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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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