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Code Editor : fromnumeric.pyi
import sys import datetime as dt from typing import Optional, Union, Sequence, Tuple, Any, overload, TypeVar from numpy import ( ndarray, number, integer, intp, bool_, generic, _OrderKACF, _OrderACF, _ModeKind, _PartitionKind, _SortKind, _SortSide, ) from numpy.typing import ( DTypeLike, ArrayLike, _ShapeLike, _Shape, _ArrayLikeBool_co, _ArrayLikeInt_co, _NumberLike_co, ) if sys.version_info >= (3, 8): from typing import Literal else: from typing_extensions import Literal # Various annotations for scalars # While dt.datetime and dt.timedelta are not technically part of NumPy, # they are one of the rare few builtin scalars which serve as valid return types. # See https://github.com/numpy/numpy-stubs/pull/67#discussion_r412604113. _ScalarNumpy = Union[generic, dt.datetime, dt.timedelta] _ScalarBuiltin = Union[str, bytes, dt.date, dt.timedelta, bool, int, float, complex] _Scalar = Union[_ScalarBuiltin, _ScalarNumpy] # Integers and booleans can generally be used interchangeably _ScalarGeneric = TypeVar("_ScalarGeneric", bound=generic) _Number = TypeVar("_Number", bound=number) # The signature of take() follows a common theme with its overloads: # 1. A generic comes in; the same generic comes out # 2. A scalar comes in; a generic comes out # 3. An array-like object comes in; some keyword ensures that a generic comes out # 4. An array-like object comes in; an ndarray or generic comes out def take( a: ArrayLike, indices: _ArrayLikeInt_co, axis: Optional[int] = ..., out: Optional[ndarray] = ..., mode: _ModeKind = ..., ) -> Any: ... def reshape( a: ArrayLike, newshape: _ShapeLike, order: _OrderACF = ..., ) -> ndarray: ... def choose( a: _ArrayLikeInt_co, choices: ArrayLike, out: Optional[ndarray] = ..., mode: _ModeKind = ..., ) -> Any: ... def repeat( a: ArrayLike, repeats: _ArrayLikeInt_co, axis: Optional[int] = ..., ) -> ndarray: ... def put( a: ndarray, ind: _ArrayLikeInt_co, v: ArrayLike, mode: _ModeKind = ..., ) -> None: ... def swapaxes( a: ArrayLike, axis1: int, axis2: int, ) -> ndarray: ... def transpose( a: ArrayLike, axes: Union[None, Sequence[int], ndarray] = ... ) -> ndarray: ... def partition( a: ArrayLike, kth: _ArrayLikeInt_co, axis: Optional[int] = ..., kind: _PartitionKind = ..., order: Union[None, str, Sequence[str]] = ..., ) -> ndarray: ... def argpartition( a: ArrayLike, kth: _ArrayLikeInt_co, axis: Optional[int] = ..., kind: _PartitionKind = ..., order: Union[None, str, Sequence[str]] = ..., ) -> Any: ... def sort( a: ArrayLike, axis: Optional[int] = ..., kind: Optional[_SortKind] = ..., order: Union[None, str, Sequence[str]] = ..., ) -> ndarray: ... def argsort( a: ArrayLike, axis: Optional[int] = ..., kind: Optional[_SortKind] = ..., order: Union[None, str, Sequence[str]] = ..., ) -> ndarray: ... @overload def argmax( a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ..., ) -> intp: ... @overload def argmax( a: ArrayLike, axis: Optional[int] = ..., out: Optional[ndarray] = ..., ) -> Any: ... @overload def argmin( a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ..., ) -> intp: ... @overload def argmin( a: ArrayLike, axis: Optional[int] = ..., out: Optional[ndarray] = ..., ) -> Any: ... @overload def searchsorted( a: ArrayLike, v: _Scalar, side: _SortSide = ..., sorter: Optional[_ArrayLikeInt_co] = ..., # 1D int array ) -> intp: ... @overload def searchsorted( a: ArrayLike, v: ArrayLike, side: _SortSide = ..., sorter: Optional[_ArrayLikeInt_co] = ..., # 