_SupportsItem[_T]]], # type: ignore[type-var]
|
|
args: tuple[SupportsIndex, ...],
|
|
/,
|
|
) -> _T: ...
|
|
|
|
@overload
|
|
def itemset(self, value: Any, /) -> None: ...
|
|
@overload
|
|
def itemset(self, item: _ShapeLike, value: Any, /) -> None: ...
|
|
|
|
@overload
|
|
def resize(self, new_shape: _ShapeLike, /, *, refcheck: bool = ...) -> None: ...
|
|
@overload
|
|
def resize(self, *new_shape: SupportsIndex, refcheck: bool = ...) -> None: ...
|
|
|
|
def setflags(
|
|
self, write: bool = ..., align: bool = ..., uic: bool = ...
|
|
) -> None: ...
|
|
|
|
def squeeze(
|
|
self,
|
|
axis: None | SupportsIndex | tuple[SupportsIndex, ...] = ...,
|
|
) -> ndarray[Any, _DType_co]: ...
|
|
|
|
def swapaxes(
|
|
self,
|
|
axis1: SupportsIndex,
|
|
axis2: SupportsIndex,
|
|
) -> ndarray[Any, _DType_co]: ...
|
|
|
|
@overload
|
|
def transpose(self: _ArraySelf, axes: None | _ShapeLike, /) -> _ArraySelf: ...
|
|
@overload
|
|
def transpose(self: _ArraySelf, *axes: SupportsIndex) -> _ArraySelf: ...
|
|
|
|
def argpartition(
|
|
self,
|
|
kth: _ArrayLikeInt_co,
|
|
axis: None | SupportsIndex = ...,
|
|
kind: _PartitionKind = ...,
|
|
order: None | str | Sequence[str] = ...,
|
|
) -> ndarray[Any, _dtype[intp]]: ...
|
|
|
|
def diagonal(
|
|
self,
|
|
offset: SupportsIndex = ...,
|
|
axis1: SupportsIndex = ...,
|
|
axis2: SupportsIndex = ...,
|
|
) -> ndarray[Any, _DType_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: ...
|
|
@overload
|
|
def dot(self, b: ArrayLike, out: None = ...) -> Any: ... # type: ignore[misc]
|
|
@overload
|
|
def dot(self, b: ArrayLike, out: _NdArraySubClass) -> _NdArraySubClass: ...
|
|
|
|
# `nonzero()` is deprecated for 0d arrays/generics
|
|
def nonzero(self) -> tuple[ndarray[Any, _dtype[intp]], ...]: ...
|
|
|
|
def partition(
|
|
self,
|
|
kth: _ArrayLikeInt_co,
|
|
axis: SupportsIndex = ...,
|
|
kind: _PartitionKind = ...,
|
|
order: None | str | Sequence[str] = ...,
|
|
) -> None: ...
|
|
|
|
# `put` is technically available to `generic`,
|
|
# but is pointless as `generic`s are immutable
|
|
def put(
|
|
self,
|
|
ind: _ArrayLikeInt_co,
|
|
v: ArrayLike,
|
|
mode: _ModeKind = ...,
|
|
) -> None: ...
|
|
|
|
@overload
|
|
def searchsorted( # type: ignore[misc]
|
|
self, # >= 1D array
|
|
v: _ScalarLike_co, # 0D array-like
|
|
side: _SortSide = ...,
|
|
sorter: None | _ArrayLikeInt_co = ...,
|
|
) -> intp: ...
|
|
@overload
|
|
def searchsorted(
|
|
self, # >= 1D array
|
|
v: ArrayLike,
|
|
side: _SortSide = ...,
|
|
sorter: None | _ArrayLikeInt_co = ...,
|
|
) -> ndarray[Any, _dtype[intp]]: ...
|
|
|
|
def setfield(
|
|
self,
|
|
val: ArrayLike,
|
|
dtype: DTypeLike,
|
|
offset: SupportsIndex = ...,
|
|
) -> None: ...
|
|
|
|
def sort(
|
|
self,
|
|
axis: SupportsIndex = ...,
|
|
kind: None | _SortKind = ...,
|
|
order: None | str | Sequence[str] = ...,
|
|
) -> 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: _NdArraySubClass = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def take( # type: ignore[misc]
|
|
self: ndarray[Any, _dtype[_ScalarType]],
|
|
indices: _IntLike_co,
|
|
axis: None | SupportsIndex = ...,
|
|
out: None = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> _ScalarType: ...
|
|
@overload
|
|
def take( # type: ignore[misc]
|
|
self,
|
|
indices: _ArrayLikeInt_co,
|
|
axis: None | SupportsIndex = ...,
|
|
out: None = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> ndarray[Any, _DType_co]: ...
|
|
@overload
|
|
def take(
|
|
self,
|
|
indices: _ArrayLikeInt_co,
|
|
axis: None | SupportsIndex = ...,
|
|
out: _NdArraySubClass = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
def repeat(
|
|
self,
|
|
repeats: _ArrayLikeInt_co,
|
|
axis: None | SupportsIndex = ...,
|
|
) -> ndarray[Any, _DType_co]: ...
|
|
|
|
def flatten(
|
|
self,
|
|
order: _OrderKACF = ...,
|
|
) -> ndarray[Any, _DType_co]: ...
|
|
|
|
def ravel(
|
|
self,
|
|
order: _OrderKACF = ...,
|
|
) -> ndarray[Any, _DType_co]: ...
|
|
|
|
@overload
|
|
def reshape(
|
|
self, shape: _ShapeLike, /, *, order: _OrderACF = ...
|
|
) -> ndarray[Any, _DType_co]: ...
|
|
@overload
|
|
def reshape(
|
|
self, *shape: SupportsIndex, order: _OrderACF = ...
|
|
) -> ndarray[Any, _DType_co]: ...
|
|
|
|
@overload
|
|
def astype(
|
|
self,
|
|
dtype: _DTypeLike[_ScalarType],
|
|
order: _OrderKACF = ...,
|
|
casting: _CastingKind = ...,
|
|
subok: bool = ...,
|
|
copy: bool | _CopyMode = ...,
|
|
) -> NDArray[_ScalarType]: ...
|
|
@overload
|
|
def astype(
|
|
self,
|
|
dtype: DTypeLike,
|
|
order: _OrderKACF = ...,
|
|
casting: _CastingKind = ...,
|
|
subok: bool = ...,
|
|
copy: bool | _CopyMode = ...,
|
|
) -> NDArray[Any]: ...
|
|
|
|
@overload
|
|
def view(self: _ArraySelf) -> _ArraySelf: ...
|
|
@overload
|
|
def view(self, type: type[_NdArraySubClass]) -> _NdArraySubClass: ...
|
|
@overload
|
|
def view(self, dtype: _DTypeLike[_ScalarType]) -> NDArray[_ScalarType]: ...
|
|
@overload
|
|
def view(self, dtype: DTypeLike) -> NDArray[Any]: ...
|
|
@overload
|
|
def view(
|
|
self,
|
|
dtype: DTypeLike,
|
|
type: type[_NdArraySubClass],
|
|
) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def getfield(
|
|
self,
|
|
dtype: _DTypeLike[_ScalarType],
|
|
offset: SupportsIndex = ...
|
|
) -> NDArray[_ScalarType]: ...
|
|
@overload
|
|
def getfield(
|
|
self,
|
|
dtype: DTypeLike,
|
|
offset: SupportsIndex = ...
|
|
) -> NDArray[Any]: ...
|
|
|
|
# Dispatch to the underlying `generic` via protocols
|
|
def __int__(
|
|
self: ndarray[Any, _dtype[SupportsInt]], # type: ignore[type-var]
|
|
) -> int: ...
|
|
|
|
def __float__(
|
|
self: ndarray[Any, _dtype[SupportsFloat]], # type: ignore[type-var]
|
|
) -> float: ...
|
|
|
|
def __complex__(
|
|
self: ndarray[Any, _dtype[SupportsComplex]], # type: ignore[type-var]
|
|
) -> complex: ...
|
|
|
|
def __index__(
|
|
self: ndarray[Any, _dtype[SupportsIndex]], # type: ignore[type-var]
|
|
) -> int: ...
|
|
|
|
def __len__(self) -> int: ...
|
|
def __setitem__(self, key, value): ...
|
|
def __iter__(self) -> Any: ...
|
|
def __contains__(self, key) -> bool: ...
|
|
|
|
# The last overload is for catching recursive objects whose
|
|
# nesting is too deep.
|
|
# The first overload is for catching `bytes` (as they are a subtype of
|
|
# `Sequence[int]`) and `str`. As `str` is a recursive sequence of
|
|
# strings, it will pass through the final overload otherwise
|
|
|
|
@overload
|
|
def __lt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __lt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __lt__(self: NDArray[datetime64], other: _ArrayLikeDT64_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __lt__(self: NDArray[object_], other: Any) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __lt__(self: NDArray[Any], other: _ArrayLikeObject_co) -> NDArray[bool_]: ...
|
|
|
|
@overload
|
|
def __le__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __le__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __le__(self: NDArray[datetime64], other: _ArrayLikeDT64_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __le__(self: NDArray[object_], other: Any) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __le__(self: NDArray[Any], other: _ArrayLikeObject_co) -> NDArray[bool_]: ...
|
|
|
|
@overload
|
|
def __gt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __gt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __gt__(self: NDArray[datetime64], other: _ArrayLikeDT64_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __gt__(self: NDArray[object_], other: Any) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __gt__(self: NDArray[Any], other: _ArrayLikeObject_co) -> NDArray[bool_]: ...
|
|
|
|
@overload
|
|
def __ge__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __ge__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __ge__(self: NDArray[datetime64], other: _ArrayLikeDT64_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __ge__(self: NDArray[object_], other: Any) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __ge__(self: NDArray[Any], other: _ArrayLikeObject_co) -> NDArray[bool_]: ...
|
|
|
|
# Unary ops
|
|
@overload
|
|
def __abs__(self: NDArray[bool_]) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __abs__(self: NDArray[complexfloating[_NBit1, _NBit1]]) -> NDArray[floating[_NBit1]]: ...
|
|
@overload
|
|
def __abs__(self: NDArray[_NumberType]) -> NDArray[_NumberType]: ...
