41ebf6d0df65dc0b68094515b73aafe153acd9cb,trackpy/preprocessing.py,,bandpass,#Any#Any#Any#Any#Any#,11

Before Change

    axes = range(image.ndim)
    sizes = [x*2+1 for x in llong]
    boxcar = np.asarray(image)
    for (axis, size) in zip(axes, sizes):
        boxcar = uniform_filter1d(boxcar, size, axis, **settings)
    gaussian = gaussian_filter(image, lshort, truncate=truncate, **settings)
    result = gaussian - boxcar
    return np.where(result > threshold, result, 0)

After Change

            threshold = 1/256.
    boxcar = image.copy()
    result = np.array(image, dtype=np.float)
    for axis, (sigma, smoothing) in enumerate(zip(lshort, llong)):
        if smoothing > 1:
            uniform_filter1d(boxcar, 2*smoothing+1, axis, output=boxcar,
                             mode="nearest", cval=0)
        if sigma > 0:
            correlate1d(result, gaussian_kernel(sigma, truncate), axis,
                        output=result, mode="constant", cval=0.0)
    result -= boxcar
    return np.where(result > threshold, result, 0)

Instances