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Editing: twodim_base.pyi
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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