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Add initial implementation of resampy library #15341

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0fb9f63
Add initial stubs for resampy including core functionality and metadata
hunterhogan Jan 29, 2026
547b4c4
Merge branch 'main' of https://github.com/python/typeshed into resampy
hunterhogan Jan 29, 2026
c910ff9
METADATA: add requires
hunterhogan Jan 29, 2026
4f8383c
Update stubs/resampy/METADATA.toml
hunterhogan Jan 31, 2026
3b0c8f8
Update stubs/resampy/resampy/version.pyi
hunterhogan Jan 31, 2026
2801ee4
Update stubs/resampy/resampy/__init__.pyi
hunterhogan Jan 31, 2026
541d8e8
Update stubs/resampy/resampy/filters.pyi
hunterhogan Jan 31, 2026
52b6063
Update stubs/resampy/resampy/interpn.pyi
hunterhogan Jan 31, 2026
a6ecd3c
Update stubs/resampy/resampy/interpn.pyi
hunterhogan Jan 31, 2026
d0405af
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jan 31, 2026
e1c3fd0
Create stubtest_allowlist.txt
hunterhogan Jan 31, 2026
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2 changes: 2 additions & 0 deletions stubs/resampy/@tests/stubtest_allowlist.txt
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resampy.interpn.resample_f_p
resampy.interpn.resample_f_s
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@donbarbos donbarbos Jan 31, 2026

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Could you add an explanatory comment to these entries?
I don’t claim to fully understand how this works in practice, but from what I’ve seen in the calls to these functions within the library, it seems the following:

Suggested change
resampy.interpn.resample_f_p
resampy.interpn.resample_f_s
# @numba.guvectorize creates np.ufunc objects, but these functions still have the same signatures
resampy.interpn.resample_f_p
resampy.interpn.resample_f_s

4 changes: 4 additions & 0 deletions stubs/resampy/METADATA.toml
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version = "0.4.*"
upstream_repository = "https://github.com/bmcfee/resampy"
# Requires a version of numpy with a `py.typed` file
requires = ["numpy>=1.20"]
2 changes: 2 additions & 0 deletions stubs/resampy/resampy/__init__.pyi
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from . import filters as filters
from .core import *
29 changes: 29 additions & 0 deletions stubs/resampy/resampy/core.pyi
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from collections.abc import Callable
from typing import Any
from typing_extensions import TypeAlias, TypeVar

import numpy as np

__all__ = ["resample", "resample_nu"]

_FloatArray = TypeVar("_FloatArray", bound=np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]])
_FilterType: TypeAlias = str | Callable[..., tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]]
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@donbarbos donbarbos Jan 31, 2026

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The type passed to generic np.floating is responsible for precision, which by default already has the value Any (and this is rarely necessary), so you can get rid of all these Any:

Suggested change
_FloatArray = TypeVar("_FloatArray", bound=np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]])
_FilterType: TypeAlias = str | Callable[..., tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]]
_FloatArray = TypeVar("_FloatArray", bound=np.ndarray[tuple[int, ...], np.dtype[np.floating]])
_FilterType: TypeAlias = str | Callable[..., tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating]], int, float]]


def resample(
x: _FloatArray,
sr_orig: float,
sr_new: float,
axis: int = -1,
filter: _FilterType = "kaiser_best",
parallel: bool = False,
**kwargs: Any,
) -> _FloatArray: ...
def resample_nu(
x: _FloatArray,
sr_orig: float,
t_out: _FloatArray,
axis: int = -1,
filter: _FilterType = "kaiser_best",
parallel: bool = False,
**kwargs: Any,
Comment on lines +19 to +28
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@donbarbos donbarbos Jan 31, 2026

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typeshed expects that Any types have comment with explanation on why Any was used or what types are allowed, especially important for **kwargs: Any.
You can leave them unannotated, but then be sure to add resampy path(s) to the exclude list:

Suggested change
**kwargs: Any,
) -> _FloatArray: ...
def resample_nu(
x: _FloatArray,
sr_orig: float,
t_out: _FloatArray,
axis: int = -1,
filter: _FilterType = "kaiser_best",
parallel: bool = False,
**kwargs: Any,
**kwargs,
) -> _FloatArray: ...
def resample_nu(
x: _FloatArray,
sr_orig: float,
t_out: _FloatArray,
axis: int = -1,
filter: _FilterType = "kaiser_best",
parallel: bool = False,
**kwargs,

