def style_1d_sensel_func(pylab, n, y_max, extra_vspace=1.1):
"""
Decorates approapriateyle
"""
y_axis_set(pylab, -y_max * extra_vspace, y_max * extra_vspace)
x_axis_set(pylab, -1, n)
turn_off_bottom_and_top(pylab)
turn_off_right(pylab)
set_left_spines_outward(pylab, offset=10)
set_thick_ticks(pylab, markersize=3, markeredgewidth=1)
pylab.plot([0, n - 1], [0, 0], "--", color=[0.7, 0.7, 0.7])
def publish(self, pub):
if not self.once:
pub.text('info', 'never called yet')
return
N = self.last_y.size
with pub.plot('last_w') as pylab:
pylab.plot(self.last_w, 's')
x_axis_set(pylab, -1, N)
y_axis_set(pylab, -0.1, 1.1)
turn_off_bottom_and_top(pylab)
gy0 = self.last_gy[0, :]
def plot_good_bad(pylab, x, valid):
invalid = np.logical_not(valid)
pylab.plot(np.nonzero(valid)[0], x[valid], 'ks')
pylab.plot(np.nonzero(invalid)[0], x[invalid], 'rs')
with pub.plot('last_y') as pylab:
plot_good_bad(pylab, self.last_y, self.last_y_valid)
y_axis_set(pylab, -0.1, +1.1)
x_axis_set(pylab, -1, N)
turn_off_bottom_and_top(pylab)
with pub.plot('last_y_dot') as pylab:
pylab.plot(self.last_y_dot)
plot_good_bad(pylab, self.last_y_dot, self.last_y_dot_valid)
upper = np.ones(N) * self.max_y_dot
lower = -upper
pylab.plot(upper, 'r--')
pylab.plot(lower, 'r--')
x_axis_set(pylab, -1, N)
y_axis_balanced(pylab)
turn_off_bottom_and_top(pylab)
with pub.plot('last_gy') as pylab:
plot_good_bad(pylab, gy0, self.last_gy_valid)
upper = np.ones(N) * self.max_gy
lower = -upper
pylab.plot(upper, 'r--')
pylab.plot(lower, 'r--')
x_axis_set(pylab, -1, N)
y_axis_balanced(pylab)
turn_off_bottom_and_top(pylab)
self.w_stats.publish(pub.section('w_stats'))
def comparison_balanced(pylab, a, b, perc=0.95, M=1.1):
"""
Computes 1-perc and perc percentiles, and fits only as much.
"""
values = np.hstack((a, b))
level = np.percentile(np.abs(values), perc * 100)
limit = M * level
y_axis_set(pylab, -limit, limit)
n = a.size
x_axis_set(pylab, -1, n)
def plot(self, pylab):
ts = self.get_relative_times()
es = self.get_relative_errors()
assert ts.size == es.size
self.plot_anim.set_pylab(pylab)
if ts.size > 0:
T = self.get_T(duration=(ts[-1] - ts[0]), chunk=5, min_time=15)
assert T >= ts[-1]
else:
T = 10
if self.plot_line is None:
ax1 = pylab.gca()
ax1.axes.get_xaxis().set_visible(False)
ax1.axes.get_yaxis().set_visible(False)
self.plot_line = True
border = 2
if self.config.use_first_y_goal:
pylab.axis((-1, T + border, -0.1, 1.3))
else:
x_axis_set(pylab, -1, T + border)
if es.size > 0:
y_axis_set(pylab, -0.1, np.max(es) * 1.3)
# s = '%s %s' % (self.servo_state, self.timestamp)
# self.plot_anim.text('status', 0.7 * T, 1.2, s)
# self.plot_anim.text('clock', 0.7 * T, 1.2, '%5.2f' % self.time_since_start)
self.plot_anim.plot('error', ts, es, 'k-')
if es.size > 0:
self.plot_anim.plot('error1', ts[0], es[0], 'rs')
self.plot_anim.plot('error2', ts[-1], es[-1], 'rs')
else:
self.plot_anim.plot('error1', [], [], 'ro')
self.plot_anim.plot('error2', [], [], 'ro')
self.plot_anim.plot('zero', [0, T], [0, 0], 'k--')
if self.servo_state == STATE_SERVOING:
# self.info('Servoing')
pass
else:
pass
def publish_u_stats(self, pub):
T = len(self.u_stats)
print('Obtained %d obs' % T)
K = 2 # FIXME: change this
u_act = np.zeros((T, K))
u_est = np.zeros((T, K))
u_mis = np.zeros((T, K))
u_suc = np.zeros((T, K))
time = np.zeros(T)
num_episode = np.zeros(T, 'int')
id_episode2num = {}
num2id_episode = {}
id_episode2start = {}
# cmd2faults = {}
for t, stats in enumerate(self.u_stats):
u_act[t, :] = stats['u']
u_est[t, :] = stats['u_est']
time[t] = stats['timestamp']
id_ep = stats['id_episode']
if not id_ep in id_episode2num:
id_episode2num[id_ep] = len(id_episode2num)
id_episode2start[id_ep] = time[t]
num2id_episode[id_episode2num[id_ep]] = id_ep
num_episode[t] = id_episode2num[id_ep]
s = ""
for k, v in id_episode2num.items():
s += '%s: %s\n' % (k, v)
pub.text('episodes', s)
with pub.plot('num_episode') as pylab:
pylab.plot(num_episode, '-')
pylab.xlabel('index')
pylab.ylabel('num\_episode')
for id_episode, num in id_episode2num.items():
print id_episode
S = pub.section('Episode:%s' % id_episode)
# times for this episode
et = num_episode == num
# normalize from 0
e_timestamps = time[et]
log_start = e_timestamps[0]
e_timestamps -= log_start
cmd2color = {0: 'g', 1: 'b'}
episode_bounds = (18, 60)
markersize = 2
with S.plot('mis', figsize=(8, 2), mime=MIME_PDF) as pylab:
for k in range(K):
# scale = 7
# u_mis_smooth = scipy.signal.convolve(u_mis[et, k],
# np.ones(scale) / scale,
# mode='same')
pylab.plot(e_timestamps, u_mis[et, k], # u_mis_smooth,
'%s-' % cmd2color[k], label='u[%d]' % k,
markersize=markersize)
x_axis_set(pylab, episode_bounds[0], episode_bounds[1])
with S.plot('success', figsize=(8, 2), mime=MIME_PDF) as pylab:
pylab.plot(e_timestamps, e_timestamps * 0, 'k--')
pylab.plot(e_timestamps, np.ones(len(e_timestamps)), 'k--')
for k in range(K):
pylab.plot(e_timestamps, u_suc[et, k],
'%s-' % cmd2color[k], label='cmd #%d' % k)
y_axis_set(pylab, -0.05, 1.05)
x_axis_set(pylab, episode_bounds[0], episode_bounds[1])
pylab.legend(loc='lower right')
for k in range(K):
with S.plot('commands_%d' % k, figsize=(8, 2),
mime=MIME_PDF) as pylab:
pylab.plot(e_timestamps, u_act[et, k], 'y.',
label='actual', markersize=3)
plot_with_colors(pylab, e_timestamps,
u_est[et, k], u_act[et, k],
markersize=markersize)
y_axis_set(pylab, -2, 2)
x_axis_set(pylab, episode_bounds[0], episode_bounds[1])
def plot_style_sensels(pylab):
y_axis_set(pylab, -0.1, 1.1)
x_axis_extra_space(pylab)
def plot_style_sensels_deriv(pylab):
y_axis_set(pylab, -0.1, 0.1)
x_axis_extra_space(pylab)
