Add initial implementation of resampy library #15341
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According to mypy_primer, this change has no effect on the checked open source code. 🤖🎉 |
donbarbos
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Jan 30, 2026
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| version = "0.4.*" | |||
| upstream_repository = "https://github.com/bmcfee/resampy" | |||
| requires = ["numba", "numpy"] | |||
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numpyshipspy.typedfile since version1.20numbais not needed here, we can get rid of it (see below)
Suggested change
| requires = ["numba", "numpy"] | |
| # Requires a version of numpy with a `py.typed` file | |
| requires = ["numpy>=1.20"] |
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| short_version: str | ||
| version: str |
Contributor
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Let's add Final here:
Suggested change
| short_version: str | |
| version: str | |
| from typing import Final | |
| short_version: Final[str] | |
| version: Final[str] |
| @@ -0,0 +1,2 @@ | |||
| from . import filters as filters | |||
Contributor
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Let's also reflect the __version__ since it is also imported:
Suggested change
| from . import filters as filters | |
| from .version import version as __version__ | |
| from . import filters as filters |
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| FILTER_CACHE: dict[str, tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]] = {} | ||
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|
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| # List of filter functions available | ||
| FILTER_FUNCTIONS: list[str] = ["sinc_window"] |
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There is no need to specify a default value for variables:
Suggested change
| FILTER_CACHE: dict[str, tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]] = {} | |
| # List of filter functions available | |
| FILTER_FUNCTIONS: list[str] = ["sinc_window"] | |
| FILTER_CACHE: dict[str, tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]] | |
| # List of filter functions available | |
| FILTER_FUNCTIONS: list[str] |
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| import numba | ||
| import numpy as np | ||
| from numba import guvectorize |
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Suggested change
| import numba | |
| import numpy as np | |
| from numba import guvectorize | |
| import numpy as np |
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| @guvectorize( | ||
| ( | ||
| numba.float32[:, :, :], | ||
| numba.float32[:, :], | ||
| numba.float32[:], | ||
| numba.float32[:], | ||
| numba.int32, | ||
| numba.float32, | ||
| numba.float32[:, :], | ||
| ), | ||
| "(n),(m),(p),(p),(),()->(m)", | ||
| nopython=True, | ||
| ) | ||
| def resample_f_p( | ||
| x: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | ||
| t_out: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | ||
| interp_win: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | ||
| interp_delta: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | ||
| num_table: int, | ||
| scale: float, | ||
| y: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | ||
| ) -> None: ... | ||
| @guvectorize( | ||
| ( | ||
| numba.float32[:, :, :], | ||
| numba.float32[:, :], | ||
| numba.float32[:], | ||
| numba.float32[:], | ||
| numba.int32, | ||
| numba.float32, | ||
| numba.float32[:, :], | ||
| ), | ||
| "(n),(m),(p),(p),(),()->(m)", | ||
| nopython=True, | ||
| ) | ||
| def resample_f_s( | ||
| x: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | ||
| t_out: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | ||
| interp_win: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | ||
| interp_delta: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | ||
| num_table: int, | ||
| scale: float, | ||
| y: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | ||
| ) -> None: ... |
Contributor
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If a decorator does not affect the function signature, there is no need to use it. In cases where the runtime signature differs from the stub signature, we can either ignore it or add it manually as needed.
Suggested change
| @guvectorize( | |
| ( | |
| numba.float32[:, :, :], | |
| numba.float32[:, :], | |
| numba.float32[:], | |
| numba.float32[:], | |
| numba.int32, | |
| numba.float32, | |
| numba.float32[:, :], | |
| ), | |
| "(n),(m),(p),(p),(),()->(m)", | |
| nopython=True, | |
| ) | |
| def resample_f_p( | |
| x: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| t_out: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| interp_win: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| interp_delta: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| num_table: int, | |
| scale: float, | |
| y: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| ) -> None: ... | |
| @guvectorize( | |
| ( | |
| numba.float32[:, :, :], | |
| numba.float32[:, :], | |
| numba.float32[:], | |
| numba.float32[:], | |
| numba.int32, | |
| numba.float32, | |
| numba.float32[:, :], | |
| ), | |
| "(n),(m),(p),(p),(),()->(m)", | |
| nopython=True, | |
| ) | |
| def resample_f_s( | |
| x: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| t_out: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| interp_win: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| interp_delta: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| num_table: int, | |
| scale: float, | |
| y: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| ) -> None: ... | |
| def resample_f_p( | |
| x: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| t_out: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| interp_win: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| interp_delta: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| num_table: int, | |
| scale: float, | |
| y: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| ) -> None: ... | |
| def resample_f_s( | |
| x: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| t_out: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| interp_win: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| interp_delta: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| num_table: int, | |
| scale: float, | |
| y: np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], | |
| ) -> None: ... |
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Introduce core functionality and metadata for the resampy library, including initial stubs for resampling methods and filter functions. Update metadata to specify dependencies.
See also #15338.