) -> _FloatArray: ...
25 changes: 25 additions & 0 deletions stubs/resampy/resampy/filters.pyi
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from collections.abc import Callable
from typing import Any

import numpy as np

__all__ = ["get_filter", "clear_cache", "sinc_window"]

# Dictionary to cache loaded filters
FILTER_CACHE: dict[str, tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]]
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@donbarbos donbarbos Jan 31, 2026

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Suggested change
FILTER_CACHE: dict[str, tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]]
FILTER_CACHE: dict[str, tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating]], int, float]]


# List of filter functions available
FILTER_FUNCTIONS: list[str]

def sinc_window(
num_zeros: int = 64,
precision: int = 9,
window: Callable[..., np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]]] | None = None,
rolloff: float = 0.945,
) -> tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]: ...
def get_filter(
name_or_function: str | Callable[..., tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]],
**kwargs: Any,
) -> tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]: ...
def load_filter(filter_name: str) -> tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]: ...
def clear_cache() -> None: ...
Comment on lines +9 to +25
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@donbarbos donbarbos Jan 31, 2026

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same here:

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]
def sinc_window(
num_zeros: int = 64,
precision: int = 9,
window: Callable[..., np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]]] | None = None,
rolloff: float = 0.945,
) -> tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]: ...
def get_filter(
name_or_function: str | Callable[..., tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]],
**kwargs: Any,
) -> tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]: ...
def load_filter(filter_name: str) -> tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating[Any]]], int, float]: ...
def clear_cache() -> None: ...
FILTER_CACHE: dict[str, tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating]], int, float]]
# List of filter functions available
FILTER_FUNCTIONS: list[str]
def sinc_window(
num_zeros: int = 64,
precision: int = 9,
window: Callable[..., np.ndarray[tuple[int, ...], np.dtype[np.floating]]] | None = None,
rolloff: float = 0.945,
) -> tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating]], int, float]: ...
def get_filter(
name_or_function: str | Callable[..., tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating]], int, float]],
**kwargs,
) -> tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating]], int, float]: ...
def load_filter(filter_name: str) -> tuple[np.ndarray[tuple[int, ...], np.dtype[np.floating]], int, float]: ...
def clear_cache() -> None: ...

38 changes: 38 additions & 0 deletions stubs/resampy/resampy/interpn.pyi
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from typing import Any

import numpy as np

def _resample_loop(
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: ...
Comment on lines +5 to +13
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@donbarbos donbarbos Jan 31, 2026 •
edited
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see above

Suggested change
def _resample_loop(
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_loop(
x: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
t_out: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
interp_win: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
interp_delta: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
num_table: int,
scale: float,
y: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
) -> None: ...


# JIT-compiled parallel version of _resample_loop
_resample_loop_p = ...

# JIT-compiled sequential version of _resample_loop
_resample_loop_s = ...

Comment on lines +14 to +20
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@donbarbos donbarbos Jan 31, 2026

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It looks like internal API, so I suggest removing it, especially if you don't add types to it (this may also apply to _resample_loop)

Suggested change
# JIT-compiled parallel version of _resample_loop
_resample_loop_p = ...
# JIT-compiled sequential version of _resample_loop
_resample_loop_s = ...

or if you are sure about types and consider it necessary, you can specify them something like this:

Suggested change
# JIT-compiled parallel version of _resample_loop
_resample_loop_p = ...
# JIT-compiled sequential version of _resample_loop
_resample_loop_s = ...
_resample_loop_p = _resample_loop
_resample_loop_s = _resample_loop

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: ...
Comment on lines +21 to +38
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@donbarbos donbarbos Jan 31, 2026 •
edited
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see above

Suggested change
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: ...
def resample_f_p(
x: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
t_out: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
interp_win: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
interp_delta: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
num_table: int,
scale: float,
y: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
) -> None: ...
def resample_f_s(
x: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
t_out: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
interp_win: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
interp_delta: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
num_table: int,
scale: float,
y: np.ndarray[tuple[int, ...], np.dtype[np.floating]],
) -> None: ...

4 changes: 4 additions & 0 deletions stubs/resampy/resampy/version.pyi
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from typing import Final

short_version: Final[str]
version: Final[str]