def dogdec(signal, widths=None, angle=0, ellipticity=1, boundary="symm"):
if widths is None:
widths = np.arange(1, min(signal.shape) / 4)
beams = [imgutils.GaussianBeam(ellipticity * w, w, bpa=angle) for w in widths]
filtered = [b.convolve(signal, boundary=boundary) for b in beams]
res = [(el[0] - el[-1]) for el in nputils.nwise(filtered, 2)]
for s in res:
s[s <= 0] = 0
res = [s - b2.convolve(s, boundary=boundary) for (s, (b1, b2)) in zip(res, nputils.nwise(beams, 2))]
# res = [b1.convolve(s, boundary=boundary) - b2.convolve(s, boundary=boundary) for (s, (b1, b2)) in zip(res, nputils.nwise(beams, 2))]
return res
def dogdec(signal, widths=None, angle=0, ellipticity=1, boundary="symm"):
if widths is None:
widths = np.arange(1, min(signal.shape) / 4)
beams = [
imgutils.GaussianBeam(ellipticity * w, w, bpa=angle) for w in widths
]
filtered = [b.convolve(signal, boundary=boundary) for b in beams]
res = [(el[0] - el[-1]) for el in nputils.nwise(filtered, 2)]
for s in res:
s[s <= 0] = 0
res = [
s - b2.convolve(s, boundary=boundary)
for (s, (b1, b2)) in zip(res, nputils.nwise(beams, 2))
]
# res = [b1.convolve(s, boundary=boundary) - b2.convolve(s, boundary=boundary) for (s, (b1, b2)) in zip(res, nputils.nwise(beams, 2))]
return res
def pyramiddec(signal, widths=None, angle=0, ellipticity=1, boundary="symm"):
if widths is None:
widths = np.arange(1, min(signal.shape) / 4)
beams = [imgutils.GaussianBeam(ellipticity * w, w, angle=angle) for w in widths]
min_scale = beams[0].convolve(signal, boundary=boundary) - beams[1].convolve(signal, boundary=boundary)
filtered_min = [b.convolve(min_scale, boundary=boundary) for b in beams]
filtered_all = [b.convolve(signal, boundary=boundary) for b in beams]
dog = [(el[0] - el[-1]) for el in nputils.nwise(filtered_all, 2)]
return [v - k for k, v in zip(filtered_min, dog)]
def pyramiddec(signal, widths=None, angle=0, ellipticity=1, boundary="symm"):
if widths is None:
widths = np.arange(1, min(signal.shape) / 4)
beams = [
imgutils.GaussianBeam(ellipticity * w, w, angle=angle) for w in widths
]
min_scale = beams[0].convolve(
signal, boundary=boundary) - beams[1].convolve(signal,
boundary=boundary)
filtered_min = [b.convolve(min_scale, boundary=boundary) for b in beams]
filtered_all = [b.convolve(signal, boundary=boundary) for b in beams]
dog = [(el[0] - el[-1]) for el in nputils.nwise(filtered_all, 2)]
return [v - k for k, v in zip(filtered_min, dog)]
