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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
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3 changes: 3 additions & 0 deletions stubs/resampy/METADATA.toml
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version = "0.4.*"
upstream_repository = "https://github.com/bmcfee/resampy"
requires = ["numba", "numpy"]
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@donbarbos donbarbos Jan 30, 2026

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  1. numpy ships py.typed file since version 1.20
  2. numba is 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"]

2 changes: 2 additions & 0 deletions stubs/resampy/resampy/__init__.pyi
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from . import filters as filters
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@donbarbos donbarbos Jan 30, 2026

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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

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]]

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,
) -> _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]] = {}

# List of filter functions available
FILTER_FUNCTIONS: list[str] = ["sinc_window"]
Comment on lines +9 to +12
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@donbarbos donbarbos Jan 30, 2026

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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]


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: ...
66 changes: 66 additions & 0 deletions stubs/resampy/resampy/interpn.pyi
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from typing import Any

import numba

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Stub file not found for "numba" (reportMissingTypeStubs)
import numpy as np
from numba import guvectorize

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@donbarbos donbarbos Jan 30, 2026

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Suggested change
import numba
import numpy as np
from numba import guvectorize
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: ...

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

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

@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: ...
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@donbarbos donbarbos Jan 30, 2026

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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: ...

2 changes: 2 additions & 0 deletions stubs/resampy/resampy/version.pyi
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short_version: str
version: str
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@donbarbos donbarbos Jan 30, 2026

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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]

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