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
def main():
sigma_deg = 6
kernel1 = get_contrast_kernel(sigma_deg=sigma_deg, eyes_interact=True)
kernel2 = get_contrast_kernel(sigma_deg=sigma_deg, eyes_interact=False) # better
kernel1 = kernel1.astype('float32')
kernel2 = kernel2.astype('float32')
meany = Expectation()
ex1 = Expectation()
ex2 = Expectation()
cp = ClientProcess()
cp.config_use_white_arena()
cp.config_stimulus_xml(example_stim_xml)
#position = [0.15, 0.5, 0.25]
position = [0.35, 0.5, 0.25]
linear_velocity_body = [0, 0, 0]
angular_velocity_body = [0, 0, 0]
#from flydra_render.contrast import intrinsic_contrast
from fast_contrast import intrinsic_contrast #@UnresolvedImport
N = 360
pb = progress_bar('Computing contrast', N)
orientation = numpy.linspace(0, 2 * numpy.pi, N)
for i, theta in enumerate(orientation):
attitude = rotz(theta)
pb.update(i)
res = cp.render(position, attitude,
linear_velocity_body, angular_velocity_body)
y = numpy.array(res['luminance']).astype('float32')
meany.update(y)
#y = numpy.random.rand(1398)
c1 = intrinsic_contrast(y, kernel1)
c2 = intrinsic_contrast(y, kernel2)
ex1.update(c1)
ex2.update(c2)
r = Report()
r.data_rgb('meany', scale(values2retina(meany.get_value())))
r.data_rgb('mean1', plot_contrast(ex1.get_value()))
r.data_rgb('mean2', plot_contrast(ex2.get_value()))
r.data_rgb('one-y', (plot_luminance(y)))
r.data_rgb('one-c1', plot_contrast(c1))
r.data_rgb('one-c2', plot_contrast(c2))
r.data_rgb('kernel', scale(values2retina(kernel2[100, :])))
f = r.figure(shape=(2, 3))
f.sub('one-y', 'One random image')
f.sub('one-c1', 'Contrast of random image')
f.sub('one-c2', 'Contrast of random image')
f.sub('meany', 'Mean luminance')
f.sub('mean1', 'Mean over %s samples' % N)
f.sub('mean2', 'Mean over %s samples' % N)
f.sub('kernel')
filename = 'compute_contrast_demo.html'
print("Writing on %s" % filename)
r.to_html(filename)
def plot_ret(x):
pylab.imshow( values2retina(x))