|
|
@overload
|
|
def __abs__(self: NDArray[timedelta64]) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __abs__(self: NDArray[object_]) -> Any: ...
|
|
|
|
@overload
|
|
def __invert__(self: NDArray[bool_]) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __invert__(self: NDArray[_IntType]) -> NDArray[_IntType]: ...
|
|
@overload
|
|
def __invert__(self: NDArray[object_]) -> Any: ...
|
|
|
|
@overload
|
|
def __pos__(self: NDArray[_NumberType]) -> NDArray[_NumberType]: ...
|
|
@overload
|
|
def __pos__(self: NDArray[timedelta64]) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __pos__(self: NDArray[object_]) -> Any: ...
|
|
|
|
@overload
|
|
def __neg__(self: NDArray[_NumberType]) -> NDArray[_NumberType]: ...
|
|
@overload
|
|
def __neg__(self: NDArray[timedelta64]) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __neg__(self: NDArray[object_]) -> Any: ...
|
|
|
|
# Binary ops
|
|
# NOTE: `ndarray` does not implement `__imatmul__`
|
|
@overload
|
|
def __matmul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __matmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __matmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __matmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __matmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
|
|
@overload
|
|
def __matmul__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __matmul__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __matmul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __rmatmul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmatmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmatmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmatmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmatmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
|
|
@overload
|
|
def __rmatmul__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __rmatmul__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __rmatmul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __mod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __mod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __mod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __mod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __mod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __mod__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __mod__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __rmod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[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[bool_], other: _ArrayLikeBool_co) -> _2Tuple[NDArray[int8]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __divmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[NDArray[unsignedinteger[Any]]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __divmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _2Tuple[NDArray[signedinteger[Any]]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __divmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[NDArray[floating[Any]]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __divmod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> tuple[NDArray[int64], NDArray[timedelta64]]: ...
|
|
|
|
@overload
|
|
def __rdivmod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> _2Tuple[NDArray[int8]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rdivmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[NDArray[unsignedinteger[Any]]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rdivmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _2Tuple[NDArray[signedinteger[Any]]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rdivmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[NDArray[floating[Any]]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rdivmod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> tuple[NDArray[int64], NDArray[timedelta64]]: ...
|
|
|
|
@overload
|
|
def __add__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __add__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __add__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __add__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __add__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __add__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __add__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[timedelta64]: ... # type: ignore[misc]
|
|
@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[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __add__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __radd__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __radd__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __radd__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __radd__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __radd__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __radd__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __radd__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[timedelta64]: ... # type: ignore[misc]
|
|
@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[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __radd__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __sub__(self: NDArray[_UnknownType], other: _ArrayLikeUnknown) -> NDArray[Any]: ...
|
|
@overload
|
|
def __sub__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NoReturn: ...
|
|
@overload
|
|
def __sub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __sub__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __sub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __sub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __sub__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __sub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[timedelta64]: ... # type: ignore[misc]
|
|
@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[_UnknownType], other: _ArrayLikeUnknown) -> NDArray[Any]: ...
|
|
@overload
|
|
def __rsub__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NoReturn: ...
|
|
@overload
|
|
def __rsub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rsub__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rsub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rsub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rsub__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[timedelta64]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> NDArray[datetime64]: ... # type: ignore[misc]
|
|
@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[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __mul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __mul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __mul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __mul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __mul__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __mul__(self: _ArrayTD64_co, other: _ArrayLikeFloat_co) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __mul__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __mul__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __mul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __rmul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rmul__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __rmul__(self: _ArrayTD64_co, other: _ArrayLikeFloat_co) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __rmul__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __rmul__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __rmul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __floordiv__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __floordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __floordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __floordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __floordiv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[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[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rfloordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rfloordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rfloordiv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[int64]: ...
|
|
@overload
|
|
def __rfloordiv__(self: NDArray[bool_], other: _ArrayLikeTD64_co) -> NoReturn: ...
|
|
@overload
|
|
def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> 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[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __pow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __pow__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __pow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __pow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
|
|
@overload
|
|
def __pow__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __pow__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __pow__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __rpow__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rpow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rpow__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rpow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rpow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
|
|
@overload
|
|
def __rpow__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __rpow__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __rpow__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __truediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> NDArray[float64]: ... # type: ignore[misc]
|
|
@overload
|
|
def __truediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __truediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __truediv__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __truediv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[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, other: _ArrayInt_co) -> NDArray[float64]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rtruediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rtruediv__(self: NDArray[number[Any]], other: _ArrayLikeNumber_co) -> NDArray[number[Any]]: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[float64]: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[bool_], other: _ArrayLikeTD64_co) -> NoReturn: ...
|
|
@overload
|
|
def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __rtruediv__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __lshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __lshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __lshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
|
|
@overload
|
|
def __lshift__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __lshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __rlshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rlshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rlshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
|
|
@overload
|
|
def __rlshift__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __rlshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __rshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
|
|
@overload
|
|
def __rshift__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __rshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __rrshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rrshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rrshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
|
|
@overload
|
|
def __rrshift__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __rrshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __and__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __and__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __and__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
|
|
@overload
|
|
def __and__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __and__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __rand__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rand__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rand__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
|
|
@overload
|
|
def __rand__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __rand__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __xor__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __xor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __xor__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
|
|
@overload
|
|
def __xor__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __xor__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __rxor__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rxor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __rxor__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
|
|
@overload
|
|
def __rxor__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __rxor__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __or__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __or__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __or__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
|
|
@overload
|
|
def __or__(self: NDArray[object_], other: Any) -> Any: ...
|
|
@overload
|
|
def __or__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ...
|
|
|
|
@overload
|
|
def __ror__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc]
|
|
@overload
|
|
def __ror__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc]
|
|
@overload
|
|
def __ror__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
|
|
@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
|
|
@overload
|
|
def __iadd__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co | _IntLike_co) -> NDArray[unsignedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[complexfloating[_NBit1, _NBit1]], other: _ArrayLikeComplex_co) -> NDArray[complexfloating[_NBit1, _NBit1]]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[timedelta64], other: _ArrayLikeTD64_co) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[datetime64], other: _ArrayLikeTD64_co) -> NDArray[datetime64]: ...
|
|
@overload
|
|
def __iadd__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def __isub__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co | _IntLike_co) -> NDArray[unsignedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __isub__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __isub__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ...
|
|
@overload
|
|
def __isub__(self: NDArray[complexfloating[_NBit1, _NBit1]], other: _ArrayLikeComplex_co) -> NDArray[complexfloating[_NBit1, _NBit1]]: ...
|
|
@overload
|
|
def __isub__(self: NDArray[timedelta64], other: _ArrayLikeTD64_co) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __isub__(self: NDArray[datetime64], other: _ArrayLikeTD64_co) -> NDArray[datetime64]: ...
|
|
@overload
|
|
def __isub__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def __imul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __imul__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co | _IntLike_co) -> NDArray[unsignedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __imul__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __imul__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ...
|
|
@overload
|
|
def __imul__(self: NDArray[complexfloating[_NBit1, _NBit1]], other: _ArrayLikeComplex_co) -> NDArray[complexfloating[_NBit1, _NBit1]]: ...
|
|
@overload
|
|
def __imul__(self: NDArray[timedelta64], other: _ArrayLikeFloat_co) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __imul__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def __itruediv__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ...
|
|
@overload
|
|
def __itruediv__(self: NDArray[complexfloating[_NBit1, _NBit1]], other: _ArrayLikeComplex_co) -> NDArray[complexfloating[_NBit1, _NBit1]]: ...
|
|
@overload
|
|
def __itruediv__(self: NDArray[timedelta64], other: _ArrayLikeBool_co) -> NoReturn: ...
|
|
@overload
|
|
def __itruediv__(self: NDArray[timedelta64], other: _ArrayLikeInt_co) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __itruediv__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def __ifloordiv__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co | _IntLike_co) -> NDArray[unsignedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __ifloordiv__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __ifloordiv__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ...
|
|
@overload
|
|
def __ifloordiv__(self: NDArray[complexfloating[_NBit1, _NBit1]], other: _ArrayLikeComplex_co) -> NDArray[complexfloating[_NBit1, _NBit1]]: ...
|
|
@overload
|
|
def __ifloordiv__(self: NDArray[timedelta64], other: _ArrayLikeBool_co) -> NoReturn: ...
|
|
@overload
|
|
def __ifloordiv__(self: NDArray[timedelta64], other: _ArrayLikeInt_co) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __ifloordiv__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def __ipow__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co | _IntLike_co) -> NDArray[unsignedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __ipow__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __ipow__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ...
|
|
@overload
|
|
def __ipow__(self: NDArray[complexfloating[_NBit1, _NBit1]], other: _ArrayLikeComplex_co) -> NDArray[complexfloating[_NBit1, _NBit1]]: ...
|
|
@overload
|
|
def __ipow__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def __imod__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co | _IntLike_co) -> NDArray[unsignedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __imod__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __imod__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ...
|
|
@overload
|
|
def __imod__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[timedelta64]: ...
|
|
@overload
|
|
def __imod__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def __ilshift__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co | _IntLike_co) -> NDArray[unsignedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __ilshift__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __ilshift__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def __irshift__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co | _IntLike_co) -> NDArray[unsignedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __irshift__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __irshift__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def __iand__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __iand__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co | _IntLike_co) -> NDArray[unsignedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __iand__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __iand__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def __ixor__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __ixor__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co | _IntLike_co) -> NDArray[unsignedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __ixor__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __ixor__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def __ior__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __ior__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co | _IntLike_co) -> NDArray[unsignedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __ior__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
|
|
@overload
|
|
def __ior__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
|
|
|
|
def __dlpack__(self: NDArray[number[Any]], *, stream: None = ...) -> _PyCapsule: ...
|
|
def __dlpack_device__(self) -> tuple[int, L[0]]: ...
|
|
|
|
# Keep `dtype` at the bottom to avoid name conflicts with `np.dtype`
|
|
@property
|
|
def dtype(self) -> _DType_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.
