ENH: Add annotations to the last 8 functions in numpy.core.fromnumeric #16729
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@@ -1231,3 +1231,113 @@ def amin( | |
| initial: _NumberLike = ..., | ||
| where: _ArrayLikeBool = ..., | ||
| ) -> Union[number, ndarray]: ... | ||
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| # TODO: `np.prod()``: For object arrays `initial` does not necasarily | ||
| # have to be a numerical scalar. | ||
| # The only requirement is that it is compatible | ||
| # with the `.__mult__()` method(s) of the passed array's elements. | ||
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| # Note that the same holds for `np.sum()` and `.__add__()`. | ||
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| @overload | ||
| def prod( | ||
| a: _Number, | ||
| axis: Optional[_ShapeLike] = ..., | ||
| dtype: DtypeLike = ..., | ||
| out: Optional[ndarray] = ..., | ||
| keepdims: bool = ..., | ||
| initial: _NumberLike = ..., | ||
| where: _ArrayLikeBool = ..., | ||
| ) -> _Number: ... | ||
| @overload | ||
| def prod( | ||
| a: ArrayLike, | ||
| axis: None = ..., | ||
| dtype: DtypeLike = ..., | ||
| out: Optional[ndarray] = ..., | ||
| keepdims: Literal[False] = ..., | ||
| initial: _NumberLike = ..., | ||
| where: _ArrayLikeBool = ..., | ||
| ) -> number: ... | ||
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There was a problem hiding this comment. I am assuming we are allowing number subtypes for one but not the other why is that ? There was a problem hiding this comment. The key difference is that the first one is a To illustrate, in the first overload an arbitrary In the second overload we know that an instance of Lines 511 to 517 in 92665ab As a workaround the latter case is now simply annotated as returning a There was a problem hiding this comment. okay got it, if my understanding is correct, the functionality is exactly the same if we use number of _Number here (second overload), but it is something we want to change in the future ? There was a problem hiding this comment.
There was a problem hiding this comment. Oh hang on, I'm actually confusing the issues pertaining The problem with returning >>> from typing import TypeVar
>>> import numpy as np
>>> from numpy.typing import ArrayLike
>>> T = TypeVar('T', bound=np.number)
>>> def func(a: ArrayLike[T]) -> T:
... passIf you're interested, see #16759 for more details. |
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| @overload | ||
| def prod( | ||
| a: ArrayLike, | ||
| axis: Optional[_ShapeLike] = ..., | ||
| dtype: DtypeLike = ..., | ||
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| out: Optional[ndarray] = ..., | ||
| keepdims: bool = ..., | ||
| initial: _NumberLike = ..., | ||
| where: _ArrayLikeBool = ..., | ||
| ) -> Union[number, ndarray]: ... | ||
| def cumprod( | ||
| a: ArrayLike, | ||
| axis: Optional[int] = ..., | ||
| dtype: DtypeLike = ..., | ||
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| out: Optional[ndarray] = ..., | ||
| ) -> ndarray: ... | ||
| def ndim(a: ArrayLike) -> int: ... | ||
| def size(a: ArrayLike, axis: Optional[int] = ...) -> int: ... | ||
| @overload | ||
| def around( | ||
| a: _Number, decimals: int = ..., out: Optional[ndarray] = ... | ||
| ) -> _Number: ... | ||
| @overload | ||
| def around( | ||
| a: _NumberLike, decimals: int = ..., out: Optional[ndarray] = ... | ||
| ) -> number: ... | ||
| @overload | ||
| def around( | ||
| a: ArrayLike, decimals: int = ..., out: Optional[ndarray] = ... | ||
| ) -> ndarray: ... | ||
| @overload | ||
| def mean( | ||
| a: ArrayLike, | ||
| axis: None = ..., | ||
| dtype: DtypeLike = ..., | ||
| out: Optional[ndarray] = ..., | ||
| keepdims: Literal[False] = ..., | ||
| ) -> number: ... | ||
| @overload | ||
| def mean( | ||
| a: ArrayLike, | ||
| axis: Optional[_ShapeLike] = ..., | ||
| dtype: DtypeLike = ..., | ||
| out: Optional[ndarray] = ..., | ||
| keepdims: bool = ..., | ||
| ) -> Union[number, ndarray]: ... | ||
| @overload | ||
| def std( | ||
| a: ArrayLike, | ||
| axis: None = ..., | ||
| dtype: DtypeLike = ..., | ||
| out: Optional[ndarray] = ..., | ||
| ddof: int = ..., | ||
| keepdims: Literal[False] = ..., | ||
| ) -> number: ... | ||
| @overload | ||
| def std( | ||
| a: ArrayLike, | ||
| axis: Optional[_ShapeLike] = ..., | ||
| dtype: DtypeLike = ..., | ||
| out: Optional[ndarray] = ..., | ||
| ddof: int = ..., | ||
| keepdims: bool = ..., | ||
| ) -> Union[number, ndarray]: ... | ||
| @overload | ||
| def var( | ||
| a: ArrayLike, | ||
| axis: None = ..., | ||
| dtype: DtypeLike = ..., | ||
| out: Optional[ndarray] = ..., | ||
| ddof: int = ..., | ||
| keepdims: Literal[False] = ..., | ||
| ) -> number: ... | ||
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There was a problem hiding this comment. This applies for most but not all functions in this patch with There was a problem hiding this comment.
