Your IP : 216.73.216.74
from collections.abc import Callable, Sequence
from typing import (
Any,
Final,
Never,
NoReturn,
SupportsAbs,
SupportsIndex,
TypeAlias,
TypeGuard,
TypeVar,
Unpack,
overload,
)
from typing import Literal as L
import numpy as np
from numpy import (
False_,
True_,
_OrderCF,
_OrderKACF,
# re-exports
bitwise_not,
broadcast,
complexfloating,
dtype,
flatiter,
float64,
floating,
from_dlpack,
# other
generic,
inf,
int_,
intp,
little_endian,
matmul,
nan,
ndarray,
nditer,
newaxis,
object_,
signedinteger,
timedelta64,
ufunc,
unsignedinteger,
vecdot,
)
from numpy._typing import (
ArrayLike,
DTypeLike,
NDArray,
_ArrayLike,
_ArrayLikeBool_co,
_ArrayLikeComplex_co,
_ArrayLikeFloat_co,
_ArrayLikeInt_co,
_ArrayLikeObject_co,
_ArrayLikeTD64_co,
_ArrayLikeUInt_co,
_DTypeLike,
_NestedSequence,
_ScalarLike_co,
_Shape,
_ShapeLike,
_SupportsArrayFunc,
_SupportsDType,
)
from .fromnumeric import all as all
from .fromnumeric import any as any
from .fromnumeric import argpartition as argpartition
from .fromnumeric import matrix_transpose as matrix_transpose
from .fromnumeric import mean as mean
from .multiarray import (
# other
_Array,
_ConstructorEmpty,
_KwargsEmpty,
# re-exports
arange,
array,
asanyarray,
asarray,
ascontiguousarray,
asfortranarray,
can_cast,
concatenate,
copyto,
dot,
empty,
empty_like,
frombuffer,
fromfile,
fromiter,
fromstring,
inner,
lexsort,
may_share_memory,
min_scalar_type,
nested_iters,
promote_types,
putmask,
result_type,
shares_memory,
vdot,
where,
zeros,
)
__all__ = [
"newaxis",
"ndarray",
"flatiter",
"nditer",
"nested_iters",
"ufunc",
"arange",
"array",
"asarray",
"asanyarray",
"ascontiguousarray",
"asfortranarray",
"zeros",
"count_nonzero",
"empty",
"broadcast",
"dtype",
"fromstring",
"fromfile",
"frombuffer",
"from_dlpack",
"where",
"argwhere",
"copyto",
"concatenate",
"lexsort",
"astype",
"can_cast",
"promote_types",
"min_scalar_type",
"result_type",
"isfortran",
"empty_like",
"zeros_like",
"ones_like",
"correlate",
"convolve",
"inner",
"dot",
"outer",
"vdot",
"roll",
"rollaxis",
"moveaxis",
"cross",
"tensordot",
"little_endian",
"fromiter",
"array_equal",
"array_equiv",
"indices",
"fromfunction",
"isclose",
"isscalar",
"binary_repr",
"base_repr",
"ones",
"identity",
"allclose",
"putmask",
"flatnonzero",
"inf",
"nan",
"False_",
"True_",
"bitwise_not",
"full",
"full_like",
"matmul",
"vecdot",
"shares_memory",
"may_share_memory",
]
_T = TypeVar("_T")
_ScalarT = TypeVar("_ScalarT", bound=generic)
_DTypeT = TypeVar("_DTypeT", bound=np.dtype)
_ArrayT = TypeVar("_ArrayT", bound=np.ndarray[Any, Any])
_ShapeT = TypeVar("_ShapeT", bound=_Shape)
_AnyShapeT = TypeVar(
"_AnyShapeT",
tuple[()],
tuple[int],
tuple[int, int],
tuple[int, int, int],
tuple[int, int, int, int],
tuple[int, ...],
)
_CorrelateMode: TypeAlias = L["valid", "same", "full"]
@overload
def zeros_like(
a: _ArrayT,
dtype: None = ...,
order: _OrderKACF = ...,
subok: L[True] = ...,
shape: None = ...,
*,
device: L["cpu"] | None = ...,
) -> _ArrayT: ...
