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 brightness_intensity(dm):
"""TODO"""
lm = lme4.lmer_series((dm.type == 'light') | (dm.type == 'dark'),
'pupil ~ type + rating_intensity + (1+type|subject_nr)',
winlen=WINLEN, cacheid='lmer_series_type_intensity')
threshold = series.threshold(lm.t, lambda t: abs(t) > 1.96, min_length=200)
print('threshold', threshold > 0)
plot.new(size=(8,6))
plt.plot(threshold)
plt.plot(lm.t[1], label='Type')
plt.plot(lm.t[2], label='Intensity')
plot.save('model-type-intensity')
lm = lme4.lmer_series(dm.type != 'animal',
'pupil ~ rating_brightness + rating_intensity + (1+rating_brightness|subject_nr)',
winlen=WINLEN, cacheid='lmer_series_brightness_intensity')
plot.new(size=(8,6))
plt.plot(lm.t[1], label='Brightness')
plt.plot(lm.t[2], label='Intensity')
plot.save('model-brightness-intensity')
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
def trace_lmer_simple(dm, iv='type'):
"""
desc:
Runs the main LME for each 10 ms window separately. Here the word type
(bright or dark) is a fixed, pupil size is a dependent measure, and the
model contains random by-participant slopes and intercepts.
arguments:
dm:
type: DataMatrix
keywords:
iv: The independent variable.
returns:
desc: A DataMatrix with statistical results.
"""
dm = (dm.type == 'light') | (dm.type == 'dark')
lm = lme4.lmer_series(dm,
'pupil ~ (%(iv)s) + (1+%(iv)s|subject_nr)' \
% {'iv' : iv}, winlen=WINLEN, cacheid='lmer_series_simple.%s' % iv)
return lm
def analyse(dm, model):
'''Analyses the parsed data'''
lm = lme4.lmer_series(dm, model)
return lm