def create_report_delayed(exp_id, delayed, description):
delays = numpy.array(sorted(delayed.keys()))
r = Report(exp_id)
r.text("description", description)
f = r.figure(cols=3)
# max and sum of correlation for each delay
# corr_max = []
corr_mean = []
for delay in delays:
data = delayed[delay]
a = data["action_image_correlation"]
id = "delay%d" % delay
# rr = r.node('delay%d' % delay)
r.data(id, a).data_rgb("retina", add_reflines(posneg(values2retina(a))))
corr_mean.append(numpy.abs(a).mean())
caption = "delay: %d (max: %.3f, sum: %f)" % (delay, numpy.abs(a).max(), numpy.abs(a).sum())
f.sub(id, caption=caption)
timestamp2ms = lambda x: x * (1.0 / 60) * 1000
peak = numpy.argmax(corr_mean)
peak_ms = timestamp2ms(delays[peak])
with r.data_pylab("mean") as pylab:
T = timestamp2ms(delays)
pylab.plot(T, corr_mean, "o-")
pylab.ylabel("mean correlation field")
pylab.xlabel("delay (ms) ")
a = pylab.axis()
pylab.plot([0, 0], [a[2], a[3]], "k-")
y = a[2] + (a[3] - a[2]) * 0.1
pylab.text(+5, y, "causal", horizontalalignment="left")
pylab.text(-5, y, "non causal", horizontalalignment="right")
pylab.plot([peak_ms, peak_ms], [a[2], max(corr_mean)], "b--")
y = a[2] + (a[3] - a[2]) * 0.2
pylab.text(peak_ms + 10, y, "%d ms" % peak_ms, horizontalalignment="left")
f = r.figure("stats")
f.sub("mean")
a = delayed[int(delays[peak])]["action_image_correlation"]
r.data_rgb("best_delay", add_reflines(posneg(values2retina(a))))
return r
def add_scaled(report, id, x, **kwargs):
n = report.data(id, x)
n.data_rgb('retina',
add_reflines(scale(values2retina(x), min_value=0, **kwargs)))
#with n.data_pylab('plot') as pylab:
# pylab.plot(x, '.')
return n
def add_posneg(report, id, x, **kwargs):
n = report.data(id, x)
n.data_rgb('retina',
add_reflines(posneg(values2retina(x), **kwargs)))
#with n.data_pylab('plot') as pylab:
# pylab.plot(x, '.')
return n
