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360 lines
8.1 KiB
360 lines
8.1 KiB
from typing import (
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Literal as L,
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Any,
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TypeVar,
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overload,
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SupportsIndex,
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)
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from numpy import (
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generic,
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number,
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bool_,
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ushort,
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ubyte,
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uintc,
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uint,
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ulonglong,
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short,
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int8,
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byte,
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intc,
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int_,
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intp,
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longlong,
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half,
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single,
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double,
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longdouble,
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csingle,
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cdouble,
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clongdouble,
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timedelta64,
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datetime64,
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object_,
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str_,
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bytes_,
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void,
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)
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from numpy._typing import (
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ArrayLike,
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NDArray,
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_ArrayLike,
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_ArrayLikeBool_co,
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_ArrayLikeDT64_co,
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_ArrayLikeTD64_co,
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_ArrayLikeObject_co,
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_ArrayLikeNumber_co,
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)
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_SCT = TypeVar("_SCT", bound=generic)
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_NumberType = TypeVar("_NumberType", bound=number[Any])
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# Explicitly set all allowed values to prevent accidental castings to
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# abstract dtypes (their common super-type).
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#
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# Only relevant if two or more arguments are parametrized, (e.g. `setdiff1d`)
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# which could result in, for example, `int64` and `float64`producing a
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# `number[_64Bit]` array
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_SCTNoCast = TypeVar(
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"_SCTNoCast",
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bool_,
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ushort,
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ubyte,
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uintc,
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uint,
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ulonglong,
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short,
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byte,
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intc,
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int_,
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longlong,
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half,
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single,
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double,
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longdouble,
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csingle,
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cdouble,
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clongdouble,
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timedelta64,
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datetime64,
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object_,
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str_,
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bytes_,
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void,
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)
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__all__: list[str]
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@overload
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def ediff1d(
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ary: _ArrayLikeBool_co,
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to_end: None | ArrayLike = ...,
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to_begin: None | ArrayLike = ...,
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) -> NDArray[int8]: ...
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@overload
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def ediff1d(
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ary: _ArrayLike[_NumberType],
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to_end: None | ArrayLike = ...,
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to_begin: None | ArrayLike = ...,
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) -> NDArray[_NumberType]: ...
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@overload
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def ediff1d(
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ary: _ArrayLikeNumber_co,
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to_end: None | ArrayLike = ...,
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to_begin: None | ArrayLike = ...,
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) -> NDArray[Any]: ...
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@overload
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def ediff1d(
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ary: _ArrayLikeDT64_co | _ArrayLikeTD64_co,
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to_end: None | ArrayLike = ...,
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to_begin: None | ArrayLike = ...,
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) -> NDArray[timedelta64]: ...
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@overload
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def ediff1d(
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ary: _ArrayLikeObject_co,
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to_end: None | ArrayLike = ...,
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to_begin: None | ArrayLike = ...,
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) -> NDArray[object_]: ...
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@overload
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def unique(
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ar: _ArrayLike[_SCT],
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return_index: L[False] = ...,
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return_inverse: L[False] = ...,
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return_counts: L[False] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> NDArray[_SCT]: ...
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@overload
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def unique(
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ar: ArrayLike,
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return_index: L[False] = ...,
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return_inverse: L[False] = ...,
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return_counts: L[False] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> NDArray[Any]: ...
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@overload
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def unique(
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ar: _ArrayLike[_SCT],
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return_index: L[True] = ...,
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return_inverse: L[False] = ...,
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return_counts: L[False] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
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@overload
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def unique(
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ar: ArrayLike,
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return_index: L[True] = ...,
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return_inverse: L[False] = ...,
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return_counts: L[False] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[Any], NDArray[intp]]: ...
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@overload
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def unique(
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ar: _ArrayLike[_SCT],
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return_index: L[False] = ...,
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return_inverse: L[True] = ...,
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return_counts: L[False] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
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@overload
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def unique(
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ar: ArrayLike,
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return_index: L[False] = ...,
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return_inverse: L[True] = ...,
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return_counts: L[False] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[Any], NDArray[intp]]: ...
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@overload
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def unique(
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ar: _ArrayLike[_SCT],
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return_index: L[False] = ...,
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return_inverse: L[False] = ...,
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return_counts: L[True] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
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@overload
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def unique(
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ar: ArrayLike,
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return_index: L[False] = ...,
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return_inverse: L[False] = ...,
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return_counts: L[True] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[Any], NDArray[intp]]: ...