@overload
def zeros_like(
a: _ArrayLike[_ScalarT],
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: _ShapeLike | None = ...,
*,
device: L["cpu"] | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def zeros_like(
a: Any,
dtype: _DTypeLike[_ScalarT],
order: _OrderKACF = ...,
subok: bool = ...,
shape: _ShapeLike | None = ...,
*,
device: L["cpu"] | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def zeros_like(
a: Any,
dtype: DTypeLike | None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: _ShapeLike | None = ...,
*,
device: L["cpu"] | None = ...,
) -> NDArray[Any]: ...
ones: Final[_ConstructorEmpty]
@overload
def ones_like(
a: _ArrayT,
dtype: None = ...,
order: _OrderKACF = ...,
subok: L[True] = ...,
shape: None = ...,
*,
device: L["cpu"] | None = ...,
) -> _ArrayT: ...
@overload
def ones_like(
a: _ArrayLike[_ScalarT],
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: _ShapeLike | None = ...,
*,
device: L["cpu"] | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def ones_like(
a: Any,
dtype: _DTypeLike[_ScalarT],
order: _OrderKACF = ...,
subok: bool = ...,
shape: _ShapeLike | None = ...,
*,
device: L["cpu"] | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def ones_like(
a: Any,
dtype: DTypeLike | None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: _ShapeLike | None = ...,
*,
device: L["cpu"] | None = ...,
) -> NDArray[Any]: ...
# TODO: Add overloads for bool, int, float, complex, str, bytes, and memoryview
# 1-D shape
@overload
def full(
shape: SupportsIndex,
fill_value: _ScalarT,
dtype: None = ...,
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> _Array[tuple[int], _ScalarT]: ...
@overload
def full(
shape: SupportsIndex,
fill_value: Any,
dtype: _DTypeT | _SupportsDType[_DTypeT],
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> np.ndarray[tuple[int], _DTypeT]: ...
@overload
def full(
shape: SupportsIndex,
fill_value: Any,
dtype: type[_ScalarT],
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> _Array[tuple[int], _ScalarT]: ...
@overload
def full(
shape: SupportsIndex,
fill_value: Any,
dtype: DTypeLike | None = ...,
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> _Array[tuple[int], Any]: ...
# known shape
@overload
def full(
shape: _AnyShapeT,
fill_value: _ScalarT,
dtype: None = ...,
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> _Array[_AnyShapeT, _ScalarT]: ...
@overload
def full(
shape: _AnyShapeT,
fill_value: Any,
dtype: _DTypeT | _SupportsDType[_DTypeT],
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> np.ndarray[_AnyShapeT, _DTypeT]: ...
@overload
def full(
shape: _AnyShapeT,
fill_value: Any,
dtype: type[_ScalarT],
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> _Array[_AnyShapeT, _ScalarT]: ...
@overload
def full(
shape: _AnyShapeT,
fill_value: Any,
dtype: DTypeLike | None = ...,
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> _Array[_AnyShapeT, Any]: ...
# unknown shape
@overload
def full(
shape: _ShapeLike,
fill_value: _ScalarT,
dtype: None = ...,
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> NDArray[_ScalarT]: ...
@overload
def full(
shape: _ShapeLike,
fill_value: Any,
dtype: _DTypeT | _SupportsDType[_DTypeT],
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> np.ndarray[Any, _DTypeT]: ...
@overload
def full(
shape: _ShapeLike,
fill_value: Any,
dtype: type[_ScalarT],
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> NDArray[_ScalarT]: ...
@overload
def full(
shape: _ShapeLike,
fill_value: Any,
dtype: DTypeLike | None = ...,
order: _OrderCF = ...,
**kwargs: Unpack[_KwargsEmpty],
) -> NDArray[Any]: ...
@overload
def full_like(
a: _ArrayT,
fill_value: Any,
dtype: None = ...,
order: _OrderKACF = ...,
subok: L[True] = ...,
shape: None = ...,
*,
device: L["cpu"] | None = ...,
) -> _ArrayT: ...
