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 get_pursuit_fits(start, end):
from segreg import piecewise_linear_regression_pseudo_em, segmentation_to_table
data = get_angled_range_data2(start, end)
data = data[data["g_direction_q"] > 0.2]
params = ((1.5, 1.5), 0.5)
grouping = ("session_id", "lap")
grps = []
for i, (grp, d) in enumerate(groupby_multiple(data, grouping)):
t = d["ts"]
g = np.vstack((d["scenecam_x"], d["scenecam_y"])).T
splits, valid, winner, params = memorize(piecewise_linear_regression_pseudo_em)(t, g, *params)
seg = segmentation_to_table(splits, t[valid], g[valid])
print tuple(grp)
grps.append((tuple(grp), seg))
return grps
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