def plot(self, pylab):
tracks = self.input.tracks
track2color = get_track_colors(tracks)
for id_track, particles in enumerate_id_track(self.input.tracks):
color = track2color[id_track]
bound = particles['bound']
score = particles['score']
pylab.scatter(bound, np.log(score), marker='s', color=color)
min_score = np.min(particles['score'])
max_score = np.max(particles['score'])
if self.min_score is None:
self.min_score = min_score
self.max_score = max_score
self.min_score = min(self.min_score, min_score)
self.max_score = min(self.max_score, max_score)
# a = pylab.axis()
# pylab.axis((-1, 30, a[2], a[3]))
M = 0.1
y0 = np.log(self.min_score)
y1 = np.log(self.max_score)
delta = y1 - y0
y0 -= M * delta
y1 += M * delta
pylab.axis((-1, 30, y0, y1))
pylab.xlabel('spatial uncertainty (pixels)')
pylab.ylabel('score')
pylab.title('Particles statistics')
def plot(self, pylab):
tracks = self.input.tracks
track2color = get_track_colors(tracks)
for id_track, particles in enumerate_id_track(self.input.tracks):
color = track2color[id_track]
bound = particles['bound']
score = particles['score']
pylab.scatter(bound, np.log(score), marker='s', color=color)
min_score = np.min(particles['score'])
max_score = np.max(particles['score'])
if self.min_score is None:
self.min_score = min_score
self.max_score = max_score
self.min_score = min(self.min_score, min_score)
self.max_score = min(self.max_score, max_score)
# a = pylab.axis()
# pylab.axis((-1, 30, a[2], a[3]))
M = 0.1
y0 = np.log(self.min_score)
y1 = np.log(self.max_score)
delta = y1 - y0
y0 -= M * delta
y1 += M * delta
pylab.axis((-1, 30, y0, y1))
pylab.xlabel('spatial uncertainty (pixels)')
pylab.ylabel('score')
pylab.title('Particles statistics')
def plot(self, pylab):
tracks = self.input.tracks
track2color = get_track_colors(tracks)
for id_track, particles in enumerate_id_track(self.input.tracks):
color = track2color[id_track]
plot_particles(pylab, particles, color)
set_viewport_style(pylab)
pylab.title('Particles')
def plot(self, pylab):
tracks = self.input.tracks
track2color = get_track_colors(tracks)
for id_track, particles in enumerate_id_track(self.input.tracks):
color = track2color[id_track]
plot_particles(pylab, particles, color)
set_viewport_style(pylab)
pylab.title('Particles')
def probability_velocity(self, tracks):
for _, its_tracks in enumerate_id_track(tracks):
if len(its_tracks) == 1:
return 1.0
vn = track_velocities_norm(its_tracks)
max_vn = np.max(vn)
if max_vn >= self.max_vel:
return 0.0
else:
return 1.0
def probability_velocity(self, tracks):
for _, its_tracks in enumerate_id_track(tracks):
if len(its_tracks) == 1:
return 1.0
vn = track_velocities_norm(its_tracks)
max_vn = np.max(vn)
if max_vn >= self.max_vel:
return 0.0
else:
return 1.0
def plot_tracks(pylab, tracks, base_markersize=5, marker='s', alpha=1):
assert isinstance(tracks, np.ndarray)
t2c = get_track_colors(tracks)
for id_track, its_data in enumerate_id_track(tracks):
for t in its_data:
x = t['i']
y = t['j']
# r = 1 is best, r =0 is worst
r = (t['npeaks'] - t['peak']) * 1.0 / t['npeaks']
markersize = base_markersize * (r / 2 + 0.5)
pylab.plot(x, y, color=t2c[id_track], marker=marker,
markersize=markersize, alpha=alpha)
def plot_tracks(pylab, tracks, base_markersize=5, marker='s', alpha=1):
assert isinstance(tracks, np.ndarray)
t2c = get_track_colors(tracks)
for id_track, its_data in enumerate_id_track(tracks):
for t in its_data:
x = t['i']
y = t['j']
# r = 1 is best, r =0 is worst
r = (t['npeaks'] - t['peak']) * 1.0 / t['npeaks']
markersize = base_markersize * (r / 2 + 0.5)
pylab.plot(x,
y,
color=t2c[id_track],
marker=marker,
markersize=markersize,
alpha=alpha)
def plot(self, pylab):
tracks = self.input.tracks
assert isinstance(tracks, np.ndarray)
t2c = get_track_colors(tracks)
x_ticks = []
x_label = []
for i, xx in enumerate(enumerate_id_track(tracks)):
id_track, its_data = xx
x_ticks.append(i)
x_label.append(id_track)
quality = its_data['quality']
for j, qq in enumerate(quality):
xj = i + 0.16 * (j + 1 - len(quality) / 2.0)
pylab.plot([xj, xj], [0, qq], '%s-' % t2c[id_track], linewidth=2)
self.max_q = max(self.max_q, np.max(tracks['quality']))
M = 0.1
pylab.axis((-1, len(x_ticks), -M * self.max_q, self.max_q * (1 + M)))
pylab.xticks(x_ticks, x_label)
pylab.title('Detection quality')
def get_last(subset):
tracks = {}
for id_track, its_data in enumerate_id_track(subset):
tracks[id_track] = its_data[-1:]
return tracks
def plot_hp(self, pylab, alt, marker):
particles = alt.subset
track2color = get_track_colors(particles)
for id_track, particles in enumerate_id_track(particles):
color = track2color[id_track]
plot_particles(pylab, particles, color)
def plot_hp(self, pylab, alt, marker):
particles = alt.subset
track2color = get_track_colors(particles)
for id_track, particles in enumerate_id_track(particles):
color = track2color[id_track]
plot_particles(pylab, particles, color)
def get_last(subset):
tracks = {}
for id_track, its_data in enumerate_id_track(subset):
tracks[id_track] = its_data[-1:]
return tracks
