def repsec_servo1_generic_vel_field(r, fname, centroid, nmap, vels,
area_graphs, normalize=True):
f = r.figure(cols=2)
omegas = map(angular_from_se2, vels)
has_theta = np.any(omegas != 0)
figsize = (6, 6)
arrow_length = nmap.get_average_interpoint_R2_distance() / 2
print('arrow length: %s' % arrow_length)
caption = 'First two components of "%s".' % fname
with f.plot('xy_arrows', caption=caption, figsize=figsize) as pylab:
nmap.plot_points(pylab)
nmap.plot_vels(pylab, vels, normalize, length=arrow_length)
plot_style_servo_field_xy(pylab, area_graphs=area_graphs)
@contract(pose='SE2', returns='>=0')
def distance_to_centroid(pose):
t = translation_from_SE2(pose)
d = np.linalg.norm(t - centroid)
return d
poses = nmap.get_poses()
derivs = vector_field_derivs(SE2, poses, vels, distance_to_centroid)
def deriv2color(s):
if s == 0:
return 'k'
if s > 0:
return 'r'
if s < 0:
return 'g'
colors = map(deriv2color, derivs)
with f.plot('xy_arrows_colors', caption=caption, figsize=figsize) as pylab:
nmap.plot_vels(pylab, vels, normalize, colors=colors)
plot_style_servo_field_xy(pylab, area_graphs=area_graphs)
if has_theta:
caption = 'Third component of "%s".' % fname
with f.plot('xy_u_th_sign', caption=caption) as pylab:
nmap.plot_scalar_field_sign(pylab, omegas)
plot_style_servo_field_xy(pylab, area_graphs=area_graphs)
f = r.figure()
if has_theta:
poses = nmap.get_poses()
with f.plot('yt', caption=caption) as pylab:
plot_poses_vels_yt(pylab, poses, vels, normalize=True)
pylab.xlabel('y (m)')
pylab.ylabel('theta (deg)')
y_axis_balanced(pylab)
x_axis_balanced(pylab)
with f.plot('xt', caption=caption) as pylab:
plot_poses_vels_xt(pylab, poses, vels, normalize=True)
pylab.xlabel('x (m)')
pylab.ylabel('theta (deg)')
y_axis_balanced(pylab)
x_axis_balanced(pylab)
with f.plot('tw', caption=caption) as pylab:
plot_poses_vels_theta_omega(pylab, poses, vels)
pylab.xlabel('theta (deg)')
pylab.ylabel('omega')
y_axis_balanced(pylab)
x_axis_balanced(pylab)
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)