def publish(self, pub):
diffeo = self.summarize()
pub.array_as_image('mle', diffeomorphism_to_rgb(diffeo.d))
pub.array_as_image('angle', diffeo_to_rgb_angle(diffeo.d))
pub.array_as_image('norm', diffeo_to_rgb_norm(diffeo.d, max_value=10))
pub.array_as_image('curv', diffeo_to_rgb_curv(diffeo.d))
pub.array_as_image('variance', diffeo.variance, filter='scale')
pub.text('num_samples', self.num_samples)
pub.text('statistics', diffeo_text_stats(diffeo.d))
pub.array_as_image('legend', angle_legend((50, 50)))
n = 20
M = None
for i in range(n): # @UnusedVariable
c = self.flattening.random_coords()
Mc = self.get_similarity(c)
if M is None:
M = np.zeros(Mc.shape)
M.fill(np.nan)
ok = np.isfinite(Mc)
Mmax = np.nanmax(Mc)
if Mmax < 0:
Mc = -Mc
Mmax = -Mmax
if Mmax > 0:
M[ok] = Mc[ok] / Mmax
pub.array_as_image('coords', M, filter='scale')
if self.last_y0 is not None:
y0 = self.last_y0
y1 = self.last_y1
none = np.logical_and(y0 == 0, y1 == 0)
x = y0 - y1
x[none] = np.nan
pub.array_as_image('motion', x, filter='posneg')
def publish(self, pub):
diffeo = self.summarize()
pub.array_as_image('mle', diffeomorphism_to_rgb(diffeo.d))
pub.array_as_image('angle', diffeo_to_rgb_angle(diffeo.d))
pub.array_as_image('norm', diffeo_to_rgb_norm(diffeo.d, max_value=10))
pub.array_as_image('curv', diffeo_to_rgb_curv(diffeo.d))
pub.array_as_image('variance', diffeo.variance, filter='scale')
pub.text('num_samples', self.num_samples)
pub.text('statistics', diffeo_text_stats(diffeo.d))
pub.array_as_image('legend', angle_legend((50, 50)))
n = 20
M = None
for i in range(n): # @UnusedVariable
c = self.flattening.random_coords()
Mc = self.get_similarity(c)
if M is None:
M = np.zeros(Mc.shape)
M.fill(np.nan)
ok = np.isfinite(Mc)
Mmax = np.nanmax(Mc)
if Mmax < 0:
Mc = -Mc
Mmax = -Mmax
if Mmax > 0:
M[ok] = Mc[ok] / Mmax
pub.array_as_image('coords', M, filter='scale')
if self.last_y0 is not None:
y0 = self.last_y0
y1 = self.last_y1
none = np.logical_and(y0 == 0, y1 == 0)
x = y0 - y1
x[none] = np.nan
pub.array_as_image('motion', x, filter='posneg')
def get_rgb_norm(self):
from diffeo2d import (diffeo_to_rgb_norm)
return diffeo_to_rgb_norm(self.get_discretized_diffeo())
def get_rgb_norm(self):
from diffeo2d import (diffeo_to_rgb_norm)
return diffeo_to_rgb_norm(self.get_discretized_diffeo())
