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')
