axes[4].set_yticks(-np.arange(16) * 100)
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')
#set some seed values
SEED = 12345678
SIMULATIONSEED = 12345678
np.random.seed(SEED)
################################################################################
## PARAMETERS
################################################################################
from cellsim16popsParams_modified_spontan import multicompartment_params, \
point_neuron_network_params
#Full set of parameters including network parameters
params = multicompartment_params()
#set up the file destination
setup_file_dest(params, clearDestination=True)
###############################################################################
# MAIN simulation procedure
###############################################################################
#tic toc
tic = time()
######## Perform network simulation ############################################
##initiate nest simulation with only the point neuron network parameter class