|
|
|
|
_ScalarType = TypeVar("_ScalarType", bound=generic)
|
|
_NBit1 = TypeVar("_NBit1", bound=NBitBase)
|
|
_NBit2 = TypeVar("_NBit2", bound=NBitBase)
|
|
|
|
class generic(_ArrayOrScalarCommon):
|
|
@abstractmethod
|
|
def __init__(self, *args: Any, **kwargs: Any) -> None: ...
|
|
@overload
|
|
def __array__(self: _ScalarType, dtype: None = ..., /) -> ndarray[Any, _dtype[_ScalarType]]: ...
|
|
@overload
|
|
def __array__(self, dtype: _DType, /) -> ndarray[Any, _DType]: ...
|
|
@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[()]: ...
|
|
def byteswap(self: _ScalarType, inplace: L[False] = ...) -> _ScalarType: ...
|
|
@property
|
|
def flat(self: _ScalarType) -> flatiter[ndarray[Any, _dtype[_ScalarType]]]: ...
|
|
|
|
@overload
|
|
def astype(
|
|
self,
|
|
dtype: _DTypeLike[_ScalarType],
|
|
order: _OrderKACF = ...,
|
|
casting: _CastingKind = ...,
|
|
subok: bool = ...,
|
|
copy: bool | _CopyMode = ...,
|
|
) -> _ScalarType: ...
|
|
@overload
|
|
def astype(
|
|
self,
|
|
dtype: DTypeLike,
|
|
order: _OrderKACF = ...,
|
|
casting: _CastingKind = ...,
|
|
subok: bool = ...,
|
|
copy: 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: _ScalarType,
|
|
type: type[ndarray[Any, Any]] = ...,
|
|
) -> _ScalarType: ...
|
|
@overload
|
|
def view(
|
|
self,
|
|
dtype: _DTypeLike[_ScalarType],
|
|
type: type[ndarray[Any, Any]] = ...,
|
|
) -> _ScalarType: ...
|
|
@overload
|
|
def view(
|
|
self,
|
|
dtype: DTypeLike,
|
|
type: type[ndarray[Any, Any]] = ...,
|
|
) -> Any: ...
|
|
|
|
@overload
|
|
def getfield(
|
|
self,
|
|
dtype: _DTypeLike[_ScalarType],
|
|
offset: SupportsIndex = ...
|
|
) -> _ScalarType: ...
|
|
@overload
|
|
def getfield(
|
|
self,
|
|
dtype: DTypeLike,
|
|
offset: SupportsIndex = ...
|
|
) -> Any: ...
|
|
|
|
def item(
|
|
self, args: L[0] | tuple[()] | tuple[L[0]] = ..., /,
|
|
) -> Any: ...
|
|
|
|
@overload
|
|
def take( # type: ignore[misc]
|
|
self: _ScalarType,
|
|
indices: _IntLike_co,
|
|
axis: None | SupportsIndex = ...,
|
|
out: None = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> _ScalarType: ...
|
|
@overload
|
|
def take( # type: ignore[misc]
|
|
self: _ScalarType,
|
|
indices: _ArrayLikeInt_co,
|
|
axis: None | SupportsIndex = ...,
|
|
out: None = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> ndarray[Any, _dtype[_ScalarType]]: ...
|
|
@overload
|
|
def take(
|
|
self,
|
|
indices: _ArrayLikeInt_co,
|
|
axis: None | SupportsIndex = ...,
|
|
out: _NdArraySubClass = ...,
|
|
mode: _ModeKind = ...,
|
|
) -> _NdArraySubClass: ...
|
|
|
|
def repeat(
|
|
self: _ScalarType,
|
|
repeats: _ArrayLikeInt_co,
|
|
axis: None | SupportsIndex = ...,
|
|
) -> ndarray[Any, _dtype[_ScalarType]]: ...
|
|
|
|
def flatten(
|
|
self: _ScalarType,
|
|
order: _OrderKACF = ...,
|
|
) -> ndarray[Any, _dtype[_ScalarType]]: ...
|
|
|
|
def ravel(
|
|
self: _ScalarType,
|
|
order: _OrderKACF = ...,
|
|
) -> ndarray[Any, _dtype[_ScalarType]]: ...
|
|
|
|
@overload
|
|
def reshape(
|
|
self: _ScalarType, shape: _ShapeLike, /, *, order: _OrderACF = ...
|
|
) -> ndarray[Any, _dtype[_ScalarType]]: ...
|
|
@overload
|
|
def reshape(
|
|
self: _ScalarType, *shape: SupportsIndex, order: _OrderACF = ...
|
|
) -> ndarray[Any, _dtype[_ScalarType]]: ...
|
|
|
|
def squeeze(
|
|
self: _ScalarType, axis: None | L[0] | tuple[()] = ...
|
|
) -> _ScalarType: ...
|
|
def transpose(self: _ScalarType, axes: None | tuple[()] = ..., /) -> _ScalarType: ...
|
|
# Keep `dtype` at the bottom to avoid name conflicts with `np.dtype`
|
|
@property
|
|
def dtype(self: _ScalarType) -> _dtype[_ScalarType]: ...
|
|
|
|
class number(generic, Generic[_NBit1]): # type: ignore
|
|
@property
|
|
def real(self: _ArraySelf) -> _ArraySelf: ...
|
|
@property
|
|
def imag(self: _ArraySelf) -> _ArraySelf: ...
|
|
if sys.version_info >= (3, 9):
|
|
def __class_getitem__(self, item: Any) -> GenericAlias: ...
|
|
def __int__(self) -> int: ...
|
|
def __float__(self) -> float: ...
|
|
def __complex__(self) -> complex: ...
|
|
def __neg__(self: _ArraySelf) -> _ArraySelf: ...
|
|
def __pos__(self: _ArraySelf) -> _ArraySelf: ...
|
|
def __abs__(self: _ArraySelf) -> _ArraySelf: ...
|
|
# Ensure that objects annotated as `number` support arithmetic operations
|
|
__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__: _ComparisonOp[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__le__: _ComparisonOp[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__gt__: _ComparisonOp[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__ge__: _ComparisonOp[_NumberLike_co, _ArrayLikeNumber_co]
|
|
|
|
class bool_(generic):
|
|
def __init__(self, value: object = ..., /) -> None: ...
|
|
def item(
|
|
self, args: L[0] | tuple[()] | tuple[L[0]] = ..., /,
|
|
) -> bool: ...
|
|
def tolist(self) -> bool: ...
|
|
@property
|
|
def real(self: _ArraySelf) -> _ArraySelf: ...
|
|
@property
|
|
def imag(self: _ArraySelf) -> _ArraySelf: ...
|
|
def __int__(self) -> int: ...
|
|
def __float__(self) -> float: ...
|
|
def __complex__(self) -> complex: ...
|
|
def __abs__(self: _ArraySelf) -> _ArraySelf: ...
|
|
__add__: _BoolOp[bool_]
|
|
__radd__: _BoolOp[bool_]
|
|
__sub__: _BoolSub
|
|
__rsub__: _BoolSub
|
|
__mul__: _BoolOp[bool_]
|
|
__rmul__: _BoolOp[bool_]
|
|
__floordiv__: _BoolOp[int8]
|
|
__rfloordiv__: _BoolOp[int8]
|
|
__pow__: _BoolOp[int8]
|
|
__rpow__: _BoolOp[int8]
|
|
__truediv__: _BoolTrueDiv
|
|
__rtruediv__: _BoolTrueDiv
|
|
def __invert__(self) -> bool_: ...
|
|
__lshift__: _BoolBitOp[int8]
|
|
__rlshift__: _BoolBitOp[int8]
|
|
__rshift__: _BoolBitOp[int8]
|
|
__rrshift__: _BoolBitOp[int8]
|
|
__and__: _BoolBitOp[bool_]
|
|
__rand__: _BoolBitOp[bool_]
|
|
__xor__: _BoolBitOp[bool_]
|
|
__rxor__: _BoolBitOp[bool_]
|
|
__or__: _BoolBitOp[bool_]
|
|
__ror__: _BoolBitOp[bool_]
|
|
__mod__: _BoolMod
|
|
__rmod__: _BoolMod
|
|
__divmod__: _BoolDivMod
|
|
__rdivmod__: _BoolDivMod
|
|
__lt__: _ComparisonOp[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__le__: _ComparisonOp[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__gt__: _ComparisonOp[_NumberLike_co, _ArrayLikeNumber_co]
|
|
__ge__: _ComparisonOp[_NumberLike_co, _ArrayLikeNumber_co]
|
|
|
|
class object_(generic):
|
|
def __init__(self, value: object = ..., /) -> None: ...
|
|
@property
|
|
def real(self: _ArraySelf) -> _ArraySelf: ...
|
|
@property
|
|
def imag(self: _ArraySelf) -> _ArraySelf: ...
|
|
# The 3 protocols below may or may not raise,
|
|
# depending on the underlying object
|
|
def __int__(self) -> int: ...
|
|
def __float__(self) -> float: ...
|
|
def __complex__(self) -> complex: ...
|
|
|
|
# The `datetime64` constructors requires an object with the three attributes below,
|
|
# and thus supports datetime duck typing
|
|
class _DatetimeScalar(Protocol):
|
|
@property
|
|
def day(self) -> int: ...
|
|
@property
|
|
def month(self) -> int: ...
|
|
@property
|
|
def year(self) -> int: ...
|
|
|
|
# TODO: `item`/`tolist` returns either `dt.date`, `dt.datetime` or `int`
|
|
# depending on the unit
|
|
class datetime64(generic):
|
|
@overload
|
|
def __init__(
|
|
self,
|
|
value: None | datetime64 | _CharLike_co | _DatetimeScalar = ...,
|
|
format: _CharLike_co | tuple[_CharLike_co, _IntLike_co] = ...,
|
|
/,
|
|
) -> None: ...
|
|
@overload
|
|
def __init__(
|
|
self,
|
|
value: int,
|
|
format: _CharLike_co | tuple[_CharLike_co, _IntLike_co],
|
|
/,
|
|
) -> None: ...
|
|
def __add__(self, other: _TD64Like_co) -> datetime64: ...
|
|
def __radd__(self, other: _TD64Like_co) -> datetime64: ...
|
|
@overload
|
|
def __sub__(self, other: datetime64) -> timedelta64: ...
