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

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

Name	Size	Mode	Actions
tests/	-	0755	rm
__pycache__/	-	0755	rm
arraypad.py	31362	0644	edit dl rm
arraypad.pyi	1728	0644	edit dl rm
arraysetops.py	33655	0644	edit dl rm
arraysetops.pyi	8337	0644	edit dl rm
arrayterator.py	7063	0644	edit dl rm
arrayterator.pyi	1537	0644	edit dl rm
format.py	34211	0644	edit dl rm
format.pyi	748	0644	edit dl rm
function_base.py	185141	0644	edit dl rm
function_base.pyi	16585	0644	edit dl rm
histograms.py	37698	0644	edit dl rm
histograms.pyi	995	0644	edit dl rm
index_tricks.py	30944	0644	edit dl rm
index_tricks.pyi	4241	0644	edit dl rm
mixins.py	7052	0644	edit dl rm
mixins.pyi	3117	0644	edit dl rm
nanfunctions.py	65556	0644	edit dl rm
nanfunctions.pyi	606	0644	edit dl rm
npyio.py	97461	0644	edit dl rm
npyio.pyi	9616	0644	edit dl rm
polynomial.py	44143	0644	edit dl rm
polynomial.pyi	6958	0644	edit dl rm
recfunctions.py	56247	0644	edit dl rm
scimath.py	15037	0644	edit dl rm
scimath.pyi	2883	0644	edit dl rm
setup.py	405	0644	edit dl rm
shape_base.py	39267	0644	edit dl rm
shape_base.pyi	5184	0644	edit dl rm
stride_tricks.py	17911	0644	edit dl rm
stride_tricks.pyi	1747	0644	edit dl rm
twodim_base.py	31381	0644	edit dl rm
twodim_base.pyi	5370	0644	edit dl rm
type_check.py	19931	0644	edit dl rm
type_check.pyi	5571	0644	edit dl rm
ufunclike.py	8031	0644	edit dl rm
ufunclike.pyi	1293	0644	edit dl rm
user_array.py	7721	0644	edit dl rm
utils.py	36147	0644	edit dl rm
utils.pyi	2360	0644	edit dl rm
_datasource.py	22643	0644	edit dl rm
_iotools.py	30873	0644	edit dl rm
_version.py	4855	0644	edit dl rm
_version.pyi	633	0644	edit dl rm
__init__.py	2253	0644	edit dl rm
__init__.pyi	5596	0644	edit dl rm

Edit: /opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/lib/arraysetops.pyi (8337B)

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]: ...

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]: ...