def print_odd_ones(self, pub, P, perc=50, ratio=0.2):
std = np.sqrt(P.diagonal())
odd, okay = find_odd(std, perc, ratio)
f = pub.figure()
pub.text('okay', '%s' % list(okay))
pub.text('odd', '%s' % list(odd))
std = std.copy()
with f.plot('odd') as pylab:
style_ieee_fullcol_xy(pylab)
pylab.plot(odd, std[odd], 'rs')
pylab.plot(okay, std[okay], 'gs')
y_axis_extra_space(pylab)
def publish(self, pub):
if self.y_stats.get_num_samples() == 0:
pub.text('warning', 'Too early to publish anything.')
return
Py = self.y_stats.get_covariance()
Ry = self.y_stats.get_correlation()
Py_inv = self.y_stats.get_information()
Ey = self.y_stats.get_mean()
y_max = self.y_stats.get_maximum()
y_min = self.y_stats.get_minimum()
Ry0 = Ry.copy()
np.fill_diagonal(Ry0, np.NaN)
Py0 = Py.copy()
np.fill_diagonal(Py0, np.NaN)
pub.text('stats', 'Num samples: %s' % self.y_stats.get_num_samples())
with pub.plot('y_bounds') as pylab:
style_ieee_fullcol_xy(pylab)
pylab.plot(Ey, label='E(y)')
pylab.plot(y_max, label='y_max')
pylab.plot(y_min, label='y_min')
pylab.legend()
all_positive = (np.min(Ey) > 0
and np.min(y_max) > 0
and np.min(y_min) > 0)
if all_positive:
with pub.plot('y_stats_log') as pylab:
style_ieee_fullcol_xy(pylab)
pylab.semilogy(Ey, label='E(y)')
pylab.semilogy(y_max, label='y_max')
pylab.semilogy(y_min, label='y_min')
pylab.legend()
pub.array_as_image('Py', Py, caption='cov(y)')
pub.array_as_image('Py0', Py0, caption='cov(y) - no diagonal')
pub.array_as_image('Ry', Ry, caption='corr(y)')
pub.array_as_image('Ry0', Ry0, caption='corr(y) - no diagonal')
pub.array_as_image('Py_inv', Py_inv)
pub.array_as_image('Py_inv_n', cov2corr(Py_inv))
with pub.plot('Py_svd') as pylab: # XXX: use spectrum
style_ieee_fullcol_xy(pylab)
_, s, _ = np.linalg.svd(Py)
s /= s[0]
pylab.semilogy(s, 'bx-')
with pub.subsection('y_stats') as sub:
self.y_stats.publish(sub)
def publish(self, pub, publish_information=False):
if self.num_samples == 0:
pub.text('warning',
'Cannot publish anything as I was never updated.')
return
P = self.get_covariance()
R = self.get_correlation()
Ey = self.get_mean()
y_max = self.get_maximum()
y_min = self.get_minimum()
pub.text('data', 'Num samples: %s' % self.mean_accum.get_mass())
if Ey.size > 1:
with pub.plot('expectation') as pylab:
style_ieee_fullcol_xy(pylab)
pylab.plot(Ey, 's', label='expectation')
pylab.plot(y_max, 's', label='max')
pylab.plot(y_min, 's', label='min')
y_axis_extra_space(pylab)
pylab.legend()
stddev = np.sqrt(P.diagonal())
with pub.plot('stddev') as pylab:
style_ieee_fullcol_xy(pylab)
pylab.plot(stddev, 's', label='stddev')
y_axis_extra_space(pylab)
pylab.legend()
self.print_odd_ones(pub, P, perc=50, ratio=0.2)
from boot_agents.misc_utils.tensors_display import pub_tensor2_cov
pub_tensor2_cov(pub, 'covariance', P)
# pub.array_as_image('covariance', P)
# TODO: get rid of this?
R = R.copy()
np.fill_diagonal(R, np.nan)
pub.array_as_image('correlation', R)
if publish_information:
P_inv = self.get_information()
pub.array_as_image('information', P_inv)
with pub.plot('P_diagonal') as pylab:
style_ieee_fullcol_xy(pylab)
pylab.plot(P.diagonal(), 's')
y_axis_positive(pylab)
with pub.plot('P_diagonal_sqrt') as pylab:
style_ieee_fullcol_xy(pylab)
pylab.plot(np.sqrt(P.diagonal()), 's')
y_axis_positive(pylab)
else:
stats = ""
stats += 'min: %g\n' % y_min
stats += 'mean: %g\n' % Ey
stats += 'max: %g\n' % y_min
stats += 'std: %g\n' % np.sqrt(P)
pub.text('stats', 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)
