You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
239 lines
5.2 KiB
239 lines
5.2 KiB
from collections.abc import Callable, Sequence
|
|
from typing import (
|
|
Any,
|
|
overload,
|
|
TypeVar,
|
|
Union,
|
|
)
|
|
|
|
from numpy import (
|
|
generic,
|
|
number,
|
|
bool_,
|
|
timedelta64,
|
|
datetime64,
|
|
int_,
|
|
intp,
|
|
float64,
|
|
signedinteger,
|
|
floating,
|
|
complexfloating,
|
|
object_,
|
|
_OrderCF,
|
|
)
|
|
|
|
from numpy._typing import (
|
|
DTypeLike,
|
|
_DTypeLike,
|
|
ArrayLike,
|
|
_ArrayLike,
|
|
NDArray,
|
|
_SupportsArrayFunc,
|
|
_ArrayLikeInt_co,
|
|
_ArrayLikeFloat_co,
|
|
_ArrayLikeComplex_co,
|
|
_ArrayLikeObject_co,
|
|
)
|
|
|
|
_T = TypeVar("_T")
|
|
_SCT = TypeVar("_SCT", bound=generic)
|
|
|
|
# The returned arrays dtype must be compatible with `np.equal`
|
|
_MaskFunc = Callable[
|
|
[NDArray[int_], _T],
|
|
NDArray[Union[number[Any], bool_, timedelta64, datetime64, object_]],
|
|
]
|
|
|
|
__all__: list[str]
|
|
|
|
@overload
|
|
def fliplr(m: _ArrayLike[_SCT]) -> NDArray[_SCT]: ...
|
|
@overload
|
|
def fliplr(m: ArrayLike) -> NDArray[Any]: ...
|
|
|
|
@overload
|
|
def flipud(m: _ArrayLike[_SCT]) -> NDArray[_SCT]: ...
|
|
@overload
|
|
def flipud(m: ArrayLike) -> NDArray[Any]: ...
|
|
|
|
@overload
|
|
def eye(
|
|
N: int,
|
|
M: None | int = ...,
|
|
k: int = ...,
|
|
dtype: None = ...,
|
|
order: _OrderCF = ...,
|
|
*,
|
|
like: None | _SupportsArrayFunc = ...,
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def eye(
|
|
N: int,
|
|
M: None | int = ...,
|
|
k: int = ...,
|
|
dtype: _DTypeLike[_SCT] = ...,
|
|
order: _OrderCF = ...,
|
|
*,
|
|
like: None | _SupportsArrayFunc = ...,
|
|
) -> NDArray[_SCT]: ...
|
|
@overload
|
|
def eye(
|
|
N: int,
|
|
M: None | int = ...,
|
|
k: int = ...,
|
|
dtype: DTypeLike = ...,
|
|
order: _OrderCF = ...,
|
|
*,
|
|
like: None | _SupportsArrayFunc = ...,
|
|
) -> NDArray[Any]: ...
|
|
|
|
@overload
|
|
def diag(v: _ArrayLike[_SCT], k: int = ...) -> NDArray[_SCT]: ...
|
|
@overload
|
|
def diag(v: ArrayLike, k: int = ...) -> NDArray[Any]: ...
|
|
|
|
@overload
|
|
def diagflat(v: _ArrayLike[_SCT], k: int = ...) -> NDArray[_SCT]: ...
|
|
@overload
|
|
def diagflat(v: ArrayLike, k: int = ...) -> NDArray[Any]: ...
|
|
|
|
@overload
|
|
def tri(
|
|
N: int,
|
|
M: None | int = ...,
|
|
k: int = ...,
|
|
dtype: None = ...,
|
|
*,
|
|
like: None | _SupportsArrayFunc = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def tri(
|
|
N: int,
|
|
M: None | int = ...,
|
|
k: int = ...,
|
|
dtype: _DTypeLike[_SCT] = ...,
|
|
*,
|
|
like: None | _SupportsArrayFunc = ...
|
|
) -> NDArray[_SCT]: ...
|
|
@overload
|
|
def tri(
|
|
N: int,
|
|
M: None | int = ...,
|
|
k: int = ...,
|
|
dtype: DTypeLike = ...,
|
|
*,
|
|
like: None | _SupportsArrayFunc = ...
|
|
) -> NDArray[Any]: ...
|
|
|
|
@overload
|
|
def tril(v: _ArrayLike[_SCT], k: int = ...) -> NDArray[_SCT]: ...
|
|
@overload
|
|
def tril(v: ArrayLike, k: int = ...) -> NDArray[Any]: ...
