ax = fig.gca(projection='3d')
plt.title("Tuning Curve Width v %s, %s" % (var_pair[0], var_pair[1]))
ax.set_xlabel(var_pair[0])
ax.set_ylabel(var_pair[1])
ax.set_zlabel('tuning curve width')
# Plot separate tuning curve data for pyramidal and inhibitory neurons
for n in z_vals_all[0].keys():
if n == 'pyr':
c = 'blue'
plt_sty = 'b-'
else:
c = 'red'
plt_sty = 'r-'
z_vals = [dic[n] for dic in z_vals_all]
ax.plot_trisurf(x_vals, y_vals, z_vals, color=c)
# Reset variable to default value and increment
args[var_pair[0]] = args_defaults[0]
args[var_pair[1]] = args_defaults[1]
rt.inc_var(var_pair)
# Save the figure
if save_graphs:
plt.savefig('figures/%s_%s %s.png' \
% (var_pair[0], var_pair[1], strftime("%Y-%m-%d %H:%M")))
# Display results of all simulations
rt.final(send_msg)
if show_graphs:
plt.show()
tun_curve_w = [{} for i in test_range]
tun_curve_sem = [{} for i in test_range]
for i in range(len(firing_rates)):
for n in firing_rates[i].keys():
tuning_widths = [len(j) - j.count(0.0) for j in firing_rates[i][n]]
tun_curve_w[i][n] = np.mean(tuning_widths)
neuron_num = args['freq_num'] * args[n + '_layer_num']
tun_curve_sem[i][n] = stats.sem(tuning_widths)
###########################################
#### GRAPHS ###########################
###########################################
# Basic plot setup
plt.figure(len(rt.completed_vars) + 1)
rt.inc_var(variable)
plt.title("Tuning Curve Width v %s" % variable)
plt.xlabel(variable)
plt.ylabel('tuning curve width')
# Plot separate tuning curve data for pyramidal and inhibitory neurons
for n in firing_rates[0].keys():
if n == 'pyr':
plt_sty = 'b-'
else:
plt_sty = 'r-'
y_axis = [dic[n] for dic in tun_curve_w]
plt.plot(test_range, y_axis, plt_sty)
plt.errorbar(test_range,
y_axis,
yerr=[dic[n] for dic in tun_curve_sem],