resampy: more accurate and dynamic TypeVar - #15926
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According to mypy_primer, this change has no effect on the checked open source code. 🤖🎉 |
donbarbos
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Jul 23, 2026
donbarbos
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Thanks! Just a few comments.
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| # np.floating[Any] because precision is not important | ||
| _FloatArray = TypeVar("_FloatArray", bound=np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]]) | ||
| _Floating = TypeVar("_Floating", bound=np.floating[Any]) |
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The comment is still relevant
Suggested change
| _Floating = TypeVar("_Floating", bound=np.floating[Any]) | |
| # np.floating[Any] because precision is not important | |
| _Floating = TypeVar("_Floating", bound=np.floating[Any]) |
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| @overload | ||
| def resample( | ||
| x: np.ndarray[_Shape, np.dtype[np.integer[Any]]] | np.ndarray[_Shape, np.dtype[_Floating]], | ||
| sr_orig: float, | ||
| sr_new: float, | ||
| axis: int = -1, | ||
| filter: _FilterType = "kaiser_best", | ||
| parallel: bool = False, | ||
| *, | ||
| num_zeros: int = 64, | ||
| precision: int = 9, | ||
| rolloff: float = 0.945, | ||
| ) -> np.ndarray[_Shape, np.dtype[np.float32]] | np.ndarray[_Shape, np.dtype[_Floating]]: ... |
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I'm not sure we need the third overload here, since it seems to be already covered by the first two.
Suggested change
| @overload | |
| def resample( | |
| x: np.ndarray[_Shape, np.dtype[np.integer[Any]]] | np.ndarray[_Shape, np.dtype[_Floating]], | |
| sr_orig: float, | |
| sr_new: float, | |
| axis: int = -1, | |
| filter: _FilterType = "kaiser_best", | |
| parallel: bool = False, | |
| *, | |
| num_zeros: int = 64, | |
| precision: int = 9, | |
| rolloff: float = 0.945, | |
| ) -> np.ndarray[_Shape, np.dtype[np.float32]] | np.ndarray[_Shape, np.dtype[_Floating]]: ... |
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| @overload | ||
| def resample_nu( | ||
| x: _FloatArray, | ||
| x: np.ndarray[_Shape, np.dtype[np.integer[Any]]] | np.ndarray[_Shape, np.dtype[_Floating]], | ||
| sr_orig: float, | ||
| t_out: _FloatArray, | ||
| t_out: np.ndarray[_Shape, np.dtype[np.float32]] | np.ndarray[_Shape, np.dtype[_Floating]], | ||
| axis: int = -1, | ||
| filter: _FilterType = "kaiser_best", | ||
| parallel: bool = False, | ||
| *, | ||
| num_zeros: int = 64, | ||
| precision: int = 9, | ||
| rolloff: float = 0.945, | ||
| ) -> _FloatArray: ... | ||
| ) -> np.ndarray[_Shape, np.dtype[np.float32]] | np.ndarray[_Shape, np.dtype[_Floating]]: ... |
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same here
Suggested change
| @overload | |
| def resample_nu( | |
| x: _FloatArray, | |
| x: np.ndarray[_Shape, np.dtype[np.integer[Any]]] | np.ndarray[_Shape, np.dtype[_Floating]], | |
| sr_orig: float, | |
| t_out: _FloatArray, | |
| t_out: np.ndarray[_Shape, np.dtype[np.float32]] | np.ndarray[_Shape, np.dtype[_Floating]], | |
| axis: int = -1, | |
| filter: _FilterType = "kaiser_best", | |
| parallel: bool = False, | |
| *, | |
| num_zeros: int = 64, | |
| precision: int = 9, | |
| rolloff: float = 0.945, | |
| ) -> _FloatArray: ... | |
| ) -> np.ndarray[_Shape, np.dtype[np.float32]] | np.ndarray[_Shape, np.dtype[_Floating]]: ... |
| @overload | ||
| def resample( | ||
| x: _FloatArray, | ||
| x: np.ndarray[_Shape, np.dtype[np.integer[Any]]], |
Contributor
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To avoid writing an explanatory comment for Any each time, you can avoid using Any since it is set to default
Suggested change
| x: np.ndarray[_Shape, np.dtype[np.integer[Any]]], | |
| x: np.ndarray[_Shape, np.dtype[np.integer]], |
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| @overload | ||
| def resample_nu( | ||
| x: np.ndarray[_Shape, np.dtype[np.integer[Any]]], |
Contributor
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same here
Suggested change
| x: np.ndarray[_Shape, np.dtype[np.integer[Any]]], | |
| x: np.ndarray[_Shape, np.dtype[np.integer]], |
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If integer, converted to float32. If floating, then it copies the dtype.