|
|
|
|
@overload
|
|
def triu(v: _ArrayLike[_SCT], k: int = ...) -> NDArray[_SCT]: ...
|
|
@overload
|
|
def triu(v: ArrayLike, k: int = ...) -> NDArray[Any]: ...
|
|
|
|
@overload
|
|
def vander( # type: ignore[misc]
|
|
x: _ArrayLikeInt_co,
|
|
N: None | int = ...,
|
|
increasing: bool = ...,
|
|
) -> NDArray[signedinteger[Any]]: ...
|
|
@overload
|
|
def vander( # type: ignore[misc]
|
|
x: _ArrayLikeFloat_co,
|
|
N: None | int = ...,
|
|
increasing: bool = ...,
|
|
) -> NDArray[floating[Any]]: ...
|
|
@overload
|
|
def vander(
|
|
x: _ArrayLikeComplex_co,
|
|
N: None | int = ...,
|
|
increasing: bool = ...,
|
|
) -> NDArray[complexfloating[Any, Any]]: ...
|
|
@overload
|
|
def vander(
|
|
x: _ArrayLikeObject_co,
|
|
N: None | int = ...,
|
|
increasing: bool = ...,
|
|
) -> NDArray[object_]: ...
|
|
|
|
@overload
|
|
def histogram2d( # type: ignore[misc]
|
|
x: _ArrayLikeFloat_co,
|
|
y: _ArrayLikeFloat_co,
|
|
bins: int | Sequence[int] = ...,
|
|
range: None | _ArrayLikeFloat_co = ...,
|
|
density: None | bool = ...,
|
|
weights: None | _ArrayLikeFloat_co = ...,
|
|
) -> tuple[
|
|
NDArray[float64],
|
|
NDArray[floating[Any]],
|
|
NDArray[floating[Any]],
|
|
]: ...
|
|
@overload
|
|
def histogram2d(
|
|
x: _ArrayLikeComplex_co,
|
|
y: _ArrayLikeComplex_co,
|
|
bins: int | Sequence[int] = ...,
|
|
range: None | _ArrayLikeFloat_co = ...,
|
|
density: None | bool = ...,
|
|
weights: None | _ArrayLikeFloat_co = ...,
|
|
) -> tuple[
|
|
NDArray[float64],
|
|
NDArray[complexfloating[Any, Any]],
|
|
NDArray[complexfloating[Any, Any]],
|
|
]: ...
|
|
@overload # TODO: Sort out `bins`
|
|
def histogram2d(
|
|
x: _ArrayLikeComplex_co,
|
|
y: _ArrayLikeComplex_co,
|
|
bins: Sequence[_ArrayLikeInt_co],
|
|
range: None | _ArrayLikeFloat_co = ...,
|
|
density: None | bool = ...,
|
|
weights: None | _ArrayLikeFloat_co = ...,
|
|
) -> tuple[
|
|
NDArray[float64],
|
|
NDArray[Any],
|
|
NDArray[Any],
|
|
]: ...
|
|
|
|
# NOTE: we're assuming/demanding here the `mask_func` returns
|
|
# an ndarray of shape `(n, n)`; otherwise there is the possibility
|
|
# of the output tuple having more or less than 2 elements
|
|
@overload
|
|
def mask_indices(
|
|
n: int,
|
|
mask_func: _MaskFunc[int],
|
|
k: int = ...,
|
|
) -> tuple[NDArray[intp], NDArray[intp]]: ...
|
|
@overload
|
|
def mask_indices(
|
|
n: int,
|
|
mask_func: _MaskFunc[_T],
|
|
k: _T,
|
|
) -> tuple[NDArray[intp], NDArray[intp]]: ...
|
|
|
|
def tril_indices(
|
|
n: int,
|
|
k: int = ...,
|
|
m: None | int = ...,
|
|
) -> tuple[NDArray[int_], NDArray[int_]]: ...
|
|
|
|
def tril_indices_from(
|
|
arr: NDArray[Any],
|
|
k: int = ...,
|
|
) -> tuple[NDArray[int_], NDArray[int_]]: ...
|
|
|
|
def triu_indices(
|
|
n: int,
|
|
k: int = ...,
|
|
m: None | int = ...,
|
|
) -> tuple[NDArray[int_], NDArray[int_]]: ...
|
|
|
|
def triu_indices_from(
|
|
arr: NDArray[Any],
|
|
k: int = ...,
|
|
) -> tuple[NDArray[int_], NDArray[int_]]: ...
|
|
|