Add initial implementation of resampy library

stubs/resampy/METADATA.toml

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+version = "0.4.*"
+upstream_repository = "https://github.com/bmcfee/resampy"
+requires = ["numba", "numpy"]

stubs/resampy/resampy/__init__.pyi

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+from . import filters as filters
+from .core import *

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: ...

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"]
+
+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: ...

stubs/resampy/resampy/interpn.pyi

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+from typing import Any
+
+import numba
+import numpy as np
+from numba import guvectorize
+
+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: ...

stubs/resampy/resampy/version.pyi

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+short_version: str
+version: str