Synaptic weight and k scaling

figures/MAM2EBRAINS/M2E_compute_corrcoeff.py

@@ -42,6 +42,7 @@ def compute_corrcoeff(M, data_path, label):
         LvR_list = []
         N = []
         for pop in M.structure[area]:
+            print(area, pop)
             fp = '-'.join((label,
                            'spikes',  # assumes that the default label for spike files was used
                            area,
@@ -54,7 +55,14 @@ def compute_corrcoeff(M, data_path, label):
             dat = ch.sort_gdf_by_id(spikes, idmin=ids[0], idmax=ids[0]+subsample+1000)
             bins, hist = ch.instantaneous_spike_count(dat[1], resolution, tmin=tmin, tmax=T)
             rates = ch.strip_binned_spiketrains(hist)[:subsample]
+            print(rates)
+            print("test")
+            print(rates.shape)
             cc = np.corrcoef(rates)
+            print(cc.shape)
+            print(cc[0].size)
+            # print(cc[0])
+            # print(cc)
             cc = np.extract(1-np.eye(cc[0].size), cc)
             cc[np.where(np.isnan(cc))] = 0.
             cc_dict[area][pop] = np.mean(cc)

figures/MAM2EBRAINS/M2E_visualize_firing_rate.py

@@ -19,7 +19,7 @@ def plot_firing_rate(M):
     # display the value of mean rate
     ax.text(0.7 * max(tsteps), mean_rate+2, f'Mean firing rate: {mean_rate:.2f}', fontsize=12)
 
-    ax.set_title('Instantaneous and mean firing rate across all populations', fontsize=15, pad=20)
+    ax.set_title('Instantaneous and mean firing rate across all populations', fontsize=15, pad=10)
     ax.set_xlabel('Time (ms)', fontsize=13)
     ax.set_ylabel('Firing rate (spikes / s)', fontsize=12)
     ax.set_xlim(0, M.simulation.params['t_sim'])

figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py

@@ -36,7 +36,7 @@ def visualize_interareal_connectivity(M):
     pl.rcParams['figure.figsize'] = (width, height)
 
     fig = pl.figure()
-    fig.suptitle('Interareal connectivity for full-scale (left) and down-scale (right) multi-area model', fontsize=16, y=1.05)
+    fig.suptitle('Interareal probabilistic connectivity for full-scale (left) and down-scaled (right) multi-area model', fontsize=16, y=1.05)
     axes = {}
 
     # gs1 = gridspec.GridSpec(2, 2)
@@ -189,8 +189,11 @@ def visualize_interareal_connectivity(M):
 
     ax.set_aspect(1. / ax.get_data_ratio())
 
-    masked_matrix_full_scale = np.ma.masked_values(conn_matrix_full_scale, 0.0)
-    cmap = pl.get_cmap('inferno')
+    # masked_matrix_full_scale = np.ma.masked_values(conn_matrix_full_scale, 0.0)
+    masked_matrix_full_scale = conn_matrix_full_scale
+    print(masked_matrix_full_scale[2])
+    # cmap = pl.get_cmap('inferno')
+    cmap = pl.get_cmap('YlOrBr')
     cmap.set_bad('w', 1.0)
 
     x = np.arange(0, len(area_list) + 1)
@@ -320,8 +323,10 @@ def visualize_interareal_connectivity(M):
 
     ax.set_aspect(1. / ax.get_data_ratio())
 
-    masked_matrix_down_scale = np.ma.masked_values(conn_matrix_down_scale, 0.0)
-    cmap = pl.get_cmap('inferno')
+    # masked_matrix_down_scale = np.ma.masked_values(conn_matrix_down_scale, 0.0)
+    masked_matrix_down_scale = conn_matrix_down_scale
+    print(masked_matrix_down_scale[2])
+    # cmap = pl.get_cmap('inferno')
     cmap.set_bad('w', 1.0)
 
     x = np.arange(0, len(area_list) + 1)

figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py

@@ -78,7 +78,8 @@ def plot_time_averaged_population_rates(M, data_path, area_list=None, **keywords
     ax.set_xticklabels(area_list, rotation=90, size=10.)
     ax.set_yticks(y_index)
     # ax.set_yticklabels(A.network.structure_reversed['V1'])
-    ax.set_yticklabels(M.structure['V1'][::-1])
+    # ax.set_yticklabels(M.structure['V1'][::-1])
+    ax.set_yticklabels(['6I', '6E', '5I', '5E', '4I', '4E', '2/3I', '2/3E'])
     ax.set_ylabel('Population', size=13)
     ax.set_xlabel('Area index', size=13)
     t = FixedLocator([0.01, 0.1, 1., 10., 100.])