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

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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