def load_data(path):
def get_realXY_from_trial(trial):
if scapp_report[trial]['ExpcResp'] == 0:
realX = scapp_report[trial]['X2']+51
realY = scapp_report[trial]['Y2']+55
elif scapp_report[trial]['ExpcResp'] == 1:
realX = scapp_report[trial]['X1']+51
realY = scapp_report[trial]['Y1']+55
else:
raise "Missing Value on Report"
return (K.SCREEN_WIDTH_PX-realX, realY)
realXY = [get_realXY_from_trial(i) for i in range(len(scapp_report))]
by_trial = []
all_gaze = []
all_real = []
mus = []
clusters = np.load(path)
for cluster in clusters:
gaze_by_trial = []
real_by_trial = []
for gaze in cluster:
g = gaze['norm_pos']
r = get_realXY_from_trial(gaze['trial'])
gaze_by_trial.append(g)
real_by_trial.append(r)
all_gaze.append(g)
all_real.append(r)
real_by_trial = np.vstack(real_by_trial)
gaze_by_trial = np.vstack(gaze_by_trial)
gaze_by_trial = normalized_to_pixel(gaze_by_trial)
gmeanX = np.mean(gaze_by_trial[:,0])
gmeanY = np.mean(gaze_by_trial[:,1])
rmeanX = np.mean(real_by_trial[:,0])
rmeanY = np.mean(real_by_trial[:,1])
# intermediate accuracy defined as the mean
# difference of the real positions and the reported positions
by_trial.append({'g':gaze_by_trial,'r':real_by_trial})
mus.append(np.array([gmeanX,gmeanY,rmeanX,rmeanY]))
# vertical stack
realXY = np.vstack(set(realXY))
all_gaze = np.vstack(all_gaze)
all_real = np.vstack(all_real)
all_gaze = normalized_to_pixel(all_gaze)
meanX = np.mean(all_gaze[:,0])
meanY = np.mean(all_gaze[:,1])
MU = [meanX, meanY]
return MU, mus, all_gaze, all_real, realXY, by_trial
def load_data(path):
all_gaze = []
clusters = np.load(path)
sds = []
for cluster in clusters:
by_trial = []
for gaze in cluster:
by_trial.append(gaze['norm_pos'])
by_trial = np.vstack(by_trial)
by_trial = move_mean_to_zero(by_trial)
by_trial = normalized_to_pixel(by_trial)
#by_trial = normalized_to_degree(by_trial)
#by_trial = normalized_to_pixel(by_trial)
by_trial = pixel_to_degree(by_trial)
# intermediate precision defined as the Standard Deviation of observations in the cluster/trial
sds.append(np.std(by_trial, ddof=1, dtype=np.float64))
all_gaze.append(by_trial)
# overall precision defined as the Standard Deviation of all observations
all_gaze = np.vstack(all_gaze)
sd = np.std(all_gaze, ddof=0, dtype=np.float64)
print 'sd:',round(sd,3)
# overall variance
var = np.var(all_gaze,ddof=0, dtype=np.float64)
# overall precision defined as the RMS of all observations (should be equal to SD when the mean = zero)
rms = root_mean_square(all_gaze)
print 'rms:',rms
print ''
return var, sd, sds, all_gaze