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Mini Shell
Mini Shell
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_]]: ...
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