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Mini Shell
Mini Shell
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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