1D int array ) -> ndarray: ... def resize( a: ArrayLike, new_shape: _ShapeLike, ) -> ndarray: ... @overload def squeeze( a: _ScalarGeneric, axis: Optional[_ShapeLike] = ..., ) -> _ScalarGeneric: ... @overload def squeeze( a: ArrayLike, axis: Optional[_ShapeLike] = ..., ) -> ndarray: ... def diagonal( a: ArrayLike, offset: int = ..., axis1: int = ..., axis2: int = ..., # >= 2D array ) -> ndarray: ... def trace( a: ArrayLike, # >= 2D array offset: int = ..., axis1: int = ..., axis2: int = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., ) -> Any: ... def ravel(a: ArrayLike, order: _OrderKACF = ...) -> ndarray: ... def nonzero(a: ArrayLike) -> Tuple[ndarray, ...]: ... def shape(a: ArrayLike) -> _Shape: ... def compress( condition: ArrayLike, # 1D bool array a: ArrayLike, axis: Optional[int] = ..., out: Optional[ndarray] = ..., ) -> ndarray: ... @overload def clip( a: ArrayLike, a_min: ArrayLike, a_max: Optional[ArrayLike], out: Optional[ndarray] = ..., **kwargs: Any, ) -> Any: ... @overload def clip( a: ArrayLike, a_min: None, a_max: ArrayLike, out: Optional[ndarray] = ..., **kwargs: Any, ) -> Any: ... def sum( a: ArrayLike, axis: _ShapeLike = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool_co = ..., ) -> Any: ... @overload def all( a: ArrayLike, axis: None = ..., out: None = ..., keepdims: Literal[False] = ..., ) -> bool_: ... @overload def all( a: ArrayLike, axis: Optional[_ShapeLike] = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., ) -> Any: ... @overload def any( a: ArrayLike, axis: None = ..., out: None = ..., keepdims: Literal[False] = ..., ) -> bool_: ... @overload def any( a: ArrayLike, axis: Optional[_ShapeLike] = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., ) -> Any: ... def cumsum( a: ArrayLike, axis: Optional[int] = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., ) -> ndarray: ... def ptp( a: ArrayLike, axis: Optional[_ShapeLike] = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., ) -> Any: ... def amax( a: ArrayLike, axis: Optional[_ShapeLike] = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool_co = ..., ) -> Any: ... def amin( a: ArrayLike, axis: Optional[_ShapeLike] = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool_co = ..., ) -> Any: ... # TODO: `np.prod()``: For object arrays `initial` does not necessarily # have to be a numerical scalar. # The only requirement is that it is compatible # with the `.__mul__()` method(s) of the passed array's elements. # Note that the same situation holds for all wrappers around # `np.ufunc.reduce`, e.g. `np.sum()` (`.__add__()`). def prod( a: ArrayLike, axis: Optional[_ShapeLike] = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool_co = ..., ) -> Any: ... def cumprod( a: ArrayLike, axis: Optional[int] = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., ) -> ndarray: ... def ndim(a: ArrayLike) -> int: ... def size(a: ArrayLike, axis: Optional[int] = ...) -> int: ... def around( a: ArrayLike, decimals: int = ..., out: Optional[ndarray] = ..., ) -> Any: ... def mean( a: ArrayLike, axis: Optional[_ShapeLike] = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., ) -> Any: ... def std( a: ArrayLike, axis: Optional[_ShapeLike] = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., ddof: int = ..., keepdims: bool = ..., ) -> Any: ... def var( a: ArrayLike, axis: Optional[_ShapeLike] = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., ddof: int = ..., keepdims: bool = ..., ) -> Any: ...
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