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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63 changes: 48 additions & 15 deletions numpy-stubs/__init__.pyi
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8000
Original file line number Diff line number Diff line change
Expand Up @@ -813,14 +813,28 @@ _ScalarNumpy = Union[generic, dt.datetime, dt.timedelta]
_ScalarBuiltin = Union[str, bytes, dt.date, dt.timedelta, bool, int, float, complex]
_Scalar = Union[_ScalarBuiltin, _ScalarNumpy]

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

# An array-like object consisting of integers
_Int = Union[int, integer]
_Bool = Union[bool, bool_]
_IntOrBool = Union[_Int, _Bool]
_ArrayLikeIntNested = Any # TODO: wait for support for recursive types
_ArrayLikeInt = Union[_Int, ndarray, Sequence[_Int], Sequence[_ArrayLikeIntNested]]
_ArrayLikeBoolNested = Any # TODO: wait for support for recursive types

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

# The signature of take() follows a common theme with its overloads:
# 1. A generic comes in; the same generic comes out
Expand All @@ -829,12 +843,12 @@ _ArrayLikeInt = Union[_Int, ndarray, Sequence[_Int], Sequence[_ArrayLikeIntNeste
# 4. An array-like object comes in; an ndarray or generic comes out
@overload
def take(
a: _ScalarGeneric,
a: _ScalarGenericDT,
indices: int,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> _ScalarGeneric: ...
) -> _ScalarGenericDT: ...
@overload
def take(
a: _Scalar,
Expand All @@ -854,37 +868,39 @@ 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: _ScalarGeneric,
a: _ScalarIntOrBool,
choices: Union[Sequence[_ArrayLike], ndarray],
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> _ScalarGeneric: ...
) -> _ScalarIntOrBool: ...
@overload
def choose(
a: _Scalar,
a: _IntOrBool,
choices: Union[Sequence[_ArrayLike], ndarray],
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> _ScalarNumpy: ...
) -> Union[integer, bool_]: ...
@overload
def choose(
a: _ArrayLike,
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 @@ -893,14 +909,31 @@ 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: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> integer: ...
@overload
def argpartition(
a: _ScalarBuiltin,
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]] = ...,
Expand Down
26 changes: 14 additions & 12 deletions tests/fail/fromnumeric.py
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Expand Up @@ -7,17 +7,17 @@

a = np.bool_(True)

np.take(a, None) # E: No overload variant of "take" matches argument types
np.take(a, axis=1.0) # E: No overload variant of "take" matches argument types
np.take(A, out=1) # E: No overload variant of "take" matches argument types
np.take(A, mode="bob") # E: No overload variant of "take" matches argument types
np.take(a, None) # E: No overload variant of "take" matches argument type
np.take(a, axis=1.0) # E: No overload variant of "take" matches argument type
np.take(A, out=1) # E: No overload variant of "take" matches argument type
np.take(A, mode="bob") # E: No overload variant of "take" matches argument type

np.reshape(a, None) # E: Argument 2 to "reshape" has incompatible type
np.reshape(A, 1, order="bob") # E: Argument "order" to "reshape" has incompatible type

np.choose(a, None) # E: No overload variant of "choose" matches argument types
np.choose(a, out=1.0) # E: No overload variant of "choose" matches argument types
np.choose(A, mode="bob") # E: No overload variant of "choose" matches argument types
np.choose(a, None) # E: No overload variant of "choose" matches argument type
np.choose(a, out=1.0) # E: No overload variant of "choose" matches argument type
np.choose(A, mode="bob") # E: No overload variant of "choose" matches argument type

np.repeat(a, None) # E: Argument 2 to "repeat" has incompatible type
np.repeat(A, 1, axis=1.0) # E: Argument "axis" to "repeat" has incompatible type
Expand All @@ -40,12 +40,14 @@
A, 0, order=range(5) # E: Argument "order" to "partition" has incompatible type
)

