def decompose(self):
min_scale = int(2**self.config.get("min_scale"))
max_scale = int(2**self.config.get("max_scale"))
step = self.config.get("dog_step")
angle = self.config.get("dog_angle")
ellipticity = self.config.get("dog_ellipticity")
if max_scale is None:
max_scale = min(self.img.data.shape) / 4
if angle is 'beam' and self.img.has_beam():
angle = self.img.get_beam().angle
widths = np.arange(min_scale, max_scale, step)
scales = wtutils.pyramiddec(
self.img.data,
widths=widths,
angle=angle,
ellipticity=ellipticity,
boundary="symm")
scales = [nputils.resize_like(s, self.img.data) for s in scales]
scales_noises = wtutils.dec_noise_factor(
wtutils.pyramiddec,
self.bg,
widths=widths,
angle=angle,
ellipticity=ellipticity,
beam=self.img.get_beam())
return zip(scales, scales_noises, widths)
def decompose(self, wavelet_fct=None, img=None, bg=None, min_scale=None, max_scale=None):
if wavelet_fct is None:
wavelet_fct = self.config.get("wd_wavelet")
if img is None:
img = self.img
if bg is None:
bg = self.bg
if min_scale is None:
min_scale = self.config.get("min_scale")
if max_scale is None:
max_scale = self.config.get("max_scale")
wt_dec = self.config.get("dec")
if max_scale is None:
max_scale = wavelets.get_wavelet(wavelet_fct).get_max_level(img.data)
scales = wtutils.wavedec(img.data, wavelet_fct, max_scale, dec=wt_dec)
self.approx = scales[-1]
scales = scales[min_scale:-1]
scales = [nputils.resize_like(s, img.data) for s in scales]
scales_noise = wtutils.wave_noise_factor(bg, wavelet_fct, max_scale, wt_dec, beam=img.get_beam())
scales_noise = scales_noise[min_scale:]
if wavelet_fct in ['b3', 'triangle2'] :
scales_width = [max(1.5, 3 * min(1, j) * pow(2, max(0, j - 1))) for j in range(min_scale, max_scale)]
else:
scales_width = [max(1, 2 * min(1, j) * pow(2, max(0, j - 1))) for j in range(min_scale, max_scale)]
return zip(scales, scales_noise, scales_width)
def decompose(self):
min_scale = int(2**self.config.get("min_scale"))
max_scale = int(2**self.config.get("max_scale"))
step = self.config.get("dog_step")
angle = self.config.get("dog_angle")
ellipticity = self.config.get("dog_ellipticity")
if max_scale is None:
max_scale = min(self.img.data.shape) / 4
if angle is 'beam' and self.img.has_beam():
angle = self.img.get_beam().angle
widths = np.arange(min_scale, max_scale, step)
scales = wtutils.pyramiddec(self.img.data,
widths=widths,
angle=angle,
ellipticity=ellipticity,
boundary="symm")
scales = [nputils.resize_like(s, self.img.data) for s in scales]
scales_noises = wtutils.dec_noise_factor(wtutils.pyramiddec,
self.bg,
widths=widths,
angle=angle,
ellipticity=ellipticity,
beam=self.img.get_beam())
return zip(scales, scales_noises, widths)
def after_update(self, result):
titles = ["Original"] + \
["Scale %i" % k for k in range(1, int(self.scale.get()) + 1)] + ["Residu"]
plots = [self.x] + list(result)
self.view.figure.clear()
ax = self.view.figure.subplots()
for plot, title in zip(plots, titles):
plot = nputils.resize_like(plot, self.x)
ax.plot(plot, label=title)
ax.legend()
self.view.draw()
def after_update(self, result):
titles = ["Original"] + \
["Scale %i" % k for k in range(1, int(self.scale.get()) + 1)] + ["Residu"]
plots = [self.x] + list(result)
self.view.figure.clear()
ax = self.view.figure.subplots()
for plot, title in zip(plots, titles):
plot = nputils.resize_like(plot, self.x)
ax.plot(plot, label=title)
ax.legend()
self.view.draw()
def decompose(self,
wavelet_fct=None,
img=None,
bg=None,
min_scale=None,
max_scale=None):
if wavelet_fct is None:
wavelet_fct = self.config.get("wd_wavelet")
if img is None:
img = self.img
if bg is None:
bg = self.bg
if min_scale is None:
min_scale = self.config.get("min_scale")
if max_scale is None:
max_scale = self.config.get("max_scale")
wt_dec = self.config.get("dec")
if max_scale is None:
max_scale = wavelets.get_wavelet(wavelet_fct).get_max_level(
img.data)
scales = wtutils.wavedec(img.data, wavelet_fct, max_scale, dec=wt_dec)
self.approx = scales[-1]
scales = scales[min_scale:-1]
scales = [nputils.resize_like(s, img.data) for s in scales]
scales_noise = wtutils.wave_noise_factor(bg,
wavelet_fct,
max_scale,
wt_dec,
beam=img.get_beam())
scales_noise = scales_noise[min_scale:]
if wavelet_fct in ['b3', 'triangle2']:
scales_width = [
max(1.5, 3 * min(1, j) * pow(2, max(0, j - 1)))
for j in range(min_scale, max_scale)
]
else:
scales_width = [
max(1, 2 * min(1, j) * pow(2, max(0, j - 1)))
for j in range(min_scale, max_scale)
]
return zip(scales, scales_noise, scales_width)
