def plot_spectrum(pub, name, matrix):
from scipy.linalg import eigh
S, _ = eigh(matrix) # returns the eigenvalues reversed
eigs = S[::-1]
with pub.plot('%s-eigs' % name, caption='Spectrum of %s' % name) as pylab:
style_ieee_halfcol_xy(pylab)
svd_plot(pylab, eigs)
def publish(self, pub):
if self.count < 10:
pub.text('warning', 'Too early to publish anything.')
return
pub.text('info', 'Using statistics: %s' % self.statistic)
if False: # TODO: make option
S = self.get_S_discrete(2, pub=pub.section('computation'))
else:
S = self.get_S(2, pub=pub.section('computation'))
with pub.plot('S') as pylab:
style_ieee_halfcol_xy(pylab)
pylab.plot(S[0, :], S[1, :], 's')
with pub.plot('S_joined') as pylab:
style_ieee_halfcol_xy(pylab)
pylab.plot(S[0, :], S[1, :], '-')
self.y_stats.publish(pub.section('y_stats'))
self.y_dot_stats.publish(pub.section('y_dot_stats'))
self.y_dot_sgn_stats.publish(pub.section('y_dot_sgn_stats'))
self.y_dot_abs_stats.publish(pub.section('y_dot_abs_stats'))
def servo_stats_report(data_central, id_agent, id_robot, summaries,
phase='servo_stats'):
from reprep import Report
from reprep.plot_utils import x_axis_balanced
from reprep.plot_utils import (style_ieee_fullcol_xy,
style_ieee_halfcol_xy)
from geometry import translation_from_SE2
if not summaries:
raise Exception('Empty summaries')
def extract(key):
return np.array([s[key] for s in summaries])
initial_distance = extract('initial_distance')
initial_rotation = extract('initial_rotation')
dist_xy_converged = 0.25
dist_th_converged = np.deg2rad(5)
for s in summaries:
dist_xy = s['dist_xy']
dist_th = s['dist_th']
# converged = (dist_xy[0] > dist_xy[-1]) and (dist_th[0] > dist_th[-1])
converged = ((dist_xy[-1] < dist_xy_converged) and
(dist_th[-1] < dist_th_converged))
s['converged'] = converged
s['dist_th_deg'] = np.rad2deg(s['dist_th'])
s['color'] = 'b' if converged else 'r'
trans = [translation_from_SE2(x) for x in s['poses']]
s['x'] = np.abs([t[0] for t in trans])
s['y'] = np.abs([t[1] for t in trans])
s['dist_x'] = np.abs(s['x'])
s['dist_y'] = np.abs(s['y'])
basename = 'servo_analysis-%s-%s-%s' % (id_agent, id_robot, phase)
r = Report(basename)
r.data('summaries', s, caption='All raw statistics')
f = r.figure(cols=3)
with f.plot('image_L2_error') as pylab:
style_ieee_fullcol_xy(pylab)
for s in summaries:
errors = s['errors']
pylab.plot(errors, s['color'])
with f.plot('dist_xy') as pylab:
style_ieee_fullcol_xy(pylab)
for s in summaries:
pylab.plot(s['dist_xy'], s['color'] + '-')
with f.plot('xy') as pylab:
style_ieee_fullcol_xy(pylab)
for s in summaries:
pylab.plot(s['x'], s['y'], s['color'] + '-')
pylab.xlabel('x')
pylab.ylabel('y')
with f.plot('dist_th') as pylab:
style_ieee_fullcol_xy(pylab)
for s in summaries:
pylab.plot(s['dist_th'], s['color'] + '-')
with f.plot('dist_th_deg') as pylab:
style_ieee_fullcol_xy(pylab)
for s in summaries:
pylab.plot(np.rad2deg(s['dist_th']), s['color'] + '-')
with f.plot('dist_xy_th') as pl:
style_ieee_fullcol_xy(pylab)
for s in summaries:
pl.plot(s['dist_xy'], s['dist_th_deg'], s['color'] + '-')
pl.xlabel('dist x-y')
pl.ylabel('dist th (deg)')
with f.plot('dist_xy_th_log') as pl:
style_ieee_fullcol_xy(pylab)
for s in summaries:
pl.semilogx(s['dist_xy'],
s['dist_th_deg'], s['color'] + '.')
pl.xlabel('dist x-y')
pl.ylabel('dist th (deg)')
with f.plot('dist_y') as pylab:
style_ieee_fullcol_xy(pylab)
for s in summaries:
pylab.plot(s['dist_y'], s['color'] + '-')
with f.plot('dist_x') as pylab:
style_ieee_fullcol_xy(pylab)
for s in summaries:
pylab.plot(s['dist_x'], s['color'] + '-')
mark_start = 's'
mark_end = 'o'
with f.plot('dist_xy_th_start',
caption="Initial error (blue: converged)"
) as pl:
style_ieee_fullcol_xy(pylab)
for s in summaries:
pl.plot([s['dist_xy'][0], s['dist_xy'][-1]],
[s['dist_th_deg'][0],
s['dist_th_deg'][-1]], s['color'] + '-')
pl.plot(s['dist_xy'][0], s['dist_th_deg'][0],
s['color'] + mark_start)
pl.plot(s['dist_xy'][-1], s['dist_th_deg'][-1],
s['color'] + mark_end)
pl.xlabel('dist x-y')
pl.ylabel('dist th (deg)')
with f.plot('dist_xy_th_start2',
caption="Trajectories. If converged, plot square at beginning"
" and cross at end. If not converged, plot trajectory (red)."
) as pl:
style_ieee_fullcol_xy(pylab)
for s in summaries:
if s['converged']:
continue
pl.plot([s['dist_xy'][0], s['dist_xy'][-1]],
[s['dist_th_deg'][0], s['dist_th_deg'][-1]], 'r-')
for s in summaries:
pl.plot(s['dist_xy'][0], s['dist_th_deg'][0], s['color'] + mark_start)
pl.plot(s['dist_xy'][-1], s['dist_th_deg'][-1], s['color'] + mark_end)
pl.xlabel('dist x-y')
pl.ylabel('dist th (deg)')
with f.plot('initial_rotation') as pylab:
style_ieee_halfcol_xy(pylab)
pylab.hist(np.rad2deg(initial_rotation))
x_axis_balanced(pylab)
pylab.xlabel('Initial rotation (deg)')
with f.plot('initial_distance') as pylab:
style_ieee_halfcol_xy(pylab)
pylab.hist(initial_distance)
pylab.xlabel('Initial distance (m)')
ds = data_central.get_dir_structure()
filename = ds.get_report_filename(id_agent=id_agent,
id_robot=id_robot,
id_state='servo_stats',
phase=phase)
resources_dir = ds.get_report_res_dir(id_agent=id_agent,
id_robot=id_robot,
id_state='servo_stats',
phase=phase)
save_report(data_central, r, filename, resources_dir, save_pickle=True)
