Exemplo n.º 1
0
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_smooth():

    dm = DataMatrix(length=2)
    dm.series = SeriesColumn(depth=6)
    dm.series[0] = range(6)
    dm.series[1] = [0, 1, 2] * 2
    dm.s = series.smooth(dm.series, winlen=3, wintype='flat')
    check_series(
        dm.s,
        [[2. / 3, 1, 2, 3, 4, 4 + 1. / 3], [2. / 3, 1, 1, 1, 1, 1 + 1. / 3]])
    check_integrity(dm)
Exemplo n.º 3
0
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