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@overload
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def unique(
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ar: _ArrayLike[_SCT],
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return_index: L[True] = ...,
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return_inverse: L[True] = ...,
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return_counts: L[False] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
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@overload
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def unique(
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ar: ArrayLike,
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return_index: L[True] = ...,
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return_inverse: L[True] = ...,
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return_counts: L[False] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
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@overload
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def unique(
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ar: _ArrayLike[_SCT],
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return_index: L[True] = ...,
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return_inverse: L[False] = ...,
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return_counts: L[True] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
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@overload
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def unique(
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ar: ArrayLike,
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return_index: L[True] = ...,
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return_inverse: L[False] = ...,
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return_counts: L[True] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
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@overload
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def unique(
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ar: _ArrayLike[_SCT],
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return_index: L[False] = ...,
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return_inverse: L[True] = ...,
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return_counts: L[True] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
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@overload
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def unique(
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ar: ArrayLike,
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return_index: L[False] = ...,
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return_inverse: L[True] = ...,
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return_counts: L[True] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
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@overload
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def unique(
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ar: _ArrayLike[_SCT],
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return_index: L[True] = ...,
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return_inverse: L[True] = ...,
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return_counts: L[True] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp], NDArray[intp]]: ...
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@overload
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def unique(
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ar: ArrayLike,
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return_index: L[True] = ...,
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return_inverse: L[True] = ...,
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return_counts: L[True] = ...,
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axis: None | SupportsIndex = ...,
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*,
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equal_nan: bool = ...,
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) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp], NDArray[intp]]: ...
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@overload
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def intersect1d(
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ar1: _ArrayLike[_SCTNoCast],
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ar2: _ArrayLike[_SCTNoCast],
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assume_unique: bool = ...,
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return_indices: L[False] = ...,
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) -> NDArray[_SCTNoCast]: ...
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@overload
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def intersect1d(
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ar1: ArrayLike,
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ar2: ArrayLike,
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assume_unique: bool = ...,
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return_indices: L[False] = ...,
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) -> NDArray[Any]: ...
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@overload
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def intersect1d(
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ar1: _ArrayLike[_SCTNoCast],
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ar2: _ArrayLike[_SCTNoCast],
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assume_unique: bool = ...,
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return_indices: L[True] = ...,
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) -> tuple[NDArray[_SCTNoCast], NDArray[intp], NDArray[intp]]: ...
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@overload
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def intersect1d(
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ar1: ArrayLike,
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ar2: ArrayLike,
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assume_unique: bool = ...,
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return_indices: L[True] = ...,
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) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
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@overload
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def setxor1d(
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ar1: _ArrayLike[_SCTNoCast],
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ar2: _ArrayLike[_SCTNoCast],
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assume_unique: bool = ...,
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) -> NDArray[_SCTNoCast]: ...
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@overload
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def setxor1d(
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ar1: ArrayLike,
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ar2: ArrayLike,
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assume_unique: bool = ...,
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) -> NDArray[Any]: ...
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def in1d(
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ar1: ArrayLike,
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ar2: ArrayLike,
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assume_unique: bool = ...,
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invert: bool = ...,
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) -> NDArray[bool_]: ...
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def isin(
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element: ArrayLike,
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test_elements: ArrayLike,
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assume_unique: bool = ...,
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invert: bool = ...,
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) -> NDArray[bool_]: ...
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@overload
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def union1d(
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ar1: _ArrayLike[_SCTNoCast],
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ar2: _ArrayLike[_SCTNoCast],
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) -> NDArray[_SCTNoCast]: ...
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@overload
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def union1d(
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ar1: ArrayLike,
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ar2: ArrayLike,
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) -> NDArray[Any]: ...
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@overload
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def setdiff1d(
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ar1: _ArrayLike[_SCTNoCast],
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ar2: _ArrayLike[_SCTNoCast],
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assume_unique: bool = ...,
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) -> NDArray[_SCTNoCast]: ...
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@overload
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def setdiff1d(
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ar1: ArrayLike,
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ar2: ArrayLike,
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assume_unique: bool = ...,
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) -> NDArray[Any]: ...
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