/

figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_instantaneous_and_mean_firing_rates-checkpoint.py

@@ -8,9 +8,11 @@ def plot_instan_mean_firing_rate(M):
     rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
     
     # visualize calculate instantaneous and mean firing rates
+    nrows = 1
+    ncols = 1
     width = 10
     panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2.  # golden ratio
-
+    
     height = width / panel_wh_ratio * float(nrows) / ncols
     pl.rcParams['figure.figsize'] = (width, height)
 

figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_time_ave_pop_rates-checkpoint.py

@@ -63,7 +63,8 @@ def plot_time_averaged_population_rates(M):
     y_index = [a + 0.5 for a in y_index]
     # print(M.network.structure['V1'])
     ax.set_xticks(x_index)
-    ax.set_xticklabels(x_ticks)
+    # ax.set_xticklabels(x_ticks)
+    ax.set_xticklabels(x_ticks) = area_list
     ax.set_yticks(y_index)
     # ax.set_yticklabels(M.network.structure_reversed['V1'])
     ax.set_yticklabels(M.network.structure['V1'])

figures/MAM2EBRAINS/M2E_visualize_instantaneous_and_mean_firing_rates.py

@@ -8,9 +8,11 @@ def plot_instan_mean_firing_rate(M):
     rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
     
     # visualize calculate instantaneous and mean firing rates
+    nrows = 1
+    ncols = 1
     width = 10
     panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2.  # golden ratio
-
+    
     height = width / panel_wh_ratio * float(nrows) / ncols
     pl.rcParams['figure.figsize'] = (width, height)
 

figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py

@@ -63,7 +63,8 @@ def plot_time_averaged_population_rates(M):
     y_index = [a + 0.5 for a in y_index]
     # print(M.network.structure['V1'])
     ax.set_xticks(x_index)
-    ax.set_xticklabels(x_ticks)
+    # ax.set_xticklabels(x_ticks)
+    ax.set_xticklabels(x_ticks) = area_list
     ax.set_yticks(y_index)
     # ax.set_yticklabels(M.network.structure_reversed['V1'])
     ax.set_yticklabels(M.network.structure['V1'])