@overload
def full_like(
a: _ArrayLike[_ScalarT],
fill_value: Any,
dtype: None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: _ShapeLike | None = ...,
*,
device: L["cpu"] | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def full_like(
a: Any,
fill_value: Any,
dtype: _DTypeLike[_ScalarT],
order: _OrderKACF = ...,
subok: bool = ...,
shape: _ShapeLike | None = ...,
*,
device: L["cpu"] | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def full_like(
a: Any,
fill_value: Any,
dtype: DTypeLike | None = ...,
order: _OrderKACF = ...,
subok: bool = ...,
shape: _ShapeLike | None = ...,
*,
device: L["cpu"] | None = ...,
) -> NDArray[Any]: ...
#
@overload
def count_nonzero(a: ArrayLike, axis: None = None, *, keepdims: L[False] = False) -> np.intp: ...
@overload
def count_nonzero(a: _ScalarLike_co, axis: _ShapeLike | None = None, *, keepdims: L[True]) -> np.intp: ...
@overload
def count_nonzero(
a: NDArray[Any] | _NestedSequence[ArrayLike], axis: _ShapeLike | None = None, *, keepdims: L[True]
) -> NDArray[np.intp]: ...
@overload
def count_nonzero(a: ArrayLike, axis: _ShapeLike | None = None, *, keepdims: bool = False) -> Any: ...
#
def isfortran(a: NDArray[Any] | generic) -> bool: ...
def argwhere(a: ArrayLike) -> NDArray[intp]: ...
def flatnonzero(a: ArrayLike) -> NDArray[intp]: ...
@overload
def correlate(
a: _ArrayLike[Never],
v: _ArrayLike[Never],
mode: _CorrelateMode = ...,
) -> NDArray[Any]: ...
@overload
def correlate(
a: _ArrayLikeBool_co,
v: _ArrayLikeBool_co,
mode: _CorrelateMode = ...,
) -> NDArray[np.bool]: ...
@overload
def correlate(
a: _ArrayLikeUInt_co,
v: _ArrayLikeUInt_co,
mode: _CorrelateMode = ...,
) -> NDArray[unsignedinteger]: ...
@overload
def correlate(
a: _ArrayLikeInt_co,
v: _ArrayLikeInt_co,
mode: _CorrelateMode = ...,
) -> NDArray[signedinteger]: ...
@overload
def correlate(
a: _ArrayLikeFloat_co,
v: _ArrayLikeFloat_co,
mode: _CorrelateMode = ...,
) -> NDArray[floating]: ...
@overload
def correlate(
a: _ArrayLikeComplex_co,
v: _ArrayLikeComplex_co,
mode: _CorrelateMode = ...,
) -> NDArray[complexfloating]: ...
@overload
def correlate(
a: _ArrayLikeTD64_co,
v: _ArrayLikeTD64_co,
mode: _CorrelateMode = ...,
) -> NDArray[timedelta64]: ...
@overload
def correlate(
a: _ArrayLikeObject_co,
v: _ArrayLikeObject_co,
mode: _CorrelateMode = ...,
) -> NDArray[object_]: ...
@overload
def convolve(
a: _ArrayLike[Never],
v: _ArrayLike[Never],
mode: _CorrelateMode = ...,
) -> NDArray[Any]: ...
@overload
def convolve(
a: _ArrayLikeBool_co,
v: _ArrayLikeBool_co,
mode: _CorrelateMode = ...,
) -> NDArray[np.bool]: ...
@overload
def convolve(
a: _ArrayLikeUInt_co,
v: _ArrayLikeUInt_co,
mode: _CorrelateMode = ...,
) -> NDArray[unsignedinteger]: ...
@overload
def convolve(
a: _ArrayLikeInt_co,
v: _ArrayLikeInt_co,
mode: _CorrelateMode = ...,
) -> NDArray[signedinteger]: ...
@overload
def convolve(
a: _ArrayLikeFloat_co,
v: _ArrayLikeFloat_co,
mode: _CorrelateMode = ...,
) -> NDArray[floating]: ...
