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
#=========================================================================================
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
#=========================================================================================
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']
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(
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)