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))