|
|
@overload
|
|
def __sub__(self, other: _TD64Like_co) -> datetime64: ...
|
|
def __rsub__(self, other: datetime64) -> timedelta64: ...
|
|
__lt__: _ComparisonOp[datetime64, _ArrayLikeDT64_co]
|
|
__le__: _ComparisonOp[datetime64, _ArrayLikeDT64_co]
|
|
__gt__: _ComparisonOp[datetime64, _ArrayLikeDT64_co]
|
|
__ge__: _ComparisonOp[datetime64, _ArrayLikeDT64_co]
|
|
|
|
_IntValue = Union[SupportsInt, _CharLike_co, SupportsIndex]
|
|
_FloatValue = Union[None, _CharLike_co, SupportsFloat, SupportsIndex]
|
|
_ComplexValue = Union[
|
|
None,
|
|
_CharLike_co,
|
|
SupportsFloat,
|
|
SupportsComplex,
|
|
SupportsIndex,
|
|
complex, # `complex` is not a subtype of `SupportsComplex`
|
|
]
|
|
|
|
class integer(number[_NBit1]): # type: ignore
|
|
@property
|
|
def numerator(self: _ScalarType) -> _ScalarType: ...
|
|
@property
|
|
def denominator(self) -> L[1]: ...
|
|
@overload
|
|
def __round__(self, ndigits: None = ...) -> int: ...
|
|
@overload
|
|
def __round__(self: _ScalarType, ndigits: SupportsIndex) -> _ScalarType: ...
|
|
|
|
# NOTE: `__index__` is technically defined in the bottom-most
|
|
# sub-classes (`int64`, `uint32`, etc)
|
|
def item(
|
|
self, args: L[0] | tuple[()] | tuple[L[0]] = ..., /,
|
|
) -> int: ...
|
|
def tolist(self) -> int: ...
|
|
def is_integer(self) -> L[True]: ...
|
|
def bit_count(self: _ScalarType) -> int: ...
|
|
def __index__(self) -> int: ...
|
|
__truediv__: _IntTrueDiv[_NBit1]
|
|
__rtruediv__: _IntTrueDiv[_NBit1]
|
|
def __mod__(self, value: _IntLike_co) -> integer: ...
|
|
def __rmod__(self, value: _IntLike_co) -> integer: ...
|
|
def __invert__(self: _IntType) -> _IntType: ...
|
|
# 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: _IntValue = ..., /) -> 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_ = signedinteger[_NBitInt]
|
|
longlong = signedinteger[_NBitLongLong]
|
|
|
|
# TODO: `item`/`tolist` returns either `dt.timedelta` or `int`
|
|
# depending on the unit
|
|
class timedelta64(generic):
|
|
def __init__(
|
|
self,
|
|
value: None | int | _CharLike_co | dt.timedelta | timedelta64 = ...,
|
|
format: _CharLike_co | tuple[_CharLike_co, _IntLike_co] = ...,
|
|
/,
|
|
) -> None: ...
|
|
@property
|
|
def numerator(self: _ScalarType) -> _ScalarType: ...
|
|
@property
|
|
def denominator(self) -> L[1]: ...
|
|
|
|
# NOTE: Only a limited number of units support conversion
|
|
# to builtin scalar types: `Y`, `M`, `ns`, `ps`, `fs`, `as`
|
|
def __int__(self) -> int: ...
|
|
def __float__(self) -> float: ...
|
|
def __complex__(self) -> complex: ...
|
|
def __neg__(self: _ArraySelf) -> _ArraySelf: ...
|
|
def __pos__(self: _ArraySelf) -> _ArraySelf: ...
|
|
def __abs__(self: _ArraySelf) -> _ArraySelf: ...
|
|
def __add__(self, other: _TD64Like_co) -> timedelta64: ...
|
|
def __radd__(self, other: _TD64Like_co) -> timedelta64: ...
|
|
def __sub__(self, other: _TD64Like_co) -> timedelta64: ...
|
|
def __rsub__(self, other: _TD64Like_co) -> timedelta64: ...
|
|
def __mul__(self, other: _FloatLike_co) -> timedelta64: ...
|
|
def __rmul__(self, other: _FloatLike_co) -> timedelta64: ...
|
|
__truediv__: _TD64Div[float64]
|
|
__floordiv__: _TD64Div[int64]
|
|
def __rtruediv__(self, other: timedelta64) -> float64: ...
|
|
def __rfloordiv__(self, other: timedelta64) -> int64: ...
|
|
def __mod__(self, other: timedelta64) -> timedelta64: ...
|
|
def __rmod__(self, other: timedelta64) -> timedelta64: ...
|
|
def __divmod__(self, other: timedelta64) -> tuple[int64, timedelta64]: ...
|
|
def __rdivmod__(self, other: timedelta64) -> tuple[int64, timedelta64]: ...
|
|
__lt__: _ComparisonOp[_TD64Like_co, _ArrayLikeTD64_co]
|
|
__le__: _ComparisonOp[_TD64Like_co, _ArrayLikeTD64_co]
|
|
__gt__: _ComparisonOp[_TD64Like_co, _ArrayLikeTD64_co]
|
|
__ge__: _ComparisonOp[_TD64Like_co, _ArrayLikeTD64_co]
|
|
|
|
class unsignedinteger(integer[_NBit1]):
|
|
# NOTE: `uint64 + signedinteger -> float64`
|
|
def __init__(self, value: _IntValue = ..., /) -> 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 = unsignedinteger[_8Bit]
|
|
uint16 = unsignedinteger[_16Bit]
|
|
uint32 = unsignedinteger[_32Bit]
|
|
uint64 = unsignedinteger[_64Bit]
|
|
|
|
ubyte = unsignedinteger[_NBitByte]
|
|
ushort = unsignedinteger[_NBitShort]
|
|
uintc = unsignedinteger[_NBitIntC]
|
|
uintp = unsignedinteger[_NBitIntP]
|
|
uint = unsignedinteger[_NBitInt]
|
|
ulonglong = unsignedinteger[_NBitLongLong]
|
|
|
|
class inexact(number[_NBit1]): # type: ignore
|
|
def __getnewargs__(self: inexact[_64Bit]) -> tuple[float, ...]: ...
|
|
|
|
_IntType = TypeVar("_IntType", bound=integer)
|
|
_FloatType = TypeVar('_FloatType', bound=floating)
|
|
|
|
class floating(inexact[_NBit1]):
|
|
def __init__(self, value: _FloatValue = ..., /) -> None: ...
|
|
def item(
|
|
self, args: L[0] | tuple[()] | tuple[L[0]] = ...,
|
|
/,
|
|
) -> float: ...
|
|
def tolist(self) -> float: ...
|
|
def is_integer(self) -> bool: ...
|
|
def hex(self: float64) -> str: ...
|
|
@classmethod
|
|
def fromhex(cls: type[float64], string: str, /) -> float64: ...
|
|
def as_integer_ratio(self) -> tuple[int, int]: ...
|
|
if sys.version_info >= (3, 9):
|
|
def __ceil__(self: float64) -> int: ...
|
|
def __floor__(self: float64) -> int: ...
|
|
def __trunc__(self: float64) -> int: ...
|
|
def __getnewargs__(self: float64) -> tuple[float]: ...
|
|
def __getformat__(self: float64, typestr: L["double", "float"], /) -> str: ...
|
|
@overload
|
|
def __round__(self, ndigits: None = ...) -> int: ...
|
|
@overload
|
|
def __round__(self: _ScalarType, ndigits: SupportsIndex) -> _ScalarType: ...
|
|
__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]
|
|
|
|
float16 = floating[_16Bit]
|
|
float32 = floating[_32Bit]
|
|
float64 = floating[_64Bit]
|
|
|
|
half = floating[_NBitHalf]
|
|
single = floating[_NBitSingle]
|
|
double = floating[_NBitDouble]
|
|
float_ = floating[_NBitDouble]
|
|
longdouble = floating[_NBitLongDouble]
|
|
longfloat = 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], Generic[_NBit1, _NBit2]):
|
|
def __init__(self, value: _ComplexValue = ..., /) -> None: ...
|
|
def item(
|
|
self, args: L[0] | tuple[()] | tuple[L[0]] = ..., /,
|
|
) -> complex: ...
|
|
def tolist(self) -> complex: ...
|
|
@property
|
|
def real(self) -> floating[_NBit1]: ... # type: ignore[override]
|
|
@property
|
|
def imag(self) -> floating[_NBit2]: ... # type: ignore[override]
|
|
def __abs__(self) -> floating[_NBit1]: ... # type: ignore[override]
|
|
def __getnewargs__(self: complex128) -> tuple[float, float]: ...
|
|
# NOTE: Deprecated
|
|
# def __round__(self, ndigits=...): ...
|
|
__add__: _ComplexOp[_NBit1]
|
|
__radd__: _ComplexOp[_NBit1]
|
|
__sub__: _ComplexOp[_NBit1]
|
|
__rsub__: _ComplexOp[_NBit1]
|
|
__mul__: _ComplexOp[_NBit1]
|
|
__rmul__: _ComplexOp[_NBit1]
|
|
__truediv__: _ComplexOp[_NBit1]
|
|
__rtruediv__: _ComplexOp[_NBit1]
|
|
__pow__: _ComplexOp[_NBit1]
|
|
__rpow__: _ComplexOp[_NBit1]
|
|
|
|
complex64 = complexfloating[_32Bit, _32Bit]
|
|
complex128 = complexfloating[_64Bit, _64Bit]
|
|
|
|
csingle = complexfloating[_NBitSingle, _NBitSingle]
|
|
singlecomplex = complexfloating[_NBitSingle, _NBitSingle]
|
|
cdouble = complexfloating[_NBitDouble, _NBitDouble]
|
|
complex_ = complexfloating[_NBitDouble, _NBitDouble]
|
|
cfloat = complexfloating[_NBitDouble, _NBitDouble]
|
|
clongdouble = complexfloating[_NBitLongDouble, _NBitLongDouble]
|
|
clongfloat = complexfloating[_NBitLongDouble, _NBitLongDouble]
|
|
longcomplex = complexfloating[_NBitLongDouble, _NBitLongDouble]
|
|
|
|
class flexible(generic): ... # type: ignore
|
|
|
|
# TODO: `item`/`tolist` returns either `bytes` or `tuple`
|
|
# depending on whether or not it's used as an opaque bytes sequence
|
|
# or a structure
|
|
class void(flexible):
|
|
@overload
|
|
def __init__(self, value: _IntLike_co | bytes, /, dtype : None = ...) -> None: ...
