Exemplo n.º 1
0
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
Exemplo n.º 2
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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')
Exemplo n.º 3
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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
Exemplo n.º 4
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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)
Exemplo n.º 6
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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)
Exemplo n.º 7
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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
Exemplo n.º 8
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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
Exemplo n.º 9
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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
Exemplo n.º 11
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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
Exemplo n.º 12
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def baseline(dm):

    return srs.reduce_(srs.window(dm.y, start=BASELINE[0], end=BASELINE[1]),
                       operation=REDUCEFNC)