diff --git a/figures/Schmidt2018_dyn/plotstyle.rc b/figures/Schmidt2018_dyn/plotstyle.rc
index 73563a3f77558a3c7396dc1e8335aac5c2786cf4..ded0ecfe4185f653fddd6ab3988b75a8db893539 100644
--- a/figures/Schmidt2018_dyn/plotstyle.rc
+++ b/figures/Schmidt2018_dyn/plotstyle.rc
@@ -50,5 +50,4 @@ ps.useafm : False # use of afm fonts, results in small files
 ps.fonttype : 3 # Output Type 3 (Type3) or Type 42 (TrueType)
 
 # set different default color cycle
-# axes.color_cycle : 4c72b0, 55a868, c44e52, 8172b2, ccb974, 64b5cd
-axes.prop_cycle: cycler('color', ['4c72b0', '55a868', 'c44e52', '8172b2', 'ccb974', '64b5cd'])
\ No newline at end of file
+axes.color_cycle : 4c72b0, 55a868, c44e52, 8172b2, ccb974, 64b5cd
\ No newline at end of file
diff --git a/multiarea_model/.ipynb_checkpoints/analysis-checkpoint.py b/multiarea_model/.ipynb_checkpoints/analysis-checkpoint.py
index 5102f3c9855d5ec9def442a0c1381b70e6f14ffb..bce048ff226b7bc6baec59b12332524a16f64895 100644
--- a/multiarea_model/.ipynb_checkpoints/analysis-checkpoint.py
+++ b/multiarea_model/.ipynb_checkpoints/analysis-checkpoint.py
@@ -443,7 +443,8 @@ class Analysis:
                                                           params['resolution'],
                                                           kernel=params['kernel'])
                 else:
-                    time_series = np.nan*np.ones(int(params['t_max'] - params['t_min']))
+                    time_series = np.nan*np.ones(params['t_max'] - params['t_min'])
+                    # time_series = np.nan*np.ones(int(params['t_max'] - params['t_min']))
                 d_pops[area][pop] = time_series
 
                 total_spikes = ah.area_spike_train(self.spike_data[area])
@@ -898,7 +899,8 @@ class Analysis:
 
         for i, area in enumerate(area_list):
             print(i, area)
-            for j, pop in enumerate(self.network.structure['V1']):
+            for j, pop in enumerate(self.network.structure_reversed['V1']):
+            # for j, pop in enumerate(self.network.structure['V1']):
                 if pop in self.network.structure[area]:
                     rate = self.pop_rates[area][pop][0]
                     if rate == 0.0:
@@ -931,6 +933,7 @@ class Analysis:
         ax.set_xticks(x_index)
         ax.set_xticklabels(x_ticks)
         ax.set_yticks(y_index)
+        ax.set_yticklabels(self.network.structure_reversed['V1'])
         ax.set_ylabel('Population', size=18)
         ax.set_xlabel('Area index', size=18)
         t = FixedLocator([0.01, 0.1, 1., 10., 100.])
@@ -951,7 +954,6 @@ class Analysis:
         Saves all post-processed data to files.
         """
         members = inspect.getmembers(self)
-        print(members)
         save_list_json = ['structure', 'pop_rates', 'synchrony',
                           'pop_cv_isi', 'pop_LvR',
                           'indegree_data', 'indegree_areas_data',
diff --git a/multiarea_model/analysis.py b/multiarea_model/analysis.py
index 9c1fd318f52697bd90815dc6829c084ff6b06eab..bce048ff226b7bc6baec59b12332524a16f64895 100644
--- a/multiarea_model/analysis.py
+++ b/multiarea_model/analysis.py
@@ -443,7 +443,8 @@ class Analysis:
                                                           params['resolution'],
                                                           kernel=params['kernel'])
                 else:
-                    time_series = np.nan*np.ones(int(params['t_max'] - params['t_min']))
+                    time_series = np.nan*np.ones(params['t_max'] - params['t_min'])
+                    # time_series = np.nan*np.ones(int(params['t_max'] - params['t_min']))
                 d_pops[area][pop] = time_series
 
                 total_spikes = ah.area_spike_train(self.spike_data[area])
@@ -898,7 +899,8 @@ class Analysis:
 
         for i, area in enumerate(area_list):
             print(i, area)
-            for j, pop in enumerate(self.network.structure['V1']):
+            for j, pop in enumerate(self.network.structure_reversed['V1']):
+            # for j, pop in enumerate(self.network.structure['V1']):
                 if pop in self.network.structure[area]:
                     rate = self.pop_rates[area][pop][0]
                     if rate == 0.0:
@@ -931,6 +933,7 @@ class Analysis:
         ax.set_xticks(x_index)
         ax.set_xticklabels(x_ticks)
         ax.set_yticks(y_index)
+        ax.set_yticklabels(self.network.structure_reversed['V1'])
         ax.set_ylabel('Population', size=18)
         ax.set_xlabel('Area index', size=18)
         t = FixedLocator([0.01, 0.1, 1., 10., 100.])