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Difference-of-Gaussians (dog) plugin

patchworks.plugins.dog.dog_label_fn(low_sigma: float | tuple[float, ...], high_sigma: float | tuple[float, ...], threshold: float, *, use_gpu: bool = False, decon_kwargs: dict[str, Any] | None = None) -> Callable[[np.ndarray], np.ndarray]

Return a ready-to-use DoG labeler for tile_process.

Parameters:

Name Type Description Default
low_sigma float | tuple[float, ...]

Gaussian sigmas (pixels) for the narrow/wide blur. dog = blur(low_sigma) - blur(high_sigma).

 required
high_sigma float | tuple[float, ...]

Gaussian sigmas (pixels) for the narrow/wide blur. dog = blur(low_sigma) - blur(high_sigma).

 required
threshold float

Binary threshold applied to the DoG image (dog > threshold).

 required
use_gpu bool

Run gaussian_filter + label on GPU via cupy/cupyx instead of scipy. Independent of decon_kwargs — pycudadecon always needs a CUDA GPU regardless of this flag (it takes/returns plain NumPy), and this flag only picks the backend for the blur/label steps that follow.

 False
decon_kwargs dict[str, Any] | None

If given, each tile is first deconvolved via pycudadecon.decon(block, **decon_kwargs) before the DoG step. None (default) skips deconvolution. pycudadecon is CUDA-only, so a SLURM job running this needs a GPU allocated regardless of use_gpu above. Widen tile_process's overlap to cover the PSF support when deconvolving, so edge tiles don't get truncated context.

 None

Returns:

Type Description
Callable[[ndarray], ndarray]

Picklable function ready for tile_process.
Source code in src/patchworks/plugins/dog.py 
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def dog_label_fn(
    low_sigma: float | tuple[float, ...],
    high_sigma: float | tuple[float, ...],
    threshold: float,
    *,
    use_gpu: bool = False,
    decon_kwargs: dict[str, Any] | None = None,
) -> Callable[[np.ndarray], np.ndarray]:
    """Return a ready-to-use DoG labeler for ``tile_process``.

    Parameters
    ----------
    low_sigma, high_sigma:
        Gaussian sigmas (pixels) for the narrow/wide blur.
        ``dog = blur(low_sigma) - blur(high_sigma)``.
    threshold:
        Binary threshold applied to the DoG image (``dog > threshold``).
    use_gpu:
        Run gaussian_filter + label on GPU via cupy/cupyx instead of scipy.
        Independent of ``decon_kwargs`` — pycudadecon always needs a CUDA
        GPU regardless of this flag (it takes/returns plain NumPy), and this
        flag only picks the backend for the blur/label steps that follow.
    decon_kwargs:
        If given, each tile is first deconvolved via
        ``pycudadecon.decon(block, **decon_kwargs)`` before the DoG step.
        ``None`` (default) skips deconvolution. pycudadecon is CUDA-only, so
        a SLURM job running this needs a GPU allocated regardless of
        ``use_gpu`` above. Widen ``tile_process``'s ``overlap`` to cover the
        PSF support when deconvolving, so edge tiles don't get truncated
        context.

    Returns
    -------
    Callable[[ndarray], ndarray]
        Picklable function ready for ``tile_process``.
    """
    if use_gpu:
        _require_cupy()
    if decon_kwargs is not None:
        _require_pycudadecon()
    cfg = {
        "low_sigma": low_sigma,
        "high_sigma": high_sigma,
        "threshold": threshold,
        "use_gpu": use_gpu,
        "decon_kwargs": decon_kwargs,
    }
    return partial(_run, dog_dict=cfg)