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resampy: more accurate and dynamic TypeVar #15926

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71 changes: 63 additions & 8 deletions stubs/resampy/resampy/core.pyi
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Original file line number Diff line number Diff line change
@@ -1,16 +1,17 @@
from collections.abc import Callable
from typing import Any, TypeAlias, TypeVar
from typing import Any, TypeAlias, TypeVar, overload

import numpy as np

__all__ = ["resample", "resample_nu"]

# 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])
_Shape = TypeVar("_Shape", bound=tuple[int, ...])
_FilterType: TypeAlias = str | Callable[[int], np.ndarray[tuple[int], np.dtype[np.float64]]]

@overload
def resample(
x: _FloatArray,
x: np.ndarray[_Shape, np.dtype[np.integer[Any]]],
sr_orig: float,
sr_new: float,
axis: int = -1,
Expand All @@ -20,16 +21,70 @@ def resample(
num_zeros: int = 64,
precision: int = 9,
rolloff: float = 0.945,
) -> _FloatArray: ...
) -> np.ndarray[_Shape, np.dtype[np.float32]]: ...
@overload
def resample(
x: 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[_Floating]]: ...
@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]]: ...

@overload
def resample_nu(
x: np.ndarray[_Shape, np.dtype[np.integer[Any]]],
sr_orig: float,
t_out: np.ndarray[_Shape, np.dtype[np.float32]],
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]]: ...
@overload
def resample_nu(
x: np.ndarray[_Shape, np.dtype[_Floating]],
sr_orig: float,
t_out: 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,
) -> np.ndarray[_Shape, np.dtype[_Floating]]: ...
@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]]: ...