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| 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_]]: ...
|
|
