golden hour

/opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/lib

File/Folder	Size	Actions
__init__.py	2.7 KB
__init__.pyi	5.46 KB
__pycache__	-
_datasource.py	22.1 KB
_iotools.py	30.14 KB
_version.py	4.74 KB
_version.pyi	633 B
arraypad.py	31.06 KB
arraypad.pyi	1.69 KB
arraysetops.py	32.87 KB
arraysetops.pyi	8.14 KB
arrayterator.py	6.9 KB
arrayterator.pyi	1.5 KB
format.py	33.95 KB
format.pyi	748 B
function_base.py	184.67 KB
function_base.pyi	16.2 KB
histograms.py	36.81 KB
histograms.pyi	995 B
index_tricks.py	30.61 KB
index_tricks.pyi	4.15 KB
mixins.py	6.91 KB
mixins.pyi	3.04 KB
nanfunctions.py	64.23 KB
nanfunctions.pyi	606 B
npyio.py	95.04 KB
npyio.pyi	9.5 KB
polynomial.py	43.1 KB
polynomial.pyi	6.79 KB
recfunctions.py	58.03 KB
scimath.py	14.68 KB
scimath.pyi	2.82 KB
setup.py	405 B
shape_base.py	38.03 KB
shape_base.pyi	5.06 KB
stride_tricks.py	17.49 KB
stride_tricks.pyi	1.71 KB
tests	-
twodim_base.py	32.17 KB
twodim_base.pyi	5.24 KB
type_check.py	19.49 KB
type_check.pyi	5.44 KB
ufunclike.py	6.18 KB
ufunclike.pyi	1.26 KB
user_array.py	7.54 KB
utils.py	36.92 KB
utils.pyi	2.3 KB

Edit: 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_]]: ...