@overload
def convolve(
a: _ArrayLikeComplex_co,
v: _ArrayLikeComplex_co,
mode: _CorrelateMode = ...,
) -> NDArray[complexfloating]: ...
@overload
def convolve(
a: _ArrayLikeTD64_co,
v: _ArrayLikeTD64_co,
mode: _CorrelateMode = ...,
) -> NDArray[timedelta64]: ...
@overload
def convolve(
a: _ArrayLikeObject_co,
v: _ArrayLikeObject_co,
mode: _CorrelateMode = ...,
) -> NDArray[object_]: ...
@overload
def outer(
a: _ArrayLike[Never],
b: _ArrayLike[Never],
out: None = ...,
) -> NDArray[Any]: ...
@overload
def outer(
a: _ArrayLikeBool_co,
b: _ArrayLikeBool_co,
out: None = ...,
) -> NDArray[np.bool]: ...
@overload
def outer(
a: _ArrayLikeUInt_co,
b: _ArrayLikeUInt_co,
out: None = ...,
) -> NDArray[unsignedinteger]: ...
@overload
def outer(
a: _ArrayLikeInt_co,
b: _ArrayLikeInt_co,
out: None = ...,
) -> NDArray[signedinteger]: ...
@overload
def outer(
a: _ArrayLikeFloat_co,
b: _ArrayLikeFloat_co,
out: None = ...,
) -> NDArray[floating]: ...
@overload
def outer(
a: _ArrayLikeComplex_co,
b: _ArrayLikeComplex_co,
out: None = ...,
) -> NDArray[complexfloating]: ...
@overload
def outer(
a: _ArrayLikeTD64_co,
b: _ArrayLikeTD64_co,
out: None = ...,
) -> NDArray[timedelta64]: ...
@overload
def outer(
a: _ArrayLikeObject_co,
b: _ArrayLikeObject_co,
out: None = ...,
) -> NDArray[object_]: ...
@overload
def outer(
a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
b: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
out: _ArrayT,
) -> _ArrayT: ...
@overload
def tensordot(
a: _ArrayLike[Never],
b: _ArrayLike[Never],
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[Any]: ...
@overload
def tensordot(
a: _ArrayLikeBool_co,
b: _ArrayLikeBool_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[np.bool]: ...
@overload
def tensordot(
a: _ArrayLikeUInt_co,
b: _ArrayLikeUInt_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[unsignedinteger]: ...
@overload
def tensordot(
a: _ArrayLikeInt_co,
b: _ArrayLikeInt_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[signedinteger]: ...
@overload
def tensordot(
a: _ArrayLikeFloat_co,
b: _ArrayLikeFloat_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[floating]: ...
@overload
def tensordot(
a: _ArrayLikeComplex_co,
b: _ArrayLikeComplex_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[complexfloating]: ...
@overload
def tensordot(
a: _ArrayLikeTD64_co,
b: _ArrayLikeTD64_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[timedelta64]: ...
@overload
def tensordot(
a: _ArrayLikeObject_co,
b: _ArrayLikeObject_co,
axes: int | tuple[_ShapeLike, _ShapeLike] = ...,
) -> NDArray[object_]: ...
@overload
def roll(
a: _ArrayLike[_ScalarT],
shift: _ShapeLike,
axis: _ShapeLike | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def roll(
a: ArrayLike,
shift: _ShapeLike,
axis: _ShapeLike | None = ...,
) -> NDArray[Any]: ...
def rollaxis(
a: NDArray[_ScalarT],
axis: int,
start: int = ...,
) -> NDArray[_ScalarT]: ...
def moveaxis(
a: NDArray[_ScalarT],
source: _ShapeLike,
destination: _ShapeLike,
) -> NDArray[_ScalarT]: ...
@overload
def cross(
a: _ArrayLike[Never],
b: _ArrayLike[Never],
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: int | None = ...,
) -> NDArray[Any]: ...
@overload
def cross(
a: _ArrayLikeBool_co,
b: _ArrayLikeBool_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: int | None = ...,
) -> NoReturn: ...