|
|
@overload
|
|
def __init__(self, value: Any, /, dtype: _DTypeLikeVoid) -> None: ...
|
|
@property
|
|
def real(self: _ArraySelf) -> _ArraySelf: ...
|
|
@property
|
|
def imag(self: _ArraySelf) -> _ArraySelf: ...
|
|
def setfield(
|
|
self, val: ArrayLike, dtype: DTypeLike, offset: int = ...
|
|
) -> 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: ...
|
|
|
|
class character(flexible): # type: ignore
|
|
def __int__(self) -> int: ...
|
|
def __float__(self) -> float: ...
|
|
|
|
# NOTE: Most `np.bytes_` / `np.str_` methods return their
|
|
# builtin `bytes` / `str` counterpart
|
|
|
|
class bytes_(character, bytes):
|
|
@overload
|
|
def __init__(self, value: object = ..., /) -> None: ...
|
|
@overload
|
|
def __init__(
|
|
self, value: str, /, encoding: str = ..., errors: str = ...
|
|
) -> None: ...
|
|
def item(
|
|
self, args: L[0] | tuple[()] | tuple[L[0]] = ..., /,
|
|
) -> bytes: ...
|
|
def tolist(self) -> bytes: ...
|
|
|
|
string_ = bytes_
|
|
|
|
class str_(character, str):
|
|
@overload
|
|
def __init__(self, value: object = ..., /) -> None: ...
|
|
@overload
|
|
def __init__(
|
|
self, value: bytes, /, encoding: str = ..., errors: str = ...
|
|
) -> None: ...
|
|
def item(
|
|
self, args: L[0] | tuple[()] | tuple[L[0]] = ..., /,
|
|
) -> str: ...
|
|
def tolist(self) -> str: ...
|
|
|
|
unicode_ = str_
|
|
|
|
#
|
|
# Constants
|
|
#
|
|
|
|
Inf: Final[float]
|
|
Infinity: Final[float]
|
|
NAN: Final[float]
|
|
NINF: Final[float]
|
|
NZERO: Final[float]
|
|
NaN: Final[float]
|
|
PINF: Final[float]
|
|
PZERO: Final[float]
|
|
e: Final[float]
|
|
euler_gamma: Final[float]
|
|
inf: Final[float]
|
|
infty: Final[float]
|
|
nan: Final[float]
|
|
pi: Final[float]
|
|
|
|
ERR_IGNORE: L[0]
|
|
ERR_WARN: L[1]
|
|
ERR_RAISE: L[2]
|
|
ERR_CALL: L[3]
|
|
ERR_PRINT: L[4]
|
|
ERR_LOG: L[5]
|
|
ERR_DEFAULT: L[521]
|
|
|
|
SHIFT_DIVIDEBYZERO: L[0]
|
|
SHIFT_OVERFLOW: L[3]
|
|
SHIFT_UNDERFLOW: L[6]
|
|
SHIFT_INVALID: L[9]
|
|
|
|
FPE_DIVIDEBYZERO: L[1]
|
|
FPE_OVERFLOW: L[2]
|
|
FPE_UNDERFLOW: L[4]
|
|
FPE_INVALID: L[8]
|
|
|
|
FLOATING_POINT_SUPPORT: L[1]
|
|
UFUNC_BUFSIZE_DEFAULT = BUFSIZE
|
|
|
|
little_endian: Final[bool]
|
|
True_: Final[bool_]
|
|
False_: Final[bool_]
|
|
|
|
UFUNC_PYVALS_NAME: L["UFUNC_PYVALS"]
|
|
|
|
newaxis: None
|
|
|
|
# See `numpy._typing._ufunc` for more concrete nin-/nout-specific stubs
|
|
@final
|
|
class ufunc:
|
|
@property
|
|
def __name__(self) -> str: ...
|
|
@property
|
|
def __doc__(self) -> str: ...
|
|
__call__: Callable[..., Any]
|
|
@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[str]: ...
|
|
# 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) -> None | str: ...
|
|
# 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.
|
|
reduce: Any
|
|
accumulate: Any
|
|
reduceat: Any
|
|
outer: Any
|
|
# Similarly at won't be defined for ufuncs that return multiple
|
|
# outputs, so we can't type it very precisely.
|
|
at: Any
|
|
|
|
# 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_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]
|
|
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]
|
|
|
|
abs = absolute
|
|
|
|
class _CopyMode(enum.Enum):
|
|
ALWAYS: L[True]
|
|
IF_NEEDED: L[False]
|
|
NEVER: L[2]
|
|
|
|
# Warnings
|
|
class ModuleDeprecationWarning(DeprecationWarning): ...
|
|
class VisibleDeprecationWarning(UserWarning): ...
|
|
class ComplexWarning(RuntimeWarning): ...
|
|
class RankWarning(UserWarning): ...
|
|
|
|
# Errors
|
|
class TooHardError(RuntimeError): ...
|
|
|
|
class AxisError(ValueError, IndexError):
|
|
axis: None | int
|
|
ndim: None | int
|
|
@overload
|
|
def __init__(self, axis: str, ndim: None = ..., msg_prefix: None = ...) -> None: ...
|
|
@overload
|
|
def __init__(self, axis: int, ndim: int, msg_prefix: None | str = ...) -> None: ...
|
|
|
|
_CallType = TypeVar("_CallType", bound=_ErrFunc | _SupportsWrite[str])
|
|
|
|
class errstate(Generic[_CallType], ContextDecorator):
|
|
call: _CallType
|
|
kwargs: _ErrDictOptional
|
|
|
|
# Expand `**kwargs` into explicit keyword-only arguments
|
|
def __init__(
|
|
self,
|
|
*,
|
|
call: _CallType = ...,
|
|
all: None | _ErrKind = ...,
|
|
divide: None | _ErrKind = ...,
|
|
over: None | _ErrKind = ...,
|
|
under: None | _ErrKind = ...,
|
|
invalid: None | _ErrKind = ...,
|
|
) -> None: ...
|
|
def __enter__(self) -> None: ...
|
|
def __exit__(
|
|
self,
|
|
exc_type: None | type[BaseException],
|
|
exc_value: None | BaseException,
|
|
traceback: None | TracebackType,
|
|
/,
|
|
) -> None: ...
|
|
|
|
@contextmanager
|
|
def _no_nep50_warning() -> Generator[None, None, None]: ...
|
|
def _get_promotion_state() -> str: ...
|
|
def _set_promotion_state(state: str, /) -> None: ...
|
|
|
|
class ndenumerate(Generic[_ScalarType]):
|
|
iter: flatiter[NDArray[_ScalarType]]
|
|
@overload
|
|
def __new__(
|
|
cls, arr: _FiniteNestedSequence[_SupportsArray[dtype[_ScalarType]]],
|
|
) -> ndenumerate[_ScalarType]: ...
|
|
@overload
|
|
def __new__(cls, arr: str | _NestedSequence[str]) -> ndenumerate[str_]: ...
|
|
@overload
|
|
def __new__(cls, arr: bytes | _NestedSequence[bytes]) -> ndenumerate[bytes_]: ...
|
|
@overload
|
|
def __new__(cls, arr: bool | _NestedSequence[bool]) -> ndenumerate[bool_]: ...
|
|
@overload
|
|
def __new__(cls, arr: int | _NestedSequence[int]) -> ndenumerate[int_]: ...
|
|
@overload
|
|
def __new__(cls, arr: float | _NestedSequence[float]) -> ndenumerate[float_]: ...
|
|
@overload
|
|
def __new__(cls, arr: complex | _NestedSequence[complex]) -> ndenumerate[complex_]: ...
|
|
def __next__(self: ndenumerate[_ScalarType]) -> tuple[_Shape, _ScalarType]: ...
|
|
def __iter__(self: _T) -> _T: ...
|
|
|
|
class ndindex:
|
|
@overload
|
|
def __init__(self, shape: tuple[SupportsIndex, ...], /) -> None: ...
|
|
@overload
|
|
def __init__(self, *shape: SupportsIndex) -> None: ...
|
|
def __iter__(self: _T) -> _T: ...
|
|
def __next__(self) -> _Shape: ...
|
|
|
|
class DataSource:
|
|
def __init__(
|
|
self,
|
|
destpath: None | str | os.PathLike[str] = ...,
|
|
) -> None: ...
|
|
def __del__(self) -> None: ...
|
|
def abspath(self, path: str) -> str: ...
|
|
def exists(self, path: str) -> bool: ...
|
|
|
|
# Whether the file-object is opened in string or bytes mode (by default)
|
|
# depends on the file-extension of `path`
|
|
def open(
|
|
self,
|
|
path: str,
|
|
mode: str = ...,
|
|
encoding: None | str = ...,
|
|
newline: None | str = ...,
|
|
) -> IO[Any]: ...
|
|
|
|
# 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) -> _Shape: ...
|
|
@property
|
|
def size(self) -> int: ...
|
|
def __next__(self) -> tuple[Any, ...]: ...
|
|
def __iter__(self: _T) -> _T: ...
|
|
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[bool_]: ...
|
|
@property
|
|
def holidays(self) -> NDArray[datetime64]: ...
|
|
|
|
class finfo(Generic[_FloatType]):
|
|
dtype: dtype[_FloatType]
|
|
bits: int
|
|
eps: _FloatType
|
|
epsneg: _FloatType
|
|
iexp: int
|
|
machep: int
|
|
max: _FloatType
|
|
maxexp: int
|
|
min: _FloatType
|
|
minexp: int
|
|
negep: int
|
|
nexp: int
|
|
nmant: int
|
|
precision: int
|
|
resolution: _FloatType
|
|
smallest_subnormal: _FloatType
|
|
@property
|
|
def smallest_normal(self) -> _FloatType: ...
|
|
@property
|
|
def tiny(self) -> _FloatType: ...
