def correct1(dm):
return srs.baseline(dm.y,
dm.y,
BASELINE[0],
BASELINE[1],
reduce_fnc=REDUCEFNC)
def preprocess(dm):
"""
desc:
Performs pupil preprocessing, and removes trials where pupil size was
unrealistic.
"""
dm.pupil = series.blinkreconstruct(dm.ptrace_target)
dm.pupil.depth = 3000
dm.pupil = series.smooth(dm.pupil, winlen=51)
dm.pupil = series.baseline(dm.pupil, dm.pupil, 0, 1)
# Remove all trials where pupil size has unrealistic values
dm.pupilmax = FloatColumn
dm.pupilmin = FloatColumn
for row in dm:
row.pupilmin = min(row.pupil)
row.pupilmax = max(row.pupil)
plot.new()
# plot.histogram(dm.pupilmin, bins=100, range=(0, 5), color=red[1], alpha=.5)
# plot.histogram(dm.pupilmax, bins=100, range=(0, 5), color=green[1], alpha=.5)
plt.hist(dm.pupilmin, bins=100, range=(0, 5), color=red[1], alpha=0.5)
plt.hist(dm.pupilmax, bins=100, range=(0, 5), color=green[1], alpha=0.5)
l0 = len(dm)
dm = (dm.pupilmin > PUPILRANGE[0]) & (dm.pupilmax < PUPILRANGE[1])
l1 = len(dm)
s = "%d of %d unrealistic values" % (l0 - l1, l0)
plt.title(s)
plot.save("pupil-peaks")
print(s)
return dm
def test_baseline():
dm = DataMatrix(length=2)
dm.series = SeriesColumn(depth=3)
dm.series[0] = range(3)
dm.series[1] = range(1, 4)
dm.baseline = SeriesColumn(depth=3)
dm.baseline[0] = range(1, 4)
dm.baseline[1] = range(3)
dm.norm = series.baseline(dm.series, dm.baseline)
check_series(dm.norm, [[-2, -1, 0], [0, 1, 2]])
check_integrity(dm)
def preprocess(dm):
"""
desc:
Do basic data preprocessing.
arguments:
dm:
type: DataMatrix
returns:
type: DataMatrix
"""
dm.pupil.depth = 540
dm.pupil = series.baseline(dm.pupil, baseline=dm.pupil, bl_start=50,
bl_end=90)
dm.pupil = series.smooth(dm.pupil, winlen=11)
dm.rename('snelheid', 'velocity')
dm.log_velocity = np.log10(dm.velocity)
return dm
run.clear()
else:
if os.path.isdir(path + "/.memoize/"):
print("Using existing parsed data (unless new files have been added).")
else: print("No existing directory with parsed data! Parsing files, this will take some time...")
dm = run()
print(dm)
# Here we set up the baseline to remove NaN values
dm.pupil = srs.endlock(dm.ptrace_rsvp)
dm.pupil = srs.baseline(
series= dm.pupil,
baseline=dm.ptrace_rsvp,
bl_start=0,
bl_end=2,
method='subtractive'
)
# Splitting datamatrix between the two type of stimuli
blueDM, redDM = ops.split(dm.target_object, "blue", "red")
# Plot for preprocessed trace
plt.figure()
plt.title('Preprocesed trace')
for tone, cdm in ops.split(redDM.tone_red):
colour = "#FF3333" if tone == "bright" else "#8B0000"
plot.trace(cdm.pupil, color=colour)
for tone, cdm in ops.split(blueDM.tone_blue):