np.argpartition(a, None) # E: Argument 2 to "argpartition" has incompatible type
np.argpartition(
a, 0, axis="bob" # E: Argument "axis" to "argpartition" has incompatible type
np.argpartition( # E: No overload variant of "argpartition" matches argument type
a, None
)
np.argpartition(
A, 0, kind="bob" # E: Argument "kind" to "argpartition" has incompatible type
np.argpartition( # E: No overload variant of "argpartition" matches argument type
a, 0, axis="bob"
)
np.argpartition( # E: No overload variant of "argpartition" matches argument type
A, 0, kind="bob"
)
np.argpartition(
A, 0, order=range(5) # E: Argument "order" to "argpartition" has incompatible type
Expand Down
13 changes: 5 additions & 8 deletions tests/pass/fromnumeric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,11 +25,8 @@
np.reshape(A, 1)
np.reshape(B, 1)

np.choose(a, [True])
np.choose(b, [1.0])
np.choose(c, [1.0])
np.choose(A, [True])
np.choose(B, [1.0])
np.choose(a, [True, True])
np.choose(A, [1.0, 1.0])

np.repeat(a, 1)
np.repeat(b, 1)
Expand All @@ -46,9 +43,9 @@
np.transpose(A)
np.transpose(B)

np.partition(a, 0)
np.partition(b, 0)
np.partition(c, 0)
np.partition(a, 0, axis=None)
np.partition(b, 0, axis=None)
np.partition(c, 0, axis=None)
np.partition(A, 0)
np.partition(B, 0)

Expand Down
21 changes: 7 additions & 14 deletions tests/reveal/fromnumeric.py
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Expand Up @@ -39,15 +39,8 @@
reveal_type(np.reshape(A, 1)) # E: numpy.ndarray
reveal_type(np.reshape(B, 1)) # E: numpy.ndarray

reveal_type(np.choose(a, [True])) # E: numpy.bool_
reveal_type(np.choose(b, [1.0])) # E: numpy.float32
reveal_type(
np.choose( # E: Union[numpy.generic, datetime.datetime, datetime.timedelta]
c, [1.0]
)
)
reveal_type(np.choose(A, [True])) # E: numpy.ndarray
reveal_type(np.choose(B, [1.0])) # E: numpy.ndarray
reveal_type(np.choose(a, [True, True])) # E: numpy.bool_
reveal_type(np.choose(A, [True, True])) # E: numpy.ndarray

reveal_type(np.repeat(a, 1)) # E: numpy.ndarray
reveal_type(np.repeat(b, 1)) # E: numpy.ndarray
Expand All @@ -66,14 +59,14 @@
reveal_type(np.transpose(A)) # E: numpy.ndarray
reveal_type(np.transpose(B)) # E: numpy.ndarray

reveal_type(np.partition(a, 0)) # E: numpy.ndarray
reveal_type(np.partition(b, 0)) # E: numpy.ndarray
reveal_type(np.partition(c, 0)) # E: numpy.ndarray
reveal_type(np.partition(a, 0, axis=None)) # E: numpy.ndarray
reveal_type(np.partition(b, 0, axis=None)) # E: numpy.ndarray
reveal_type(np.partition(c, 0, axis=None)) # E: numpy.ndarray
reveal_type(np.partition(A, 0)) # E: numpy.ndarray
reveal_type(np.partition(B, 0)) # E: numpy.ndarray

reveal_type(np.argpartition(a, 0)) # E: numpy.ndarray
reveal_type(np.argpartition(b, 0)) # E: numpy.ndarray
reveal_type(np.argpartition(a, 0)) # E: numpy.integer
reveal_type(np.argpartition(b, 0)) # E: numpy.integer
reveal_type(np.argpartition(c, 0)) # E: numpy.ndarray
reveal_type(np.argpartition(A, 0)) # E: numpy.ndarray
reveal_type(np.argpartition(B, 0)) # E: numpy.ndarray
Expand Down
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