|
|
@overload
|
|
def __new__(
|
|
cls, dtype: inexact[_NBit1] | _DTypeLike[inexact[_NBit1]]
|
|
) -> finfo[floating[_NBit1]]: ...
|
|
@overload
|
|
def __new__(
|
|
cls, dtype: complex | float | type[complex] | type[float]
|
|
) -> finfo[float_]: ...
|
|
@overload
|
|
def __new__(
|
|
cls, dtype: str
|
|
) -> finfo[floating[Any]]: ...
|
|
|
|
class iinfo(Generic[_IntType]):
|
|
dtype: dtype[_IntType]
|
|
kind: str
|
|
bits: int
|
|
key: str
|
|
@property
|
|
def min(self) -> int: ...
|
|
@property
|
|
def max(self) -> int: ...
|
|
|
|
@overload
|
|
def __new__(cls, dtype: _IntType | _DTypeLike[_IntType]) -> iinfo[_IntType]: ...
|
|
@overload
|
|
def __new__(cls, dtype: int | type[int]) -> iinfo[int_]: ...
|
|
@overload
|
|
def __new__(cls, dtype: str) -> iinfo[Any]: ...
|
|
|
|
class format_parser:
|
|
dtype: dtype[void]
|
|
def __init__(
|
|
self,
|
|
formats: DTypeLike,
|
|
names: None | str | Sequence[str],
|
|
titles: None | str | Sequence[str],
|
|
aligned: bool = ...,
|
|
byteorder: None | _ByteOrder = ...,
|
|
) -> None: ...
|
|
|
|
class recarray(ndarray[_ShapeType, _DType_co]):
|
|
# NOTE: While not strictly mandatory, we're demanding here that arguments
|
|
# for the `format_parser`- and `dtype`-based dtype constructors are
|
|
# mutually exclusive
|
|
@overload
|
|
def __new__(
|
|
subtype,
|
|
shape: _ShapeLike,
|
|
dtype: None = ...,
|
|
buf: None | _SupportsBuffer = ...,
|
|
offset: SupportsIndex = ...,
|
|
strides: None | _ShapeLike = ...,
|
|
*,
|
|
formats: DTypeLike,
|
|
names: None | str | Sequence[str] = ...,
|
|
titles: None | str | Sequence[str] = ...,
|
|
byteorder: None | _ByteOrder = ...,
|
|
aligned: bool = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> recarray[Any, dtype[record]]: ...
|
|
@overload
|
|
def __new__(
|
|
subtype,
|
|
shape: _ShapeLike,
|
|
dtype: DTypeLike,
|
|
buf: None | _SupportsBuffer = ...,
|
|
offset: SupportsIndex = ...,
|
|
strides: None | _ShapeLike = ...,
|
|
formats: None = ...,
|
|
names: None = ...,
|
|
titles: None = ...,
|
|
byteorder: None = ...,
|
|
aligned: L[False] = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> recarray[Any, dtype[Any]]: ...
|
|
def __array_finalize__(self, obj: object) -> None: ...
|
|
def __getattribute__(self, attr: str) -> Any: ...
|
|
def __setattr__(self, attr: str, val: ArrayLike) -> None: ...
|
|
@overload
|
|
def __getitem__(self, indx: (
|
|
SupportsIndex
|
|
| _ArrayLikeInt_co
|
|
| tuple[SupportsIndex | _ArrayLikeInt_co, ...]
|
|
)) -> Any: ...
|
|
@overload
|
|
def __getitem__(self: recarray[Any, dtype[void]], indx: (
|
|
None
|
|
| slice
|
|
| ellipsis
|
|
| SupportsIndex
|
|
| _ArrayLikeInt_co
|
|
| tuple[None | slice | ellipsis | _ArrayLikeInt_co | SupportsIndex, ...]
|
|
)) -> recarray[Any, _DType_co]: ...
|
|
@overload
|
|
def __getitem__(self, indx: (
|
|
None
|
|
| slice
|
|
| ellipsis
|
|
| SupportsIndex
|
|
| _ArrayLikeInt_co
|
|
| tuple[None | slice | ellipsis | _ArrayLikeInt_co | SupportsIndex, ...]
|
|
)) -> ndarray[Any, _DType_co]: ...
|
|
@overload
|
|
def __getitem__(self, indx: str) -> NDArray[Any]: ...
|
|
@overload
|
|
def __getitem__(self, indx: list[str]) -> recarray[_ShapeType, dtype[record]]: ...
|
|
@overload
|
|
def field(self, attr: int | str, val: None = ...) -> Any: ...
|
|
@overload
|
|
def field(self, attr: int | str, val: ArrayLike) -> None: ...
|
|
|
|
class record(void):
|
|
def __getattribute__(self, attr: str) -> Any: ...
|
|
def __setattr__(self, attr: str, val: ArrayLike) -> None: ...
|
|
def pprint(self) -> str: ...
|
|
@overload
|
|
def __getitem__(self, key: str | SupportsIndex) -> Any: ...
|
|
@overload
|
|
def __getitem__(self, key: list[str]) -> record: ...
|
|
|
|
_NDIterFlagsKind = 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",
|
|
]
|
|
|
|
_NDIterOpFlagsKind = L[
|
|
"aligned",
|
|
"allocate",
|
|
"arraymask",
|
|
"copy",
|
|
"config",
|
|
"nbo",
|
|
"no_subtype",
|
|
"no_broadcast",
|
|
"overlap_assume_elementwise",
|
|
"readonly",
|
|
"readwrite",
|
|
"updateifcopy",
|
|
"virtual",
|
|
"writeonly",
|
|
"writemasked"
|
|
]
|
|
|
|
@final
|
|
class nditer:
|
|
def __new__(
|
|
cls,
|
|
op: ArrayLike | Sequence[ArrayLike],
|
|
flags: None | Sequence[_NDIterFlagsKind] = ...,
|
|
op_flags: None | Sequence[Sequence[_NDIterOpFlagsKind]] = ...,
|
|
op_dtypes: DTypeLike | Sequence[DTypeLike] = ...,
|
|
order: _OrderKACF = ...,
|
|
casting: _CastingKind = ...,
|
|
op_axes: None | Sequence[Sequence[SupportsIndex]] = ...,
|
|
itershape: None | _ShapeLike = ...,
|
|
buffersize: SupportsIndex = ...,
|
|
) -> nditer: ...
|
|
def __enter__(self) -> nditer: ...
|
|
def __exit__(
|
|
self,
|
|
exc_type: None | type[BaseException],
|
|
exc_value: None | BaseException,
|
|
traceback: None | TracebackType,
|
|
) -> 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) -> bool: ...
|
|
def remove_axis(self, i: SupportsIndex, /) -> None: ...
|
|
def remove_multi_index(self) -> None: ...
|
|
def reset(self) -> None: ...
|
|
@property
|
|
def dtypes(self) -> tuple[dtype[Any], ...]: ...
|
|
@property
|
|
def finished(self) -> bool: ...
|
|
@property
|
|
def has_delayed_bufalloc(self) -> bool: ...
|
|
@property
|
|
def has_index(self) -> bool: ...
|
|
@property
|
|
def has_multi_index(self) -> bool: ...
|
|
@property
|
|
def index(self) -> int: ...
|
|
@property
|
|
def iterationneedsapi(self) -> 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], ...]: ...
|
|
|
|
_MemMapModeKind = L[
|
|
"readonly", "r",
|
|
"copyonwrite", "c",
|
|
"readwrite", "r+",
|
|
"write", "w+",
|
|
]
|
|
|
|
class memmap(ndarray[_ShapeType, _DType_co]):
|
|
__array_priority__: ClassVar[float]
|
|
filename: str | None
|
|
offset: int
|
|
mode: str
|
|
@overload
|
|
def __new__(
|
|
subtype,
|
|
filename: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _MemMapIOProtocol,
|
|
dtype: type[uint8] = ...,
|
|
mode: _MemMapModeKind = ...,
|
|
offset: int = ...,
|
|
shape: None | int | tuple[int, ...] = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> memmap[Any, dtype[uint8]]: ...
|
|
@overload
|
|
def __new__(
|
|
subtype,
|
|
filename: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _MemMapIOProtocol,
|
|
dtype: _DTypeLike[_ScalarType],
|
|
mode: _MemMapModeKind = ...,
|
|
offset: int = ...,
|
|
shape: None | int | tuple[int, ...] = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> memmap[Any, dtype[_ScalarType]]: ...
|
|
@overload
|
|
def __new__(
|
|
subtype,
|
|
filename: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _MemMapIOProtocol,
|
|
dtype: DTypeLike,
|
|
mode: _MemMapModeKind = ...,
|
|
offset: int = ...,
|
|
shape: None | int | tuple[int, ...] = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> memmap[Any, dtype[Any]]: ...
|
|
def __array_finalize__(self, obj: object) -> None: ...
|
|
def __array_wrap__(
|
|
self,
|
|
array: memmap[_ShapeType, _DType_co],
|
|
context: None | tuple[ufunc, tuple[Any, ...], int] = ...,
|
|
) -> 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: bool
|
|
signature: None | str
|
|
otypes: None | str
|
|
excluded: set[int | str]
|
|
__doc__: None | str
|
|
def __init__(
|
|
self,
|
|
pyfunc: Callable[..., Any],
|
|
otypes: None | str | Iterable[DTypeLike] = ...,
|
|
doc: None | str = ...,
|
|
excluded: None | Iterable[int | str] = ...,
|
|
cache: bool = ...,
|
|
signature: None | str = ...,
|
|
) -> None: ...
|
|
def __call__(self, *args: Any, **kwargs: Any) -> Any: ...
|
|
|
|
class poly1d:
|
|
@property
|
|
def variable(self) -> str: ...
|
|
@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
|
|
|
|
@overload
|
|
def __array__(self, t: None = ...) -> NDArray[Any]: ...
|
|
@overload
|
|
def __array__(self, t: _DType) -> ndarray[Any, _DType]: ...
|
|
|
|
@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: bool = ...,
|
|
variable: None | str = ...,
|
|
) -> 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 __div__(self, other: ArrayLike) -> poly1d: ...
|
|
def __truediv__(self, other: ArrayLike) -> poly1d: ...