Considering the widespread nature of this issue I'd propose to save it for a future maintenance pull request and fix it all at once (as there are already quite a few older annotated functions with There was a problem hiding this comment. In the meantime then, it would probably be a good idea to annotate these with There was a problem hiding this comment. As it turns out is fairly easy way to fix this: I have noticed some inconsistent behavior when using In [1]: import numpy as np
In [2]: np.__version__
Out[2]: '1.20.0.dev0+62f26cf'
In [3]: np.var(np.int64(1), out=np.zeros(()))
Out[3]: 0.0
In [4]: np.var(1, out=np.zeros(()))
Out[4]: array(0.)
In [5]: np.var([1], out=np.zeros(()))
Out[5]: array(0.)
In [6]: np.var(np.ones(1), out=np.zeros(()))
Out[6]: array(0.)There was a problem hiding this comment. That is arguably a bug in There was a problem hiding this comment.
Fixed as of 5a42440. There was a problem hiding this comment.
I'll double check which (currently annotated) functions are affected by this this and create an issue afterwards (see #16734). |
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| @overload | ||
| def var( | ||
| a: ArrayLike, | ||
| axis: Optional[_ShapeLike] = ..., | ||
| dtype: DtypeLike = ..., | ||
| out: Optional[ndarray] = ..., | ||
| ddof: int = ..., | ||
| keepdims: bool = ..., | ||
| ) -> Union[number, ndarray]: ... | ||
| Original file line number | Diff line number | Diff line change |
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@@ -124,3 +124,31 @@ | |
| np.amin(a, out=1.0) # E: No overload variant of "amin" matches argument type | ||
| np.amin(a, initial=[1.0]) # E: No overload variant of "amin" matches argument type | ||
| np.amin(a, where=[1.0]) # E: List item 0 has incompatible type | ||
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| np.prod(a, axis=1.0) # E: No overload variant of "prod" matches argument type | ||
| np.prod(a, out=False) # E: No overload variant of "prod" matches argument type | ||
| np.prod(a, keepdims=1.0) # E: No overload variant of "prod" matches argument type | ||
| np.prod(a, initial=int) # E: No overload variant of "prod" matches argument type | ||
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There was a problem hiding this comment. This is legal for suitable a: Or for an almost plausible use: >>> from ctypes import c_uint8
>>> shape = np.array([1, 2, 3])
>>> np.prod(shape.astype(object), initial=c_uint8)
# a ctypes array type
numpy.core.fromnumeric.c_ubyte_Array_1_Array_2_Array_3There was a problem hiding this comment. Incidentally, this also proves that the return type of There was a problem hiding this comment.
If I'm not mistaken this is limited to, the notoriously difficult to type, object arrays. The alternative would be to set There was a problem hiding this comment.
How so? Perhaps the compromise is to stick There was a problem hiding this comment.
That's actually a good point; I more or less conflated scalar with numerical scalar here.
I agree, I'll push an update in a bit. As for the future: What I've gathered is, in the case of object arrays, that There was a problem hiding this comment.
Added in 3bc51b8. There was a problem hiding this comment.
I wouldn't personally bother. Simply having |
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| np.prod(a, where=1.0) # E: No overload variant of "prod" matches argument type | ||
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| np.cumprod(a, axis=1.0) # E: Argument "axis" to "cumprod" has incompatible type | ||
| np.cumprod(a, out=False) # E: Argument "out" to "cumprod" has incompatible type | ||
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| np.size(a, axis=1.0) # E: Argument "axis" to "size" has incompatible type | ||
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| np.around(a, decimals=1.0) # E: No overload variant of "around" matches argument type | ||
| np.around(a, out=type) # E: No overload variant of "around" matches argument type | ||
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| np.mean(a, axis=1.0) # E: No overload variant of "mean" matches argument type | ||
| np.mean(a, out=False) # E: No overload variant of "mean" matches argument type | ||
| np.mean(a, keepdims=1.0) # E: No overload variant of "mean" matches argument type | ||
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| np.std(a, axis=1.0) # E: No overload variant of "std" matches argument type | ||
| np.std(a, out=False) # E: No overload variant of "std" matches argument type | ||
| np.std(a, ddof='test') # E: No overload variant of "std" matches argument type | ||
| np.std(a, keepdims=1.0) # E: No overload variant of "std" matches argument type | ||
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| np.var(a, axis=1.0) # E: No overload variant of "var" matches argument type | ||
| np.var(a, out=False) # E: No overload variant of "var" matches argument type | ||
| np.var(a, ddof='test') # E: No overload variant of "var" matches argument type | ||
| np.var(a, keepdims=1.0) # E: No overload variant of "var" matches argument type | ||
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