now uses the proper object type variable

code/main.py

@@ -23,7 +23,7 @@ p_ocel_file = './data/ocel.jsonocel'
 p_mode = 'all'
 
 # object-type-name
-p_object_type = 'items'
+p_object_type = 'customers'
 
 # example data for attribute weighting in distance measurements
 p_attr_weights = {}
@@ -60,23 +60,21 @@ f.print_w_ts('ocel data read.')
 # res = f.ocel_get_object_distances(ocel, p_object_type, p_attr_weights)
 
 ##### CALCULATING DISTANCE MATRIX #####
-# Setting object type
-obj_type = "items"
 
 # List of all weights
 weights = [1]
 
 # Save unique activies to a list
-unique_activities = f.get_unique_activities(sorted(pm4py.ocel_object_type_activities(ocel)[obj_type], reverse=False))
+unique_activities = f.get_unique_activities(sorted(pm4py.ocel_object_type_activities(ocel)[p_object_type], reverse=False))
 
 # Create hash-table of activities to represent each one as a UNICODE-character
 activity_dict = f.map_activities_to_letter(unique_activities)
 
 # Create data frame with desired object type
-oid_filtered = f.get_df("objects", obj_type, ocel)
+oid_filtered = f.get_df("objects", p_object_type, ocel)
 
 # Create data frame with all relations
-relations_df = f.get_df("relations", obj_type, ocel)
+relations_df = f.get_df("relations", p_object_type, ocel)
 
 # Store control flow of every object to a list
 cflow_list = f.find_cflow(relations_df, activity_dict)
@@ -111,7 +109,7 @@ f.print_w_ts('distance matrix calculated:')
 f.print_w_ts(res)
 f.print_w_ts('duration: ' + str(datetime.datetime.now() - start_ts))
 
-print('oid list:')
-print(list_oid)
+# print('oid list:')
+# print(list_oid)
 
 quit()
\ No newline at end of file