|
|
def __rdiv__(self, other: ArrayLike) -> poly1d: ...
|
|
def __rtruediv__(self, other: ArrayLike) -> poly1d: ...
|
|
def __getitem__(self, val: int) -> Any: ...
|
|
def __setitem__(self, key: int, val: Any) -> None: ...
|
|
def __iter__(self) -> Iterator[Any]: ...
|
|
def deriv(self, m: SupportsInt | SupportsIndex = ...) -> poly1d: ...
|
|
def integ(
|
|
self,
|
|
m: SupportsInt | SupportsIndex = ...,
|
|
k: None | _ArrayLikeComplex_co | _ArrayLikeObject_co = ...,
|
|
) -> poly1d: ...
|
|
|
|
class matrix(ndarray[_ShapeType, _DType_co]):
|
|
__array_priority__: ClassVar[float]
|
|
def __new__(
|
|
subtype,
|
|
data: ArrayLike,
|
|
dtype: DTypeLike = ...,
|
|
copy: bool = ...,
|
|
) -> matrix[Any, Any]: ...
|
|
def __array_finalize__(self, obj: object) -> None: ...
|
|
|
|
@overload
|
|
def __getitem__(self, key: (
|
|
SupportsIndex
|
|
| _ArrayLikeInt_co
|
|
| tuple[SupportsIndex | _ArrayLikeInt_co, ...]
|
|
)) -> Any: ...
|
|
@overload
|
|
def __getitem__(self, key: (
|
|
None
|
|
| slice
|
|
| ellipsis
|
|
| SupportsIndex
|
|
| _ArrayLikeInt_co
|
|
| tuple[None | slice | ellipsis | _ArrayLikeInt_co | SupportsIndex, ...]
|
|
)) -> matrix[Any, _DType_co]: ...
|
|
@overload
|
|
def __getitem__(self: NDArray[void], key: str) -> matrix[Any, dtype[Any]]: ...
|
|
@overload
|
|
def __getitem__(self: NDArray[void], key: list[str]) -> matrix[_ShapeType, dtype[void]]: ...
|
|
|
|
def __mul__(self, other: ArrayLike) -> matrix[Any, Any]: ...
|
|
def __rmul__(self, other: ArrayLike) -> matrix[Any, Any]: ...
|
|
def __imul__(self, other: ArrayLike) -> matrix[_ShapeType, _DType_co]: ...
|
|
def __pow__(self, other: ArrayLike) -> matrix[Any, Any]: ...
|
|
def __ipow__(self, other: ArrayLike) -> matrix[_ShapeType, _DType_co]: ...
|
|
|
|
@overload
|
|
def sum(self, axis: None = ..., dtype: DTypeLike = ..., out: None = ...) -> Any: ...
|
|
@overload
|
|
def sum(self, axis: _ShapeLike, dtype: DTypeLike = ..., out: None = ...) -> matrix[Any, Any]: ...
|
|
@overload
|
|
def sum(self, axis: None | _ShapeLike = ..., dtype: DTypeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def mean(self, axis: None = ..., dtype: DTypeLike = ..., out: None = ...) -> Any: ...
|
|
@overload
|
|
def mean(self, axis: _ShapeLike, dtype: DTypeLike = ..., out: None = ...) -> matrix[Any, Any]: ...
|
|
@overload
|
|
def mean(self, axis: None | _ShapeLike = ..., dtype: DTypeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def std(self, axis: None = ..., dtype: DTypeLike = ..., out: None = ..., ddof: float = ...) -> Any: ...
|
|
@overload
|
|
def std(self, axis: _ShapeLike, dtype: DTypeLike = ..., out: None = ..., ddof: float = ...) -> matrix[Any, Any]: ...
|
|
@overload
|
|
def std(self, axis: None | _ShapeLike = ..., dtype: DTypeLike = ..., out: _NdArraySubClass = ..., ddof: float = ...) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def var(self, axis: None = ..., dtype: DTypeLike = ..., out: None = ..., ddof: float = ...) -> Any: ...
|
|
@overload
|
|
def var(self, axis: _ShapeLike, dtype: DTypeLike = ..., out: None = ..., ddof: float = ...) -> matrix[Any, Any]: ...
|
|
@overload
|
|
def var(self, axis: None | _ShapeLike = ..., dtype: DTypeLike = ..., out: _NdArraySubClass = ..., ddof: float = ...) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def prod(self, axis: None = ..., dtype: DTypeLike = ..., out: None = ...) -> Any: ...
|
|
@overload
|
|
def prod(self, axis: _ShapeLike, dtype: DTypeLike = ..., out: None = ...) -> matrix[Any, Any]: ...
|
|
@overload
|
|
def prod(self, axis: None | _ShapeLike = ..., dtype: DTypeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def any(self, axis: None = ..., out: None = ...) -> bool_: ...
|
|
@overload
|
|
def any(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, dtype[bool_]]: ...
|
|
@overload
|
|
def any(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def all(self, axis: None = ..., out: None = ...) -> bool_: ...
|
|
@overload
|
|
def all(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, dtype[bool_]]: ...
|
|
@overload
|
|
def all(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def max(self: NDArray[_ScalarType], axis: None = ..., out: None = ...) -> _ScalarType: ...
|
|
@overload
|
|
def max(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, _DType_co]: ...
|
|
@overload
|
|
def max(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def min(self: NDArray[_ScalarType], axis: None = ..., out: None = ...) -> _ScalarType: ...
|
|
@overload
|
|
def min(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, _DType_co]: ...
|
|
@overload
|
|
def min(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def argmax(self: NDArray[_ScalarType], axis: None = ..., out: None = ...) -> intp: ...
|
|
@overload
|
|
def argmax(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, dtype[intp]]: ...
|
|
@overload
|
|
def argmax(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def argmin(self: NDArray[_ScalarType], axis: None = ..., out: None = ...) -> intp: ...
|
|
@overload
|
|
def argmin(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, dtype[intp]]: ...
|
|
@overload
|
|
def argmin(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
|
|
|
|
@overload
|
|
def ptp(self: NDArray[_ScalarType], axis: None = ..., out: None = ...) -> _ScalarType: ...
|
|
@overload
|
|
def ptp(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, _DType_co]: ...
|
|
@overload
|
|
def ptp(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
|
|
|
|
def squeeze(self, axis: None | _ShapeLike = ...) -> matrix[Any, _DType_co]: ...
|
|
def tolist(self: matrix[Any, dtype[_SupportsItem[_T]]]) -> list[list[_T]]: ... # type: ignore[typevar]
|
|
def ravel(self, order: _OrderKACF = ...) -> matrix[Any, _DType_co]: ...
|
|
def flatten(self, order: _OrderKACF = ...) -> matrix[Any, _DType_co]: ...
|
|
|
|
@property
|
|
def T(self) -> matrix[Any, _DType_co]: ...
|
|
@property
|
|
def I(self) -> matrix[Any, Any]: ...
|
|
@property
|
|
def A(self) -> ndarray[_ShapeType, _DType_co]: ...
|
|
@property
|
|
def A1(self) -> ndarray[Any, _DType_co]: ...
|
|
@property
|
|
def H(self) -> matrix[Any, _DType_co]: ...
|
|
def getT(self) -> matrix[Any, _DType_co]: ...
|
|
def getI(self) -> matrix[Any, Any]: ...
|
|
def getA(self) -> ndarray[_ShapeType, _DType_co]: ...
|
|
def getA1(self) -> ndarray[Any, _DType_co]: ...
|
|
def getH(self) -> matrix[Any, _DType_co]: ...
|
|
|
|
_CharType = TypeVar("_CharType", str_, bytes_)
|
|
_CharDType = TypeVar("_CharDType", dtype[str_], dtype[bytes_])
|
|
_CharArray = chararray[Any, dtype[_CharType]]
|
|
|
|
class chararray(ndarray[_ShapeType, _CharDType]):
|
|
@overload
|
|
def __new__(
|
|
subtype,
|
|
shape: _ShapeLike,
|
|
itemsize: SupportsIndex | SupportsInt = ...,
|
|
unicode: L[False] = ...,
|
|
buffer: _SupportsBuffer = ...,
|
|
offset: SupportsIndex = ...,
|
|
strides: _ShapeLike = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> chararray[Any, dtype[bytes_]]: ...
|
|
@overload
|
|
def __new__(
|
|
subtype,
|
|
shape: _ShapeLike,
|
|
itemsize: SupportsIndex | SupportsInt = ...,
|
|
unicode: L[True] = ...,
|
|
buffer: _SupportsBuffer = ...,
|
|
offset: SupportsIndex = ...,
|
|
strides: _ShapeLike = ...,
|
|
order: _OrderKACF = ...,
|
|
) -> chararray[Any, dtype[str_]]: ...
|
|
|
|
def __array_finalize__(self, obj: object) -> None: ...
|
|
def __mul__(self, other: _ArrayLikeInt_co) -> chararray[Any, _CharDType]: ...
|
|
def __rmul__(self, other: _ArrayLikeInt_co) -> chararray[Any, _CharDType]: ...
|
|
def __mod__(self, i: Any) -> chararray[Any, _CharDType]: ...
|
|
|
|
@overload
|
|
def __eq__(
|
|
self: _CharArray[str_],
|
|
other: _ArrayLikeStr_co,
|
|
) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __eq__(
|
|
self: _CharArray[bytes_],
|
|
other: _ArrayLikeBytes_co,
|
|
) -> NDArray[bool_]: ...
|
|
|
|
@overload
|
|
def __ne__(
|
|
self: _CharArray[str_],
|
|
other: _ArrayLikeStr_co,
|
|
) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __ne__(
|
|
self: _CharArray[bytes_],
|
|
other: _ArrayLikeBytes_co,
|
|
) -> NDArray[bool_]: ...
|
|
|
|
@overload
|
|
def __ge__(
|
|
self: _CharArray[str_],
|
|
other: _ArrayLikeStr_co,
|
|
) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __ge__(
|
|
self: _CharArray[bytes_],
|
|
other: _ArrayLikeBytes_co,
|
|
) -> NDArray[bool_]: ...
