diff --git a/code/13_pm-case-clusters.py b/code/13_pm-case-clusters.py
index fbc49ab..e1d3b81 100644
--- a/code/13_pm-case-clusters.py
+++ b/code/13_pm-case-clusters.py
@@ -6,12 +6,12 @@
 # input:  results/eventlogs_2019_case-clusters.csv
 # output: results/eval_case_clusters.csv
 #
-# last mod: 2024-04-02
+# last mod: 2024-04-04
 
 import pm4py
 import pandas as pd
 
-from python_helpers import eval_pm
+from python_helpers import eval_pm, eval_append
 
 #--------------- (1) Load data and create event logs ---------------
 
@@ -24,29 +24,30 @@ event_log = pm4py.format_dataframe(dat,
 
 #--------------- (2) Evaluation for clusters ---------------
 
-thresholds = [0.1, 0.2, 0.3, 0.4, 0.5]
+thresholds = [0.0, 0.1, 0.2, 0.3, 0.4]
 
 for nt in thresholds:
 
-  net, im, fm = pm4py.discover_petri_net_inductive(event_log, noise_threshold = nt)
-  
-  eval = pd.DataFrame(eval_pm(event_log, net, im, fm)).T
-  eval.columns = ["fitness", "generalization", "simplicity"]
-  #eval.columns = ["fitness", "precision", "generalization", "simplicity"]
-  
-  # Merge clusters into data frame
-  for cluster in [1, 2, 3, 4, 5]:
-      log_clst = pm4py.filter_event_attribute_values(event_log, "cluster", [cluster])
-      net_clst, im_clst, fm_clst = pm4py.discover_petri_net_inductive(log_clst, noise_threshold = nt)
-      eval_clst = pd.DataFrame(eval_pm(log_clst, net_clst, im_clst, fm_clst)).T
-      eval_clst.columns = ["fitness", "generalization", "simplicity"]
-      #eval_clst.columns = ["fitness", "precision", "generalization", "simplicity"]
-      eval = pd.concat([eval, eval_clst])
-      # Export process maps
-      bpmn = pm4py.convert.convert_to_bpmn(net_clst, im_clst, fm_clst)
-      pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_cluster" + str(cluster) +
-            "_cases" + str(int(nt*10)).zfill(2) + ".png")
-  
-  eval.index = ["Complete", "Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5"]
-  eval.to_csv("results/eval_case_clusters_" + str(int(nt*10)).zfill(2) + ".csv", sep = ";")
+    net, im, fm = pm4py.discover_petri_net_inductive(event_log, noise_threshold = nt)
+
+    eval = eval_append(event_log, net, im, fm)
+
+    # Export process maps
+    pm4py.save_vis_petri_net(net, im, fm, "results/processmaps/petrinet_complete_cases" + str(int(nt*10)).zfill(2) + ".png")
+    bpmn = pm4py.convert.convert_to_bpmn(net, im, fm)
+    pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_complete_cases" + str(int(nt*10)).zfill(2) + ".png")
+
+    # Merge clusters into data frame
+    for cluster in [1, 2, 3, 4, 5]:
+        log_clst = pm4py.filter_event_attribute_values(event_log, "cluster", [cluster])
+        net_clst, im_clst, fm_clst = pm4py.discover_petri_net_inductive(log_clst, noise_threshold = nt)
+        eval_clst = eval_append(log_clst, net_clst, im_clst, fm_clst)
+        eval = pd.concat([eval, eval_clst])
+        # Export process maps
+        pm4py.save_vis_petri_net(net_clst, im_clst, fm_clst, "results/processmaps/petrinet_cluster" + str(cluster) + "_cases" + str(int(nt*10)).zfill(2) + ".png")
+        bpmn = pm4py.convert.convert_to_bpmn(net_clst, im_clst, fm_clst)
+        pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_cluster" + str(cluster) + "_cases" + str(int(nt*10)).zfill(2) + ".png")
+
+    eval.index = ["Complete", "Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5"]
+    eval.to_csv("results/eval_case_clusters_" + str(int(nt*10)).zfill(2) + ".csv", sep = ";")
 
diff --git a/code/python_helpers.py b/code/python_helpers.py
index 783c30b..d2962ca 100644
--- a/code/python_helpers.py
+++ b/code/python_helpers.py
@@ -4,18 +4,18 @@ import pandas as pd
 ###### Extract metadata for petri nets on filtered logs ######
 
 def eval_pm(data, net, initial_marking, final_marking):
-    """Caculate fitness, precision, generalizability, and simplicity for petri net"""
+    """Caculate fitness, precision, generalization, and simplicity for petri net"""
     print("Fitness is calculated")
     fitness          = pm4py.fitness_token_based_replay(data, net, initial_marking, final_marking)
     #print("Precision is calculated")
     #precisison       = pm4py.precision_token_based_replay(data, net, initial_marking, final_marking)
     print("Generalizability is calculated")
-    generalizability = pm4py.algo.evaluation.generalization.algorithm.apply(data, net,
+    generalization = pm4py.algo.evaluation.generalization.algorithm.apply(data, net,
                                                                             initial_marking, final_marking)
     print("Simplicity is calculated")
     simplicity       = pm4py.algo.evaluation.simplicity.algorithm.apply(net)
-    #return [fitness['average_trace_fitness'], precisison, generalizability, simplicity]
-    return [fitness['average_trace_fitness'], generalizability, simplicity]
+    #return [fitness['average_trace_fitness'], precisison, generalization, simplicity]
+    return [fitness['average_trace_fitness'], generalization, simplicity]
 
 
 def pn_infos(log, colname, filter):
@@ -51,8 +51,8 @@ def pn_infos_miner(log, miner):
 def eval_append(log, net, im, fm):
 
     eval = eval_pm(log, net, im, fm)
-    is_sound = pm4py.check_soundness(net, im, fm)
-    eval.append(is_sound[0])
+    #is_sound = pm4py.check_soundness(net, im, fm)
+    #eval.append(is_sound[0])
     eval.append(len(net.arcs))
     eval.append(len(net.transitions))
     eval.append(len(net.places))
@@ -63,7 +63,7 @@ def eval_append(log, net, im, fm):
     eval.append({k: sorted_variants[k] for k in list(sorted_variants)[:1]})
 
     eval = pd.DataFrame(eval).T
-    eval.columns = ["fitness", "precision", "generalizability", "simplicity",
-                    "sound", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"]
+    eval.columns = ["fitness", "generalization", "simplicity", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"]
+    #eval.columns = ["fitness", "precision", "generalization", "simplicity", "sound", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"]
     return eval