def annotated_plot(dm):
"""
desc:
Plots mean pupil size separately for dark and bright trials, and
annotates the plot with significance markers.
arguments:
dm:
type: DataMatrix
"""
plot.new(size=(8,6))
lm = trace_lmer_simple(dm)
plt.axvline(RT, color='black', linestyle=':')
x = np.arange(3000)
for color, a in [
('black', 1.96), # p < .05
('red', 2.57), # p < .01
('orange', 2.81), # p < .005
('yellow', 3.29) # p < .001
]:
threshold = series.threshold(lm.t,
lambda t: abs(t) > a, min_length=1)
print('Alpha %.5f (%.2f)' % (a, series.reduce_(threshold)[1]))
plot.threshold(threshold[1], color=color, linewidth=1)
dm_dark = dm.type == 'ctrl'
plot.trace(dm_dark.pupil, color=grey[3],
label='Neutral (N=%d)' % len(dm_dark))
pupil.brightness_plot(dm, subplot=True)
plt.xticks(range(0, 3001, 500), np.linspace(0,3,7))
plt.xlabel('Time since word onset (s)')
plt.ylabel('Pupil size (normalized to word onset)')
plot.save('annotated_plot')
def plot_pupiltrace(dm, suffix='', interaction=False):
"""
desc:
Create a plot of the pupil trace annotated with significant regions.
arguments:
dm:
type: DataMatrix
keywords:
suffix: A suffix for the plot.
interaction: Indicates whether the log_velocity by side interaction
should be included.
"""
plot.new(size=(12, 6))
# Plot the three velocities as separate lines
for color, velocity in ( (orange[1], 30), (blue[1], 3), (green[1], .3) ):
dm_ = dm.velocity == velocity
plot.trace(dm_.pupil, label='%s cm/s (N=%d)' % (velocity, len(dm_)),
color=color)
# Run statistical model, and annotate the figure with three alpha levels:
# .05, .01, and .005
if interaction:
model = 'pupil ~ log_velocity*side + (1+log_velocity*side|subject_nr)'
else:
model = 'pupil ~ log_velocity + (1+log_velocity|subject_nr)'
lm = lme4.lmer_series(dm, formula=model, winlen=3,
cacheid='lmer%s' % suffix)
for y, color1, color2, alpha in [
(.94, grey[2], blue[0], .05),
(.942, grey[3], blue[1], .01),
(.944, grey[4], blue[2], .005),
(.946, grey[5], blue[2], .001)
]:
a = series.threshold(lm.p, lambda p: p > 0 and p < alpha,
min_length=1)
plot.threshold(a[1], y=y, color=color1, linewidth=4)
if interaction:
plot.threshold(a[3], y=y+.01, color=color2, linewidth=4)
# Mark the baseline period
plt.axvspan(50, 90, color='black', alpha=.2)
# Mark the cue onset
plt.axvline(90, color='black')
# Adjust the x axis such that 0 is the cue onset, and the units are time in
# seconds (assuming 33 ms per sample)
plt.xticks(np.arange(40, 540, 50), np.arange(-50, 450, 50)*.033)
plt.xlim(0, 540)
plt.xlabel('Time since auditory cue (s)')
plt.ylabel('Pupil size (relative to baseline)')
plt.legend(frameon=False)
plot.save('pupiltrace'+suffix)
def trace_lmer_ratings(dm, dv='rating_brightness'):
"""TODO"""
dm = dm.type != 'animal'
print(dm.type.unique, dv)
lm = lme4.lmer_series(dm,
'pupil ~ (%(dv)s) + (1+%(dv)s|subject_nr)' \
% {'dv' : dv}, winlen=WINLEN, cacheid='lmer_series_ratings.%s' % dv)
threshold = series.threshold(lm.p,
lambda p: p > 0 and p<.05, min_length=200)
print('Alpha .05 (%.2f)' % series.reduce_(threshold)[1])
plt.plot(lm.t[1])
plot.threshold(threshold[1], color=blue[1], linewidth=1)
plt.show()
return lm
