diff --git a/README.md b/README.md
index e5cb27c..fd08a80 100644
--- a/README.md
+++ b/README.md
@@ -13,6 +13,8 @@
     
     - see `journal_robustness.py` for robustness test modeled after cristians robustness-exp (with the exeption that we put noise on the weights). Has `synthetic` bool to switch to hand-modeled perfect fixpoint instead of naturally trained ones. 
 
+    - Also added two difference between the "time-as-fixpoint" and "time-to-verge" (i.e. to divergence / zero).
+
     - We might need to consult about the "average loss per application step", as I think application loss get gradually higher the worse the weights get. So the average might not tell us much here.
 
 - [ ] Adjust Self Training so that it favors second order fixpoints-> Second order test implementation (?)
diff --git a/journal_robustness.py b/journal_robustness.py
index b456d28..51b82d5 100644
--- a/journal_robustness.py
+++ b/journal_robustness.py
@@ -70,7 +70,7 @@ class RobustnessComparisonExperiment:
         self.id_functions = []
         self.populate_environment()
         self.count_fixpoints()
-        self.data = self.test_robustness()
+        self.time_to_vergence, self.time_as_fixpoint = self.test_robustness()
 
         self.save()
 
@@ -82,13 +82,13 @@ class RobustnessComparisonExperiment:
             
             if self.synthetic:
                 ''' Either use perfect / hand-constructed fixpoint ... '''
-                net_name = f"ST_net_{str(i)}_synthetic"
+                net_name = f"net_{str(i)}_synthetic"
                 net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
                 net.apply_weights(generate_fixpoint_weights())
             
             else:
                 ''' .. or use natural approach to train fixpoints from random initialisation. '''
-                net_name = f"ST_net_{str(i)}"
+                net_name = f"net_{str(i)}"
                 net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
                 for _ in range(self.epochs):
                     for _ in range(self.ST_steps):
@@ -98,7 +98,8 @@ class RobustnessComparisonExperiment:
 
 
     def test_robustness(self, print_it=True):
-        data = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
+        time_to_vergence = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
+        time_as_fixpoint = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
         avg_loss_per_application = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
         noise_range = range(10)
         row_headers = []
@@ -115,19 +116,24 @@ class RobustnessComparisonExperiment:
                 clone = self.apply_noise(clone, rand_noise)
                 
                 while not is_zero_fixpoint(clone) and not is_divergent(clone):
+                    if is_identity_function(clone):
+                        time_as_fixpoint[i][noise_level] += 1    
+                    
                     # Todo: what kind of comparison between application? -> before   
                     clone.self_application(1, self.log_step_size)                    
-                    data[i][noise_level] += 1
+                    time_to_vergence[i][noise_level] += 1
                     # -> after
         
         if print_it:
-            print(f"Number appplications steps: ")
             col_headers = [str(f"10e-{d}") for d in noise_range]
-            print(tabulate(data, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
-        
-            # other tables here
             
-        return data
+            print(f"\nAppplications steps until divergence / zero: ")
+            print(tabulate(time_to_vergence, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
+            
+            print(f"\nTime as fixpoint: ")
+            print(tabulate(time_as_fixpoint, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
+
+        return time_as_fixpoint, time_to_vergence
 
 
     def count_fixpoints(self):
@@ -156,7 +162,7 @@ if __name__ == "__main__":
     ST_steps = 1000
     ST_epochs = 5
     ST_log_step_size = 10
-    ST_population_size = 3
+    ST_population_size = 5
     ST_net_hidden_size = 2
     ST_net_learning_rate = 0.04
     ST_name_hash = random.getrandbits(32)