|
|
|
|
@overload
|
|
def __le__(
|
|
self: _CharArray[str_],
|
|
other: _ArrayLikeStr_co,
|
|
) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __le__(
|
|
self: _CharArray[bytes_],
|
|
other: _ArrayLikeBytes_co,
|
|
) -> NDArray[bool_]: ...
|
|
|
|
@overload
|
|
def __gt__(
|
|
self: _CharArray[str_],
|
|
other: _ArrayLikeStr_co,
|
|
) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __gt__(
|
|
self: _CharArray[bytes_],
|
|
other: _ArrayLikeBytes_co,
|
|
) -> NDArray[bool_]: ...
|
|
|
|
@overload
|
|
def __lt__(
|
|
self: _CharArray[str_],
|
|
other: _ArrayLikeStr_co,
|
|
) -> NDArray[bool_]: ...
|
|
@overload
|
|
def __lt__(
|
|
self: _CharArray[bytes_],
|
|
other: _ArrayLikeBytes_co,
|
|
) -> NDArray[bool_]: ...
|
|
|
|
@overload
|
|
def __add__(
|
|
self: _CharArray[str_],
|
|
other: _ArrayLikeStr_co,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def __add__(
|
|
self: _CharArray[bytes_],
|
|
other: _ArrayLikeBytes_co,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def __radd__(
|
|
self: _CharArray[str_],
|
|
other: _ArrayLikeStr_co,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def __radd__(
|
|
self: _CharArray[bytes_],
|
|
other: _ArrayLikeBytes_co,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def center(
|
|
self: _CharArray[str_],
|
|
width: _ArrayLikeInt_co,
|
|
fillchar: _ArrayLikeStr_co = ...,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def center(
|
|
self: _CharArray[bytes_],
|
|
width: _ArrayLikeInt_co,
|
|
fillchar: _ArrayLikeBytes_co = ...,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def count(
|
|
self: _CharArray[str_],
|
|
sub: _ArrayLikeStr_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[int_]: ...
|
|
@overload
|
|
def count(
|
|
self: _CharArray[bytes_],
|
|
sub: _ArrayLikeBytes_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[int_]: ...
|
|
|
|
def decode(
|
|
self: _CharArray[bytes_],
|
|
encoding: None | str = ...,
|
|
errors: None | str = ...,
|
|
) -> _CharArray[str_]: ...
|
|
|
|
def encode(
|
|
self: _CharArray[str_],
|
|
encoding: None | str = ...,
|
|
errors: None | str = ...,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def endswith(
|
|
self: _CharArray[str_],
|
|
suffix: _ArrayLikeStr_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[bool_]: ...
|
|
@overload
|
|
def endswith(
|
|
self: _CharArray[bytes_],
|
|
suffix: _ArrayLikeBytes_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[bool_]: ...
|
|
|
|
def expandtabs(
|
|
self,
|
|
tabsize: _ArrayLikeInt_co = ...,
|
|
) -> chararray[Any, _CharDType]: ...
|
|
|
|
@overload
|
|
def find(
|
|
self: _CharArray[str_],
|
|
sub: _ArrayLikeStr_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[int_]: ...
|
|
@overload
|
|
def find(
|
|
self: _CharArray[bytes_],
|
|
sub: _ArrayLikeBytes_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[int_]: ...
|
|
|
|
@overload
|
|
def index(
|
|
self: _CharArray[str_],
|
|
sub: _ArrayLikeStr_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[int_]: ...
|
|
@overload
|
|
def index(
|
|
self: _CharArray[bytes_],
|
|
sub: _ArrayLikeBytes_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[int_]: ...
|
|
|
|
@overload
|
|
def join(
|
|
self: _CharArray[str_],
|
|
seq: _ArrayLikeStr_co,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def join(
|
|
self: _CharArray[bytes_],
|
|
seq: _ArrayLikeBytes_co,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def ljust(
|
|
self: _CharArray[str_],
|
|
width: _ArrayLikeInt_co,
|
|
fillchar: _ArrayLikeStr_co = ...,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def ljust(
|
|
self: _CharArray[bytes_],
|
|
width: _ArrayLikeInt_co,
|
|
fillchar: _ArrayLikeBytes_co = ...,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def lstrip(
|
|
self: _CharArray[str_],
|
|
chars: None | _ArrayLikeStr_co = ...,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def lstrip(
|
|
self: _CharArray[bytes_],
|
|
chars: None | _ArrayLikeBytes_co = ...,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def partition(
|
|
self: _CharArray[str_],
|
|
sep: _ArrayLikeStr_co,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def partition(
|
|
self: _CharArray[bytes_],
|
|
sep: _ArrayLikeBytes_co,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def replace(
|
|
self: _CharArray[str_],
|
|
old: _ArrayLikeStr_co,
|
|
new: _ArrayLikeStr_co,
|
|
count: None | _ArrayLikeInt_co = ...,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def replace(
|
|
self: _CharArray[bytes_],
|
|
old: _ArrayLikeBytes_co,
|
|
new: _ArrayLikeBytes_co,
|
|
count: None | _ArrayLikeInt_co = ...,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def rfind(
|
|
self: _CharArray[str_],
|
|
sub: _ArrayLikeStr_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[int_]: ...
|
|
@overload
|
|
def rfind(
|
|
self: _CharArray[bytes_],
|
|
sub: _ArrayLikeBytes_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[int_]: ...
|
|
|
|
@overload
|
|
def rindex(
|
|
self: _CharArray[str_],
|
|
sub: _ArrayLikeStr_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[int_]: ...
|
|
@overload
|
|
def rindex(
|
|
self: _CharArray[bytes_],
|
|
sub: _ArrayLikeBytes_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[int_]: ...
|
|
|
|
@overload
|
|
def rjust(
|
|
self: _CharArray[str_],
|
|
width: _ArrayLikeInt_co,
|
|
fillchar: _ArrayLikeStr_co = ...,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def rjust(
|
|
self: _CharArray[bytes_],
|
|
width: _ArrayLikeInt_co,
|
|
fillchar: _ArrayLikeBytes_co = ...,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def rpartition(
|
|
self: _CharArray[str_],
|
|
sep: _ArrayLikeStr_co,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def rpartition(
|
|
self: _CharArray[bytes_],
|
|
sep: _ArrayLikeBytes_co,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def rsplit(
|
|
self: _CharArray[str_],
|
|
sep: None | _ArrayLikeStr_co = ...,
|
|
maxsplit: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[object_]: ...
|
|
@overload
|
|
def rsplit(
|
|
self: _CharArray[bytes_],
|
|
sep: None | _ArrayLikeBytes_co = ...,
|
|
maxsplit: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def rstrip(
|
|
self: _CharArray[str_],
|
|
chars: None | _ArrayLikeStr_co = ...,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def rstrip(
|
|
self: _CharArray[bytes_],
|
|
chars: None | _ArrayLikeBytes_co = ...,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def split(
|
|
self: _CharArray[str_],
|
|
sep: None | _ArrayLikeStr_co = ...,
|
|
maxsplit: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[object_]: ...
|
|
@overload
|
|
def split(
|
|
self: _CharArray[bytes_],
|
|
sep: None | _ArrayLikeBytes_co = ...,
|
|
maxsplit: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[object_]: ...
|
|
|
|
def splitlines(self, keepends: None | _ArrayLikeBool_co = ...) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def startswith(
|
|
self: _CharArray[str_],
|
|
prefix: _ArrayLikeStr_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[bool_]: ...
|
|
@overload
|
|
def startswith(
|
|
self: _CharArray[bytes_],
|
|
prefix: _ArrayLikeBytes_co,
|
|
start: _ArrayLikeInt_co = ...,
|
|
end: None | _ArrayLikeInt_co = ...,
|
|
) -> NDArray[bool_]: ...
|
|
|
|
@overload
|
|
def strip(
|
|
self: _CharArray[str_],
|
|
chars: None | _ArrayLikeStr_co = ...,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def strip(
|
|
self: _CharArray[bytes_],
|
|
chars: None | _ArrayLikeBytes_co = ...,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
@overload
|
|
def translate(
|
|
self: _CharArray[str_],
|
|
table: _ArrayLikeStr_co,
|
|
deletechars: None | _ArrayLikeStr_co = ...,
|
|
) -> _CharArray[str_]: ...
|
|
@overload
|
|
def translate(
|
|
self: _CharArray[bytes_],
|
|
table: _ArrayLikeBytes_co,
|
|
deletechars: None | _ArrayLikeBytes_co = ...,
|
|
) -> _CharArray[bytes_]: ...
|
|
|
|
def zfill(self, width: _ArrayLikeInt_co) -> chararray[Any, _CharDType]: ...
|
|
def capitalize(self) -> chararray[_ShapeType, _CharDType]: ...
|
|
def title(self) -> chararray[_ShapeType, _CharDType]: ...
|
|
def swapcase(self) -> chararray[_ShapeType, _CharDType]: ...
|
|
def lower(self) -> chararray[_ShapeType, _CharDType]: ...
|
|
def upper(self) -> chararray[_ShapeType, _CharDType]: ...
|
|
def isalnum(self) -> ndarray[_ShapeType, dtype[bool_]]: ...
|
|
def isalpha(self) -> ndarray[_ShapeType, dtype[bool_]]: ...
|
|
def isdigit(self) -> ndarray[_ShapeType, dtype[bool_]]: ...
|
|
def islower(self) -> ndarray[_ShapeType, dtype[bool_]]: ...
|
|
def isspace(self) -> ndarray[_ShapeType, dtype[bool_]]: ...
|
|
def istitle(self) -> ndarray[_ShapeType, dtype[bool_]]: ...
|
|
def isupper(self) -> ndarray[_ShapeType, dtype[bool_]]: ...
|
|
def isnumeric(self) -> ndarray[_ShapeType, dtype[bool_]]: ...
|
|
def isdecimal(self) -> ndarray[_ShapeType, dtype[bool_]]: ...
|
|
|
|
# NOTE: Deprecated
|
|
# class MachAr: ...
|
|
|
|
class _SupportsDLPack(Protocol[_T_contra]):
|
|
def __dlpack__(self, *, stream: None | _T_contra = ...) -> _PyCapsule: ...
|
|
|
|
def from_dlpack(obj: _SupportsDLPack[None], /) -> NDArray[Any]: ...
|