8000 MAINT: Introduced a couple of fixes for the np.core.fromnumeric functions by BvB93 · Pull Request #74 · numpy/numpy-stubs · GitHub
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MAINT: Introduced a couple of fixes for the np.core.fromnumeric functions #74

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May 1, 2020
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MAINT: Introduced a couple of fixes for the np.core.fromnumeric functions #74

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22a8632
Added 11 new functions + tests and updated some existing annotations
Apr 24, 2020
4e2e6bf
Replace _ArrayLike with _ArrayLikeNested for >= 1D arrays
Apr 24, 2020
fcd4e46
Update __init__.pyi
Apr 24, 2020
062947b
Removed the in https://github.com/numpy/numpy-stubs/pull/71 introduce…
Apr 25, 2020
443b301
``_ArrayLikeNested `` is redundant in its curent implementation; remo…
Apr 26, 2020
e519d92
Removed a leftover from https://github.com/numpy/numpy-stubs/pull/71
Apr 27, 2020
911eed0
Addressed comments from https://github.com/numpy/numpy-stubs/pull/74
Apr 29, 2020
cda1e70
Fixed an incorrect variable name
Apr 30, 2020
45158c1
Fixed: Forgot to actually rename ``_Int`` to ``_IntOrBool``in https:/…
Apr 30, 2020
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Addressed comments from #74 
* Renamed ``_ScalarGenericPlus `` to ``_ScalarGenericDT`` (#74 (comment)).
* Renamed ``_Int`` to ``_IntOrBool`` (#74 (comment)).
* Renamed ``_ArrayLikeInt`` to ``_ArrayLikeIntOrBool`` (#74 (comment)).
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Bas van Beek committed Apr 29, 2020
commit 911eed0a6998ba36fe8ead171dd4a801031475a9
36 changes: 19 additions & 17 deletions numpy-stubs/__init__.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -814,10 +814,10 @@ _ScalarBuiltin = Union[str, bytes, dt.date, dt.timedelta, bool, int, float, comp
_Scalar = Union[_ScalarBuiltin, _ScalarNumpy]

# Integers and booleans can generally be used interchangeably
_ScalarInt = TypeVar("_ScalarInt", bound=Union[integer, bool_])
_ScalarIntOrBool = TypeVar("_ScalarIntOrBool", bound=Union[integer, bool_])
_ScalarGeneric = TypeVar("_ScalarGeneric", bound=generic)
_ScalarGenericPlus = TypeVar(
"_ScalarGenericPlus", bound=Union[dt.datetime, dt.timedelta, generic]
_ScalarGenericDT = TypeVar(
"_ScalarGenericDT", bound=Union[dt.datetime, dt.timedelta, generic]
)

# An array-like object consisting of integers
Expand All @@ -827,11 +827,11 @@ _ArrayLikeIntNested = Any # TODO: wait for support for recursive types
_ArrayLikeBoolNested = Any # TODO: wait for support for recursive types

# Integers and booleans can generally be used interchangeably
_ArrayLikeInt = Union[
_ArrayLikeIntOrBool = Union[
_Int,
ndarray,
Sequence[_Int],
Sequence[_ArrayLikeIntNested],
Sequence[_ArrayLikeIntOrBoolNested],
Sequence[_ArrayLikeBoolNested],
]

Expand All @@ -842,12 +842,12 @@ _ArrayLikeInt = Union[
# 4. An array-like object comes in; an ndarray or generic comes out
@overload
def take(
a: _ScalarGenericPlus,
a: _ScalarGenericDT,
indices: int,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> _ScalarGenericPlus: ...
) -> _ScalarGenericDT 8000 : ...
@overload
def take(
a: _Scalar,
Expand All @@ -867,19 +867,19 @@ def take(
@overload
def take(
a: _ArrayLike,
indices: _ArrayLikeInt,
indices: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> Union[_ScalarNumpy, ndarray]: ...
def reshape(a: _ArrayLike, newshape: _ShapeLike, order: _Order = ...) -> ndarray: ...
@overload
def choose(
a: _ScalarInt,
a: _ScalarIntOrBool,
choices: Union[Sequence[_ArrayLike], ndarray],
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> _ScalarInt: ...
) -> _ScalarIntOrBool: ...
@overload
def choose(
a: _Int,
Expand All @@ -889,15 +889,17 @@ def choose(
) -> Union[integer, bool_]: ...
@overload
def choose(
a: _ArrayLikeInt,
a: _ArrayLikeIntOrBool,
choices: Union[Sequence[_ArrayLike], ndarray],
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> ndarray: ...
def repeat(
a: _ArrayLike, repeats: _ArrayLikeInt, axis: Optional[int] = ...
a: _ArrayLike, repeats: _ArrayLikeIntOrBool, axis: Optional[int] = ...
) -> ndarray: ...
def put(a: ndarray, ind: _ArrayLikeInt, v: _ArrayLike, mode: _Mode = ...) -> None: ...
def put(
a: ndarray, ind: _ArrayLikeIntOrBool, v: _ArrayLike, mode: _Mode = ...
) -> None: ...
def swapaxes(
a: Union[Sequence[_ArrayLike], ndarray], axis1: int, axis2: int
) -> ndarray: ...
Expand All @@ -906,31 +908,31 @@ def transpose(
) -> ndarray: ...
def partition(
a: _ArrayLike,
kth: _ArrayLikeInt,
kth: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
@overload
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The behaviour of np.argpartition() here is a bit odd.
If an np.ndarray is passed an np.ndarray is returned and if a np.generic is passed a np.generic is returned, so far so good.

However, if a builtin scalar is passed it also returns a np.ndarray, rather than a np.generic.

>>> import numpy as np

>>> type(np.argpartition(0, 0))
numpy.ndarray

>>> type(np.argpartition(np.int64(0), 0))
numpy.int64

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Ouch. Sounds like it could potentially be worthy of opening an issue against NumPy.

def argpartition(
a: generic,
kth: _ArrayLikeInt,
kth: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> integer: ...
@overload
def argpartition(
a: _ScalarBuiltin,
kth: _ArrayLikeInt,
kth: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
@overload
def argpartition(
a: _ArrayLike,
kth: _ArrayLikeInt,
kth: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
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