Skip to content

resampy: more accurate and dynamic TypeVar #15926

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
hunterhogan wants to merge 1 commit into
base: main
Choose a base branch
from
Open

resampy: more accurate and dynamic TypeVar #15926

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
71 changes: 63 additions & 8 deletions stubs/resampy/resampy/core.pyi
View file
Open in desktop
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])

@donbarbos donbarbos Jul 23, 2026

Copy link
Copy Markdown
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

The comment is still relevant

Suggested change
_Floating = TypeVar("_Floating", bound=np.floating[Any])
# np.floating[Any] because precision is not important
_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]]],

@donbarbos donbarbos Jul 23, 2026

Copy link
Copy Markdown
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

To avoid writing an explanatory comment for Any each time, you can avoid using Any since it is set to default

Suggested change
x: np.ndarray[_Shape, np.dtype[np.integer[Any]]],
x: np.ndarray[_Shape, np.dtype[np.integer]],

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]]: ...
Comment on lines +38 to +50

@donbarbos donbarbos Jul 23, 2026

Copy link
Copy Markdown
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I'm not sure we need the third overload here, since it seems to be already covered by the first two.

Suggested change
@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]]],

@donbarbos donbarbos Jul 23, 2026

Copy link
Copy Markdown
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

same here

Suggested change
x: np.ndarray[_Shape, np.dtype[np.integer[Any]]],
x: np.ndarray[_Shape, np.dtype[np.integer]],

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]]: ...
Comment on lines +78 to +90

@donbarbos donbarbos Jul 23, 2026

Copy link
Copy Markdown
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

same here

Suggested change
@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]]: ...