Add isfinite, isinf, isscalar, diagonal, allclose
#213
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tmke8
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tmke8
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Thanks! I like the approach with the ufunc; we used it for np.mean or np.std before and I always thought that this should be expanded. Also, great tests!
See some comments below.
numpy-stubs/__init__.pyi
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| @overload | ||
| def __radd__(self, value: _DType) -> ndarray[_DType]: ... | ||
| @overload | ||
| def __radd__(self, value: float) -> ndarray[_DType]: ... |
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I think the return type should be float64?
| def __radd__(self, value: float) -> ndarray[_DType]: ... | |
| def __radd__(self, value: float) -> ndarray[float64]: ... |
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Hmm, I think that not necessarily... if _DType is np.int64, the return type will indeed be np.float64, but this would not be true for complex types, for example.
Update: I've added complex64 and complex128 support, and I've incremented the test to check float + int and float + complex. This is returning _DType2 now.
| # int, an int, and a callable, but there's no way to express | ||
| # that. | ||
| extobj: List[Union[int, Callable]] = ..., | ||
| ) -> Any: ... |
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I don't love this Any return type. Is it possible to do an overload like we had before?
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I took this from NumPy stubs, I guess the return type from ufuncs is too broad, check this issue: numpy/numpy#17805
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| def test_sum_scalar_before() -> None: | ||
| x = 273.15 + np.array([-0.1e2, -0.77e1]) | ||
| assert isinstance(x, np.ndarray) |
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Can we assert the type of the ndarray?
…ove `isnan` support in NumPy stubs also add support for scalar + array fixes wearepal#209
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and improve
isnansupport in NumPy stubs. Also add support for scalar + array.fixes #209