def fixation_frequencies(range):
groups, data, grp, segs = get_pursuits_and_data_by_range(range)
plt.figure()
for sid, d in groupby(data, 'session_id'):
seg = [segs[i] for i in np.flatnonzero(groups.session_id == sid)]
seg = np.hstack(seg).view(np.recarray)
seg = seg[(seg.t1 - seg.t0) > 0.1] # take out saccades
laps = groups[groups['session_id'] == sid]
laps = laps['lap']
mean_pursuits = 1.0*len(seg)/len(laps)
mean_vel = np.mean(d['c_vel'])
plt.plot(mean_vel, mean_pursuits, '.k')
print "%s: pursuits %i, mean_per_lap %f, mean_vel: %f" % (sid, len(seg), mean_pursuits, mean_vel)
plt.show()
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
def pursuit_vs_yaw(range):
groups, data, grp, segs = get_pursuits_and_data_by_range(range)
pdf_out = PdfPages('/tmp/pursuit_vs_yaw.pdf')
bin_size = 0.5
means = []
slopes = []
noises = []
for sid, d in groupby(data, 'session_id'):
seg = [segs[i] for i in np.flatnonzero(groups.session_id == sid)]
seg = np.hstack(seg).view(np.recarray)
#seg = seg[(seg.t1 - seg.t0) > 0.2] # take out saccades
seg = seg[seg.n > 12]
# filter outliers from gaze landings
values = np.vstack((seg.d0[:,0], seg.d0[:,1]))
fkde = gaussian_kde(values)
x, y = np.mgrid[np.min(values[0]):np.max(values[0]):bin_size,
np.min(values[1]):np.max(values[1]):bin_size]
grid = np.vstack((x.ravel(), y.ravel()))
z = fkde(grid)
reshaped = z.reshape(x.shape)
medianhdr = histogram_hdr(z, bin_size**2, 0.8)
seg = [s for s in seg if fkde((s.d0[0], s.d0[1])) > medianhdr]
seg = np.hstack(seg).view(np.recarray)
# mean yaw within each pursuit
withinPursuitYaw = []
for s in seg:
slice = d[(d['ts'] >= s['t0']) &
(d['ts'] <= s['t1'])]
ym = np.mean(toyota_yaw_rate_in_degrees(slice['c_yaw']))
withinPursuitYaw.append(ym)
withinPursuitYaw = np.array(withinPursuitYaw)
dt = seg.t1 - seg.t0
valid = dt > 0
dt = dt[valid]
gspeeds = -((seg.d1[:,0] - seg.d0[:,0]) / dt)
noise = withinPursuitYaw / 2 - gspeeds
plt.figure()
# raw scatter
fit = np.polyfit(withinPursuitYaw, gspeeds, 1)
rng = np.linspace(10, 20, len(withinPursuitYaw))
rline = np.polyval(fit, rng)
model = np.poly1d([0.5, 0])
plt.subplot(221)
plt.plot(withinPursuitYaw, gspeeds, '.k', alpha=0.2)
plt.plot(rng, rline, '-b')
plt.plot([10,20], [np.polyval(model, 10), np.polyval(model, 20)], '-g')
plt.ylim(-50,50)
plt.xlim(5,25)
plt.xlabel('mean yaw deg/s')
plt.ylabel('gaze speed deg/s')
plt.annotate('spearman r: %f' % scipy.stats.spearmanr(withinPursuitYaw, gspeeds)[0], (10,-10))
plt.annotate('slope: %f' % fit[0], (10,-20))
plt.title(sid)
# noise kde + pdf
plt.subplot(222)
kde = gaussian_kde(noise)
rng = np.linspace(np.min(noise), np.max(noise), len(noise))
plt.plot(rng, kde(rng), '-b', label='kde')
plt.plot(rng, scipy.stats.norm.pdf(rng, loc=np.mean(noise), scale=np.std(noise)), '-r', label='pdf')
plt.hist(noise, bins=30, normed=True, color='green')
plt.xlabel('mean yaw / 2 - gaze speed')
plt.legend(loc='upper right')
# pursuit landings scatter & density map
plt.subplot(223)
plt.contour(x,y,reshaped, [medianhdr])
plt.plot(seg.d0[:,0], seg.d0[:,1], ',k')
plt.xlim(-60,60)
plt.ylim(-20,20)
pdf_out.savefig()
plt.close()
print '%i \nspearman r: %f' % (sid, scipy.stats.spearmanr(withinPursuitYaw, gspeeds)[0])
# how many fixations are from right to left (74-90 degrees)
#validspeed = gspeeds < 0
#print '%f of fixations from right to left' % (1.0 * len(gspeeds[validspeed]) / len(gspeeds))
print 'mean of yaws / gaze speeds %f' % (np.mean(gspeeds / withinPursuitYaw))#, scipy.stats.mode(np.around(withinPursuitYaw / gspeeds, 1))[0])
print 'mean of mean yaw / 2 - gaze speed: %f' % (np.mean(withinPursuitYaw / 2 - gspeeds))
print 'noise shapiro-wilk w: %f p: %f' % scipy.stats.shapiro(noise)
#print 'median hdr %f' % medianhdr
#print 'mean of yaws: %f' % np.mean(withinPursuitYaw)
#print 'mean of gspeeds: %f' % np.mean(gspeeds)
means.append((np.mean(withinPursuitYaw), np.mean(gspeeds)))
slopes.append(fit)
noises.append(noise)
pdf_out.close()
return (means, slopes, noises)
