ꦫꦣꦺꦤ꧀ꦄꦤ꧀ꦠꦱꦺꦤ

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

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]: ...

[-] arraysetops.pyi [open]
[-] arraysetops.py [open]
[-] user_array.py [open]
[+] ..
[-] stride_tricks.pyi [open]
[-] index_tricks.py [open]
[-] format.py [open]
[+] __pycache__
[-] arrayterator.py [open]
[+] tests
[-] _version.py [open]
[-] mixins.py [open]
[-] npyio.pyi [open]
[-] _version.pyi [open]
[-] index_tricks.pyi [open]
[-] ufunclike.pyi [open]
[-] arrayterator.pyi [open]
[-] _iotools.py [open]
[-] ufunclike.py [open]
[-] twodim_base.py [open]
[-] type_check.py [open]
[-] shape_base.py [open]
[-] histograms.py [open]
[-] stride_tricks.py [open]
[-] arraypad.py [open]
[-] __init__.pyi [open]
[-] format.pyi [open]
[-] polynomial.pyi [open]
[-] twodim_base.pyi [open]
[-] utils.py [open]
[-] function_base.pyi [open]
[-] recfunctions.py [open]
[-] histograms.pyi [open]
[-] utils.pyi [open]
[-] function_base.py [open]
[-] scimath.py [open]
[-] type_check.pyi [open]
[-] polynomial.py [open]
[-] scimath.pyi [open]
[-] __init__.py [open]
[-] _datasource.py [open]
[-] setup.py [open]
[-] nanfunctions.pyi [open]
[-] mixins.pyi [open]
[-] arraypad.pyi [open]
[-] nanfunctions.py [open]
[-] npyio.py [open]
[-] shape_base.pyi [open]