yall.append(mante.plot_unit(unit, sortedfile, mante_plots, t0=stimulus_start,
                                tmin=stimulus_start, tmax=stimulus_end,
                                sortby_fontsize=sortby_fontsize, unit_fontsize=6))

# Shared x tick labels
for i in xrange(len(units)):
    plots[str(i)+'_choice'].xticklabels()
    plots[str(i)+'_motion_choice'].xticklabels()
    plots[str(i)+'_colour_choice'].xticklabels()

# Shared y limits
shared_plots = []
for i in xrange(len(units)):
    shared_plots += [plots[str(i)+'_'+s] for s
                     in ['choice', 'motion_choice', 'colour_choice', 'context_choice']]
ylim = fig.shared_lim(shared_plots, 'y', yall)

# Shared y tick labels
plots['0_choice'].yticks([0])
plots['0_motion_choice'].yticks([0])
plots['0_colour_choice'].yticks([0])
plots['0_context_choice'].yticks([0])
for i in xrange(1, len(units)):
    for s in ['choice', 'motion_choice', 'colour_choice', 'context_choice']:
        plots[str(i)+'_'+s].yticks(plots['0_'+s].get_yticks())
        plots[str(i)+'_'+s].yticklabels()

#=========================================================================================
# Regression coefficients
#=========================================================================================
Exemplo n.º 2
0
                        unit_fontsize=6))

# Shared x tick labels
for i in xrange(len(units)):
    plots[str(i) + '_choice'].xticklabels()
    plots[str(i) + '_motion_choice'].xticklabels()
    plots[str(i) + '_colour_choice'].xticklabels()

# Shared y limits
shared_plots = []
for i in xrange(len(units)):
    shared_plots += [
        plots[str(i) + '_' + s] for s in
        ['choice', 'motion_choice', 'colour_choice', 'context_choice']
    ]
ylim = fig.shared_lim(shared_plots, 'y', yall)

# Shared y tick labels
plots['0_choice'].yticks([0])
plots['0_motion_choice'].yticks([0])
plots['0_colour_choice'].yticks([0])
plots['0_context_choice'].yticks([0])
for i in xrange(1, len(units)):
    for s in ['choice', 'motion_choice', 'colour_choice', 'context_choice']:
        plots[str(i) + '_' + s].yticks(plots['0_' + s].get_yticks())
        plots[str(i) + '_' + s].yticklabels()

#=========================================================================================
# Regression coefficients
#=========================================================================================
Exemplo n.º 3
0
    if modality == 'v':
        plot_inputs(trial, 'v', all)
        v = True
    elif modality == 'a':
        plot_inputs(trial, 'a', all)
        a = True
    elif modality == 'va':
        plot_inputs(trial, 'va', all)
        va = True

    if v and a and va:
        break

# Shared axes
names = ['v_v', 'v_a', 'a_v', 'a_a', 'va_v', 'va_a']
fig.shared_lim([plots[p] for p in names], 'y', [0, 1.75], margin=0)
for name in names:
    plot = plots[name]
    plot.xlim(tmin - t0, tmax - t0)
    plot.xticks([tmin - t0, 0, tmax - t0])
    plot.yticks([0, 1])
    if not name.startswith('v_'):
        plot.yticklabels()
    if name != 'v_a':
        plot.xticklabels()

#=========================================================================================
# Psychometric functions
#=========================================================================================

plot = plots['psy']
    if modality == 'v':
        plot_inputs(trial, 'v', all)
        v = True
    elif modality == 'a':
        plot_inputs(trial, 'a', all)
        a = True
    elif modality == 'va':
        plot_inputs(trial, 'va', all)
        va = True

    if v and a and va:
        break

# Shared axes
names = ['v_v', 'v_a', 'a_v', 'a_a', 'va_v', 'va_a']
fig.shared_lim([plots[p] for p in names], 'y', [0, 1.75], margin=0)
for name in names:
    plot = plots[name]
    plot.xlim(tmin-t0, tmax-t0)
    plot.xticks([tmin-t0, 0, tmax-t0])
    plot.yticks([0, 1])
    if not name.startswith('v_'):
        plot.yticklabels()
    if name != 'v_a':
        plot.xticklabels()

#=========================================================================================
# Psychometric functions
#=========================================================================================

plot = plots['psy']
Exemplo n.º 5
0
plot = plots[">"]
plot.plot(t, rnn.u[0], color=Figure.colors("orange"), lw=0.5)
yall.append(rnn.u[0])
plot.xticklabels()

trial_args = {"name": "test", "catch": False, "fpair": (34, 26), "gt_lt": "<"}
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)

# f1 < f2
plot = plots["<"]
plot.plot(t, rnn.u[0], color=Figure.colors("purple"), lw=0.5)
yall.append(rnn.u[0])

# Shared axes
input_plots = [plots[">"], plots["<"]]
ymin, ymax = fig.shared_lim(input_plots, "y", yall, lower=0)
for plot in input_plots:
    plot.xlim(t[0], t[-1])
    plot.xticks([0, 1, 2, 3, 4])
    plot.yticks([0, 1])

# Delay epoch
plot = plots[">"]
plot.plot(delay, 1.1 * ymax * np.ones(2), color="k", lw=1.5)
plot.text(np.mean(delay), 1.15 * ymax, "Delay", ha="center", va="bottom", fontsize=5.5)

# Conditions
plots[">"].text(
    np.mean(delay), 0.9 * ymax, "$f_1 > f_2$", ha="center", va="top", color=Figure.colors("orange"), fontsize=6
)
plots["<"].text(
Exemplo n.º 6
0
trial_args = {
    'name':  'test',
    'catch': False,
    'fpair': (34, 26),
    'gt_lt': '<'
    }
info = rnn.run(inputs=(trial_func, trial_args), rng=rng)

# f1 < f2
plot = plots['<']
plot.plot(t, rnn.u[0], color=Figure.colors('purple'), lw=0.5)
yall.append(rnn.u[0])

# Shared axes
input_plots = [plots['>'], plots['<']]
ymin, ymax = fig.shared_lim(input_plots, 'y', yall, lower=0)
for plot in input_plots:
    plot.xlim(t[0], t[-1])
    plot.xticks([0, 1, 2, 3, 4])
    plot.yticks([0, 1])

# Delay epoch
plot = plots['>']
plot.plot(delay, 1.1*ymax*np.ones(2), color='k', lw=1.5)
plot.text(np.mean(delay), 1.15*ymax, 'Delay', ha='center', va='bottom', fontsize=5.5)

# Conditions
plots['>'].text(np.mean(delay), 0.9*ymax, '$f_1 > f_2$', ha='center', va='top',
                color=Figure.colors('orange'), fontsize=6)
plots['<'].text(np.mean(delay), 0.9*ymax, '$f_1 < f_2$', ha='center', va='top',
                color=Figure.colors('purple'), fontsize=6)