@overload
def cross(
a: _ArrayLikeUInt_co,
b: _ArrayLikeUInt_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: int | None = ...,
) -> NDArray[unsignedinteger]: ...
@overload
def cross(
a: _ArrayLikeInt_co,
b: _ArrayLikeInt_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: int | None = ...,
) -> NDArray[signedinteger]: ...
@overload
def cross(
a: _ArrayLikeFloat_co,
b: _ArrayLikeFloat_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: int | None = ...,
) -> NDArray[floating]: ...
@overload
def cross(
a: _ArrayLikeComplex_co,
b: _ArrayLikeComplex_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: int | None = ...,
) -> NDArray[complexfloating]: ...
@overload
def cross(
a: _ArrayLikeObject_co,
b: _ArrayLikeObject_co,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: int | None = ...,
) -> NDArray[object_]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: type[int] = ...,
sparse: L[False] = ...,
) -> NDArray[int_]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: type[int],
sparse: L[True],
) -> tuple[NDArray[int_], ...]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: type[int] = ...,
*,
sparse: L[True],
) -> tuple[NDArray[int_], ...]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: _DTypeLike[_ScalarT],
sparse: L[False] = ...,
) -> NDArray[_ScalarT]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: _DTypeLike[_ScalarT],
sparse: L[True],
) -> tuple[NDArray[_ScalarT], ...]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: DTypeLike = ...,
sparse: L[False] = ...,
) -> NDArray[Any]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: DTypeLike,
sparse: L[True],
) -> tuple[NDArray[Any], ...]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: DTypeLike = ...,
*,
sparse: L[True],
) -> tuple[NDArray[Any], ...]: ...
def fromfunction(
function: Callable[..., _T],
shape: Sequence[int],
*,
dtype: DTypeLike = ...,
like: _SupportsArrayFunc | None = ...,
**kwargs: Any,
) -> _T: ...
def isscalar(element: object) -> TypeGuard[generic | complex | str | bytes | memoryview]: ...
def binary_repr(num: SupportsIndex, width: int | None = ...) -> str: ...
def base_repr(
number: SupportsAbs[float],
base: float = ...,
padding: SupportsIndex | None = ...,
) -> str: ...
@overload
def identity(
n: int,
dtype: None = ...,
*,
like: _SupportsArrayFunc | None = ...,
) -> NDArray[float64]: ...
@overload
def identity(
n: int,
dtype: _DTypeLike[_ScalarT],
*,
like: _SupportsArrayFunc | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def identity(
n: int,
dtype: DTypeLike | None = ...,
*,
like: _SupportsArrayFunc | None = ...,
) -> NDArray[Any]: ...
def allclose(
a: ArrayLike,
b: ArrayLike,
rtol: ArrayLike = ...,
atol: ArrayLike = ...,
equal_nan: bool = ...,
) -> bool: ...
@overload
def isclose(
a: _ScalarLike_co,
b: _ScalarLike_co,
rtol: ArrayLike = ...,
atol: ArrayLike = ...,
equal_nan: bool = ...,
) -> np.bool: ...
@overload
def isclose(
a: ArrayLike,
b: ArrayLike,
rtol: ArrayLike = ...,
atol: ArrayLike = ...,
equal_nan: bool = ...,
) -> NDArray[np.bool]: ...
def array_equal(a1: ArrayLike, a2: ArrayLike, equal_nan: bool = ...) -> bool: ...
def array_equiv(a1: ArrayLike, a2: ArrayLike) -> bool: ...
@overload
def astype(
x: ndarray[_ShapeT, dtype],
dtype: _DTypeLike[_ScalarT],
/,
*,
copy: bool = ...,
device: L["cpu"] | None = ...,
) -> ndarray[_ShapeT, dtype[_ScalarT]]: ...
@overload
def astype(
x: ndarray[_ShapeT, dtype],
dtype: DTypeLike,
/,
*,
copy: bool = ...,
device: L["cpu"] | None = ...,
) -> ndarray[_ShapeT, dtype]: ...