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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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