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