code cleaning

figures/MAM2EBRAINS/M2E_compute_corrcoeff.py

@@ -32,11 +32,8 @@ def compute_corrcoeff(M, data_path, label):
     resolution = 1.
 
     cc_dict = {}
-
     for area in areas_simulated:
         cc_dict[area] = {}
-        LvR_list = []
-        N = []
         for pop in M.structure[area]:
             fp = '-'.join((label,
                            'spikes',  # assumes that the default label for spike files was used

figures/MAM2EBRAINS/M2E_compute_pop_LvR.py

@@ -32,8 +32,6 @@ def compute_pop_LvR(M, data_path, label):
 
     for area in areas_simulated:
         pop_LvR_dict[area] = {}
-        LvR_list = []
-        N = []
         for pop in M.structure[area]:
             fp = '-'.join((label,
                            'spikes',  # Assumes that the default label for spike files was used

figures/MAM2EBRAINS/M2E_compute_rate_time_series.py

@@ -52,7 +52,7 @@ def compute_rate_time_series(M, data_path, label, area, method):
     N_list = []
     for pop in M.structure[area]:
         fp = '-'.join((label,
-                       'spikes',  # assumes that the default label for spike files was used
+                       'spikes',  # Assumes that the default label for spike files was used
                        area,
                        pop))
         fn = '{}/{}.npy'.format(load_path, fp)

figures/MAM2EBRAINS/M2E_firing_rate.py

@@ -73,7 +73,6 @@ def plot_firing_rate_over_areas(M, data_path):
 
     t_min = 500.
     t_max = M.simulation.params['t_sim']
-    time = np.arange(t_min, t_max)
     matrix = []
     for area in area_list:
         binned_spikes = rate_time_series[area][500:600]

figures/MAM2EBRAINS/M2E_visualize_fc.py

@@ -90,7 +90,6 @@ def visualize_fc(M, data_path):
     Returns:
         None
     """
-    area_list = M.simulation.params["areas_simulated"]
     label = M.simulation.label
     
     # Compute functional connectivity