OXIESEC PANEL

Name	Size	Modified	Perms
📁 ..	-	10/26/2024 01:26:35 PM	rwxr-xr-x
📄 __init__.py	2.2 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 __init__.pyi	5.48 KB	10/26/2024 01:26:29 PM	rw-r--r--
📁 __pycache__	-	10/26/2024 01:27:40 PM	rwxr-xr-x
📄 _datasource.py	22.11 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 _iotools.py	30.15 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 _version.py	4.74 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 _version.pyi	633 bytes	10/26/2024 01:26:29 PM	rw-r--r--
📄 arraypad.py	30.63 KB	10/26/2024 01:26:27 PM	rw-r--r--
📄 arraypad.pyi	1.69 KB	10/26/2024 01:26:27 PM	rw-r--r--
📄 arraysetops.py	32.87 KB	10/26/2024 01:26:27 PM	rw-r--r--
📄 arraysetops.pyi	8.14 KB	10/26/2024 01:26:27 PM	rw-r--r--
📄 arrayterator.py	6.9 KB	10/26/2024 01:26:27 PM	rw-r--r--
📄 arrayterator.pyi	1.5 KB	10/26/2024 01:26:27 PM	rw-r--r--
📄 format.py	33.41 KB	10/26/2024 01:26:27 PM	rw-r--r--
📄 format.pyi	748 bytes	10/26/2024 01:26:27 PM	rw-r--r--
📄 function_base.py	180.8 KB	10/26/2024 01:26:27 PM	rw-r--r--
📄 function_base.pyi	16.2 KB	10/26/2024 01:26:27 PM	rw-r--r--
📄 histograms.py	36.81 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 histograms.pyi	995 bytes	10/26/2024 01:26:28 PM	rw-r--r--
📄 index_tricks.py	30.22 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 index_tricks.pyi	4.14 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 mixins.py	6.89 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 mixins.pyi	3.04 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 nanfunctions.py	64.02 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 nanfunctions.pyi	606 bytes	10/26/2024 01:26:28 PM	rw-r--r--
📄 npyio.py	95.15 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 npyio.pyi	9.39 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 polynomial.py	43.11 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 polynomial.pyi	6.79 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 recfunctions.py	54.93 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 scimath.py	14.68 KB	10/26/2024 01:26:28 PM	rw-r--r--
📄 scimath.pyi	2.82 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 setup.py	405 bytes	10/26/2024 01:26:29 PM	rw-r--r--
📄 shape_base.py	38.35 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 shape_base.pyi	5.06 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 stride_tricks.py	17.49 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 stride_tricks.pyi	1.71 KB	10/26/2024 01:26:29 PM	rw-r--r--
📁 tests	-	10/26/2024 01:28:54 PM	rwxr-xr-x
📄 twodim_base.py	30.62 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 twodim_base.pyi	5.24 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 type_check.py	19.46 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 type_check.pyi	5.44 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 ufunclike.py	7.84 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 ufunclike.pyi	1.26 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 user_array.py	7.54 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 utils.py	35.3 KB	10/26/2024 01:26:29 PM	rw-r--r--
📄 utils.pyi	2.3 KB	10/26/2024 01:26:29 PM	rw-r--r--

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]: ...