def get_pursuits_and_data_by_range(range):
t1, t2 = range
data = get_angled_range_data2(t1, t2)
segs = get_pursuit_fits(t1, t2)
grp, segs = zip(*segs)
groups = np.rec.fromrecords(grp, names='session_id,lap')
return groups, data, grp, segs
def get_pooled_data(cis, is_control):
arr_desc = [('sid', int), ('bend', int), ('yaw', float), ('speed', float)]
table = np.array([], dtype=arr_desc)
#cis = [1,2,3]
for ci in cis:
data = get_angled_range_data2(CORNERING[ci][0], CORNERING[ci][1])
segs = get_pursuit_fits(CORNERING[ci][0], CORNERING[ci][1])
data, segs = filter_by_treatment(data, segs, is_control)
data.sort(order=['session_id', 'ts'])
grp, segs = zip(*segs)
groups = np.rec.fromrecords(grp, names='session_id,lap')
for (sid, lap), d in groupby_multiple(data, ('session_id', 'lap')):
#if (np.sum(d['g_direction_q'] < 0.2) > 0.25*(len(d))): continue
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]
lap_dpoints = []
for s in seg:
dt = s.t1 - s.t0
if dt == 0: continue
gspeed = -((s.d1[0]-s.d0[0]) / dt)
slice = d[(d['ts'] >= s.t0) &
(d['ts'] <= s.t1)]
ym = np.mean(toyota_yaw_rate_in_degrees(slice['c_yaw']))
lap_dpoints.append((ym, gspeed))
lap_dpoints = np.array(lap_dpoints)
if (len(lap_dpoints > 0)):
yaw = np.mean(lap_dpoints[:,0])
speed = np.median(lap_dpoints[:,1])
arr = np.array([(sid,ci,yaw,speed)], dtype=arr_desc)
table = np.append(table, arr)
return table
def pursuit_yaw_means():
pdf_out = PdfPages('/tmp/pursuit_vs_yaw_pooled.pdf')
cis = [1,2,3]
all_data = []
all_segs = []
for ci in cis:
data = get_angled_range_data2(CORNERING[ci][0], CORNERING[ci][1])
segs = get_pursuit_fits(CORNERING[ci][0], CORNERING[ci][1])
all_data.append(data)
all_segs.append(segs)
data = np.hstack(all_data)
segs = np.vstack(all_segs)
data.sort(order=['session_id', 'ts'])
grp, segs = zip(*segs)
groups = np.rec.fromrecords(grp, names='session_id,lap')
all_dpoints = []
xrange = [12,18]
for sid, i in groupby_i(data, 'session_id'):
d = data[i]
print sid
seg = [segs[k] for k in np.flatnonzero(groups.session_id == sid)]
seg = np.hstack(seg).view(np.recarray)
seg = seg[seg.n > 12]
dpoints = []
for s in seg:
dt = s.t1 - s.t0
if dt == 0: continue
gspeed = -((s.d1[0]-s.d0[0]) / dt)
slice = d[(d['ts'] >= s.t0) &
(d['ts'] <= s.t1)]
ym = np.mean(toyota_yaw_rate_in_degrees(slice['c_yaw']))
naksu_x = pxx2heading(slice['naksu_x'])
naksu_mean = (naksu_x[1] - naksu_x[0]) / dt
dpoints.append((ym, gspeed, naksu_mean))
all_dpoints.append(dpoints)
dpoints = np.array(dpoints)
print scipy.stats.pearsonr(dpoints[:,0], dpoints[:,1])
plt.figure()
plt.title(sid)
plt.hist(dpoints[:,0], bins=50)
#plt.xlim(xrange)
pdf_out.savefig()
plt.close()
all_dpoints = np.array(all_dpoints)
means = np.array([np.median(x,0) for x in all_dpoints])
ymeans = means[:,0]
gmeans = means[:,1]
nmeans = means[:,2]
#from utils import fitLine
#fitLine(ymeans,gmeans)
fit = np.polyfit(ymeans, gmeans, 1)
lsr = np.poly1d(fit)
model = np.poly1d([0.5, 0])
plt.figure()
plt.plot(ymeans,gmeans,'.k')
plt.plot(xrange, lsr(xrange), '-b')
plt.plot(xrange, model(xrange), '-g')
plt.xlabel('mean yaw rate (deg/s)')
plt.ylabel('mean gaze speed (deg/s)')
pdf_out.savefig()
plt.close()
nfit = np.polyfit(ymeans, nmeans, 1)
nlsr = np.poly1d(nfit)
plt.figure()
plt.plot(ymeans,nmeans,'.k')
plt.plot(xrange, nlsr(xrange), '-b')
plt.xlabel('mean yaw rate (deg/s)')
plt.ylabel('mean change in horizontal TP coordinate during pursuit (deg/s)')
pdf_out.savefig()
plt.close()
pdf_out.close()
print '\nspearman r: %f, p %f' % scipy.stats.spearmanr(ymeans, gmeans)
print 'pearson r: %f, p %f' % scipy.stats.pearsonr(ymeans,gmeans)
print 'least-squares fit: %f %f' % (fit[0], fit[1])
print 'yaw/TP location least-squares fit: %f %f' % (nfit[0], nfit[1])
