/

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

@@ -12,9 +12,9 @@ def plot_instan_mean_firing_rate(M):
     fig, ax = pl.subplots()
     ax.plot(tsteps, rate)
     ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
-    ax.set_title('Instantaneous and mean firing rate across all populations')
-    ax.set_xlabel('time (ms)')
-    ax.set_ylabel('firing rate (spikes / s)')
+    ax.set_title('Instantaneous and mean firing rate across all populations', fontsize=15, y=1.05)
+    ax.set_xlabel('Time (ms)', fontsize=13)
+    ax.set_ylabel('Firing rate (spikes / s)', fontsize=13)
     ax.set_xlim(0, M.simulation.params['t_sim'])
     ax.set_ylim(0, 50)
     ax.legend()

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

@@ -35,6 +35,7 @@ def plot_time_averaged_population_rates(M, area_list=None, **keywords):
     matrix = np.zeros((len(area_list), len(A.network.structure['V1'])))
 
     fig = plt.figure(figsize=(16, 4))
+    fig.suptitle('Time-averaged population rates encoded in colors', fontsize=15, y=1.05)
     ax = fig.add_subplot(111)
 
     for i, area in enumerate(area_list):

figures/MAM2EBRAINS/M2E_visualize_instantaneous_and_mean_firing_rates.py

@@ -12,9 +12,9 @@ def plot_instan_mean_firing_rate(M):
     fig, ax = pl.subplots()
     ax.plot(tsteps, rate)
     ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
-    ax.set_title('Instantaneous and mean firing rate across all populations')
-    ax.set_xlabel('time (ms)')
-    ax.set_ylabel('firing rate (spikes / s)')
+    ax.set_title('Instantaneous and mean firing rate across all populations', fontsize=15, y=1.05)
+    ax.set_xlabel('Time (ms)', fontsize=13)
+    ax.set_ylabel('Firing rate (spikes / s)', fontsize=13)
     ax.set_xlim(0, M.simulation.params['t_sim'])
     ax.set_ylim(0, 50)
     ax.legend()

figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py

@@ -35,6 +35,7 @@ def plot_time_averaged_population_rates(M, area_list=None, **keywords):
     matrix = np.zeros((len(area_list), len(A.network.structure['V1'])))
 
     fig = plt.figure(figsize=(16, 4))
+    fig.suptitle('Time-averaged population rates encoded in colors', fontsize=15, y=1.05)
     ax = fig.add_subplot(111)
 
     for i, area in enumerate(area_list):