def effect_se(dm, start=0, end=None):
"""
desc:
Gets the standard error of the differencein pupil size between dark and
bright trials during a time window.
arguments:
dm:
type: DataMatrix
keywords:
start: The start time.
end: The end time, or None for trace end.
returns:
type:
ndarrray
"""
dm_bright = dm.type == "light"
dm_dark = dm.type == "dark"
bright = series.reduce_(series.window(dm_bright.pupil, start=start, end=end))
dark = series.reduce_(series.window(dm_dark.pupil, start=start, end=end))
diff = dark.mean - bright.mean
se = ((bright.std ** 2 / len(bright)) + (dark.std ** 2 / len(dark))) ** 0.5
return diff, se
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 lmer_series(dm, formula, winlen=1):
col = formula.split()[0]
depth = dm[col].depth
rm = None
for i in range(0, depth, winlen):
wm = dm[:]
wm[col] = series.reduce_(
series.window(wm[col], start=i, end=i+winlen))
lm = lmer(wm, formula)
print('Sample %d' % i)
print(lm)
if rm is None:
rm = DataMatrix(length=len(lm))
rm.effect = list(lm.effect)
rm.p = SeriesColumn(depth=depth)
rm.t = SeriesColumn(depth=depth)
rm.est = SeriesColumn(depth=depth)
rm.se = SeriesColumn(depth=depth)
for lmrow, rmrow in zip(lm, rm):
rmrow.p[i:i+winlen] = lmrow.p
rmrow.t[i:i+winlen] = lmrow.t
rmrow.est[i:i+winlen] = lmrow.est
rmrow.se[i:i+winlen] = lmrow.se
return rm
def glmer_series(dm, formula, family, winlen=1):
col = formula.split()[0]
depth = dm[col].depth
rm = None
for i in range(0, depth, winlen):
wm = dm[:]
wm[col] = series.reduce_(
series.window(wm[col], start=i, end=i + winlen))
lm = glmer(wm, formula, family=family)
print('Sample %d' % i)
print(lm)
if rm is None:
rm = DataMatrix(length=len(lm))
rm.effect = list(lm.effect)
rm.p = SeriesColumn(depth=depth)
rm.z = SeriesColumn(depth=depth)
rm.est = SeriesColumn(depth=depth)
rm.se = SeriesColumn(depth=depth)
for lmrow, rmrow in zip(lm, rm):
rmrow.p[i:i + winlen] = lmrow.p
rmrow.z[i:i + winlen] = lmrow.z
rmrow.est[i:i + winlen] = lmrow.est
rmrow.se[i:i + winlen] = lmrow.se
return rm
def test_reduce_():
dm = DataMatrix(length=2)
dm.series = SeriesColumn(depth=3)
dm.series[0] = 1, 2, 3
dm.series[1] = 2, 3, 4
dm.col = series.reduce_(dm.series)
check_col(dm.col, [2, 3])
check_integrity(dm)
def descriptives(dm): """ desc: Provides basic descriptives of response times. These are printed directly to the stdout. arguments: dm: type: DataMatrix """ ops.keep_only(dm, cols=['type', 'rt']) gm = ops.group(dm, by=[dm.type]) gm.mean_rt = series.reduce_(gm.rt) gm.se_rt = series.reduce_(gm.rt, lambda x: np.nanstd(x)/np.sqrt(len(x))) gm.n = series.reduce_(gm.rt, lambda x: np.sum(~np.isnan(x))) del gm.rt print(gm)
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 size(dm, start=0, end=None):
"""
desc:
Gets the mean pupil size during a time window.
arguments:
dm:
type: DataMatrix
keywords:
start: The start time.
end: The end time, or None for trace end.
returns:
type:
ndarrray
"""
return series.reduce_(series.window(dm.pupil, start=start, end=end)).mean
def size_se(dm, start=0, end=None):
"""
desc:
Gets the pupil-size standard error during a time window.
arguments:
dm:
type: DataMatrix
keywords:
start: The start time.
end: The end time, or None for trace end.
returns:
type:
ndarrray
"""
s = series.reduce_(series.window(dm.pupil, start=start, end=end))
return s.mean, s.std / len(s) ** 0.5
def interactiontest(dm):
dm.bl = baseline.baseline(dm)
dm.test = srs.reduce_(srs.window(dm.y, start=-50, end=-1))
lmdata = DataMatrix(length=len(dm) * 2)
lmdata.pupil = -1
lmdata.time = -1
lmdata.condition = -1
lmdata.trialid = -1
for i, row in enumerate(dm):
lmdata.pupil[2 * i] = row.bl
lmdata.time[2 * i] = 0
lmdata.condition[2 * i] = row.c
lmdata.trialid[2 * i] = i
lmdata.pupil[2 * i + 1] = row.test
lmdata.time[2 * i + 1] = 1
lmdata.condition[2 * i + 1] = row.c
lmdata.trialid[2 * i + 1] = i
lm = lme4.lmer(lmdata, 'pupil ~ condition*time + (1|trialid)')
print(lm.p[3])
return lm.p[3] < ALPHA
def ttest(dm, col):
a1 = srs.reduce_(srs.window((dm.c == 1)[col], start=-50, end=-1))
b1 = srs.reduce_(srs.window((dm.c == 2)[col], start=-50, end=-1))
t, p = stats.ttest_ind(a1, b1)
return p < ALPHA and t < 0, p < ALPHA and t > 0
def baseline(dm):
return srs.reduce_(srs.window(dm.y, start=BASELINE[0], end=BASELINE[1]),
operation=REDUCEFNC)