8000 TYP: Type ``np.ma.getmask`` by MarcoGorelli · Pull Request #28832 · numpy/numpy · GitHub
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TYP: Type np.ma.getmask #28832

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Merged
merged 3 commits into from
Apr 26, 2025
Merged

TYP: Type np.ma.getmask #28832

merged 3 commits into from
Apr 26, 2025

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MarcoGorelli
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@MarcoGorelli MarcoGorelli commented Apr 26, 2025 •
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I've tried specialising this further with:

@overload
def getmask(a: np.ndarray[_ShapeT_co, _DTypeT_co]) -> np.ndarray[_ShapeT_co, dtype[bool_]] | bool_: ...
@overload
def getmask(a: ArrayLike) -> bool_: ...

but then the tests fail with

E   AssertionError: Reveal mismatch at line 286
    
    error:
    Expression is of type "Any", not
    "ndarray[tuple[int, ...], dtype[numpy.bool[builtins.bool]]] | numpy.bool[builtins.bool]"
     [assert-type]
        assert_type(np.ma.getmask(MAR_1d), NDArray[np.bool] | np.bool)

for

assert_type(np.ma.getmask(MAR_1d), NDArray[np.bool] | np.bool)

@jorenham jorenham self-requested a review April 26, 2025 14:26
@MarcoGorelli MarcoGorelli marked this pull request as ready for review April 26, 2025 14:56
@jorenham jorenham mentioned this pull request Apr 26, 2025
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@jorenham jorenham added the component: numpy.ma masked arrays label Apr 26, 2025
@jorenham
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I've tried specialising this further with:

@overload
def getmask(a: np.ndarray[_ShapeT_co, _DTypeT_co]) -> np.ndarray[_ShapeT_co, dtype[bool_]] | bool_: ...
@overload
def getmask(a: ArrayLike) -> bool_: ...

The _DTypeT_co seems redundant; maybe removing it helps?
These overloads are also overlapping in an incompatible way now, because ArrayLike is broader than np.ndarray, but the bool_ it returns is narrower than the return type of the other overload.

jorenham
jorenham reviewed Apr 26, 2025
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@MarcoGorelli
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ah right, thanks! now it passes (except one which PyRight detects accurates but MyPy doesn't, similar to in another issue)

@jorenham
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except one which PyRight detects accurates but MyPy doesn't

yea... mypy can be a bit "special" in some situations 😅

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needs a rebase

@jorenham jorenham merged commit 1923b38 into numpy:main Apr 26, 2025
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Thanks Marco

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