if show_xlabels:
axes[4].set_xlabel(r'$\sigma^2$ (mV$^2$)', va='center')
axes[4].set_title('LFP variance', va='baseline')
axes[4].legend(bbox_to_anchor=(1.37, 1.0), frameon=False)
axes[4].set_xlim(left=1E-7)
phlp.remove_axis_junk(axes[4])
phlp.annotate_subplot(axes[4], ncols=1, nrows=1, letter='E')
return fig
if __name__ == '__main__':
plt.close('all')
params = multicompartment_params()
ana_params = analysis_params.params()
ana_params.set_PLOS_2column_fig_style(ratio=1)
fig, axes = plt.subplots(2, 5)
fig.subplots_adjust(left=0.06,
right=0.96,
wspace=0.4,
hspace=0.2,
bottom=0.05,
top=0.95)
# params.figures_path = os.path.join(params.savefolder, 'figures')
# params.populations_path = os.path.join(params.savefolder, 'populations')
# params.spike_output_path = os.path.join(params.savefolder,
# 'processed_nest_output')
# params.networkSimParams['spike_output_path'] = params.spike_output_path
ax.set_title(panel_titles[4], va='baseline')
phlp.annotate_subplot(ax, ncols=1, nrows=1, letter=panel_labels[4])
ax.legend(bbox_to_anchor=(1.3, 1.0), frameon=False)
ax.set_yticklabels([])
#return fig
if __name__ == '__main__':
plt.close('all')
params = multicompartment_params()
ana_params = analysis_params.params()
ana_params.set_PLOS_2column_fig_style(ratio=1)
fig, axes = plt.subplots(2,5)
fig.subplots_adjust(left=0.06, right=0.96, wspace=0.4, hspace=0.16, bottom=0.05, top=0.95)
fig_exc_inh_contrib(fig, axes[0], params,
savefolders=['simulation_output_modified_ac_exc',
'simulation_output_modified_ac_inh',
'simulation_output_modified_ac_input'],
T=[800, 1000], transient=200, panel_labels='ABCDE',
show_xlabels=False)
fig_exc_inh_contrib(fig, axes[1], params,
savefolders=['simulation_output_modified_regular_exc',
'simulation_output_modified_regular_inh',
'simulation_output_modified_regular_input'],