def raw_pursuits_pdf(ci=[1], is_control=True):
#ci = [3]
#is_control = True
txt = 'free' if is_control else 'tp'
pdf_out = PdfPages('/tmp/raw_pursuits'+str(ci)+'_'+txt+'.pdf')
data, segs = get_pooled(ci)
data, segs = filter_by_treatment(data, segs, is_control)
#print 'segs: ' + str(len(segs))
grp, segs = zip(*segs)
groups = np.rec.fromrecords(grp, names='session_id,lap')
data = data[ data['g_direction_q'] > 0.4 ]
for (sid, lap), d in groupby_multiple(data, ('session_id', 'lap')):
seg = [segs[k] for k in np.flatnonzero((groups.session_id == sid) &
(groups.lap == lap))]
#if (len(seg) == 0): continue
seg = np.hstack(seg).view(np.recarray)
seg = seg[seg.n > 12]
plt.figure()
ax1 = plt.subplot(211)
plt.title(str(sid) + ', ' + str(lap))
plt.plot(d['dist'], d['scenecam_x'], ',k')
for s in seg:
plt.plot([s.t0, s.t1], [s.d0[0], s.d1[0]], '-r')
plt.ylim([-10,50])
plt.ylabel('horiz.gaze (deg)')
ax2 = plt.subplot(212, sharex=ax1)
plt.plot(d['dist'], d['scenecam_y'], ',k')
for s in seg:
plt.plot([s.t0, s.t1], [s.d0[1], s.d1[1]], '-r')
plt.ylim([-15,15])
plt.ylabel('vert.gaze (deg)')
plt.xlabel('ts')
pdf_out.savefig()
plt.close()
pdf_out.close()
def yaw_variation(is_control=True):
ci = [1,3]
#is_control = False
data, segs = get_pooled(ci)
data, segs = filter_by_treatment(data, segs, is_control)
smeans = []
for sid, d in groupby(data, 'session_id'):
yaw = toyota_yaw_rate_in_degrees(d['c_yaw'])
mean = np.mean(yaw)
std = np.std(yaw)
print sid, mean, std
smeans.append((sid, mean))
#mean = np.mean(smeans)
#std = np.std(smeans)
#print 'mean: ' + str(mean) + ' std: ' + str(std)
return smeans