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?? ..
?? __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)

Editing: arraysetops.pyi

from typing import (
    Literal as L,
    Any,
    TypeVar,
    overload,
    SupportsIndex,
)

from numpy import (
    generic,
    number,
    bool_,
    ushort,
    ubyte,
    uintc,
    uint,
    ulonglong,
    short,
    int8,
    byte,
    intc,
    int_,
    intp,
    longlong,
    half,
    single,
    double,
    longdouble,
    csingle,
    cdouble,
    clongdouble,
    timedelta64,
    datetime64,
    object_,
    str_,
    bytes_,
    void,
)

from numpy._typing import (
    ArrayLike,
    NDArray,
    _ArrayLike,
    _ArrayLikeBool_co,
    _ArrayLikeDT64_co,
    _ArrayLikeTD64_co,
    _ArrayLikeObject_co,
    _ArrayLikeNumber_co,
)

_SCT = TypeVar("_SCT", bound=generic)
_NumberType = TypeVar("_NumberType", bound=number[Any])

# Explicitly set all allowed values to prevent accidental castings to
# abstract dtypes (their common super-type).
#
# Only relevant if two or more arguments are parametrized, (e.g. `setdiff1d`)
# which could result in, for example, `int64` and `float64`producing a
# `number[_64Bit]` array
_SCTNoCast = TypeVar(
    "_SCTNoCast",
    bool_,
    ushort,
    ubyte,
    uintc,
    uint,
    ulonglong,
    short,
    byte,
    intc,
    int_,
    longlong,
    half,
    single,
    double,
    longdouble,
    csingle,
    cdouble,
    clongdouble,
    timedelta64,
    datetime64,
    object_,
    str_,
    bytes_,
    void,
)

__all__: list[str]

@overload
def ediff1d(
    ary: _ArrayLikeBool_co,
    to_end: None | ArrayLike = ...,
    to_begin: None | ArrayLike = ...,
) -> NDArray[int8]: ...
@overload
def ediff1d(
    ary: _ArrayLike[_NumberType],
    to_end: None | ArrayLike = ...,
    to_begin: None | ArrayLike = ...,
) -> NDArray[_NumberType]: ...
@overload
def ediff1d(
    ary: _ArrayLikeNumber_co,
    to_end: None | ArrayLike = ...,
    to_begin: None | ArrayLike = ...,
) -> NDArray[Any]: ...
@overload
def ediff1d(
    ary: _ArrayLikeDT64_co | _ArrayLikeTD64_co,
    to_end: None | ArrayLike = ...,
    to_begin: None | ArrayLike = ...,
) -> NDArray[timedelta64]: ...
@overload
def ediff1d(
    ary: _ArrayLikeObject_co,
    to_end: None | ArrayLike = ...,
    to_begin: None | ArrayLike = ...,
) -> NDArray[object_]: ...

@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[False] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> NDArray[_SCT]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[False] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> NDArray[Any]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[True] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[True] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[False] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[False] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[False] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[False] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[True] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[True] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[True] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[True] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[False] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[False] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[True] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[True] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp], NDArray[intp]]: ...

@overload
def intersect1d(
    ar1: _ArrayLike[_SCTNoCast],
    ar2: _ArrayLike[_SCTNoCast],
    assume_unique: bool = ...,
    return_indices: L[False] = ...,
) -> NDArray[_SCTNoCast]: ...
@overload
def intersect1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
    assume_unique: bool = ...,
    return_indices: L[False] = ...,
) -> NDArray[Any]: ...
@overload
def intersect1d(
    ar1: _ArrayLike[_SCTNoCast],
    ar2: _ArrayLike[_SCTNoCast],
    assume_unique: bool = ...,
    return_indices: L[True] = ...,
) -> tuple[NDArray[_SCTNoCast], NDArray[intp], NDArray[intp]]: ...
@overload
def intersect1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
    assume_unique: bool = ...,
    return_indices: L[True] = ...,
) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...

@overload
def setxor1d(
    ar1: _ArrayLike[_SCTNoCast],
    ar2: _ArrayLike[_SCTNoCast],
    assume_unique: bool = ...,
) -> NDArray[_SCTNoCast]: ...
@overload
def setxor1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
    assume_unique: bool = ...,
) -> NDArray[Any]: ...

def in1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
    assume_unique: bool = ...,
    invert: bool = ...,
) -> NDArray[bool_]: ...

def isin(
    element: ArrayLike,
    test_elements: ArrayLike,
    assume_unique: bool = ...,
    invert: bool = ...,
) -> NDArray[bool_]: ...

@overload
def union1d(
    ar1: _ArrayLike[_SCTNoCast],
    ar2: _ArrayLike[_SCTNoCast],
) -> NDArray[_SCTNoCast]: ...
@overload
def union1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
) -> NDArray[Any]: ...

@overload
def setdiff1d(
    ar1: _ArrayLike[_SCTNoCast],
    ar2: _ArrayLike[_SCTNoCast],
    assume_unique: bool = ...,
) -> NDArray[_SCTNoCast]: ...
@overload
def setdiff1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
    assume_unique: bool = ...,
) -> NDArray[Any]: ...

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