Function adds all dynamics into list

diagramParser.py

@@ -20,13 +20,15 @@ def parseDiagramFile(csvFile):
     # ER components
     erComponents = {}
     
+    # Dynamic components 
+    dynamics = {}
         
     #List containing all metrics
     metrics = []
     #List containing all attacks
     attacks = []
     
-    parseDynamic(df, metrics)
+    parseDynamic(df, metrics, dynamics)
     
     
 def parseThreats(df, threatDict):
@@ -73,7 +75,7 @@ def parseAttacks(df, attackDict):
     return attackDict        
         
 #Parses metrics components and adds it to list
-def parseDynamic(df, metricList):
+def parseDynamic(df, metricList, dynamicsDict):
     for i in range(len(df)):                                            # Iterates through the dataframe
         if df[const.textArea1][i] == const.Dynamics:                    # If the component is a dynamic component
             
@@ -83,21 +85,21 @@ def parseDynamic(df, metricList):
                     df[const.textArea3][i]                              # Name of dynamic
                 )
                 threatDynamic.metrics = extractMetrics(df, i, 4)        # Extracts metrics from the dynamic component
-                joinMetrcs(threatDynamic.metrics, metricList)            # Adds the metrics to the global metric list
+                metricList = joinMetrcs(threatDynamic.metrics, metricList)            # Adds the metrics to the global metric list
                 
                 threatDynamic.associateBowtie(df, threatDynamic.type)                  # Associate the dynamic with the correct components
-                print(threatDynamic, "\n")
-                
+                dynamicsDict[df.Id[i]] = threatDynamic
+                                
             elif df[const.textArea3][i] == const.ConsequenceDynamic:
                 consequenceDynamic = dynamic.BowtieDynamic(
                     df[const.Id][i],                                    # Component ID LucidChart
                     df[const.textArea3][i]                              # Name of dynamic
                 )
                 consequenceDynamic.metrics = extractMetrics(df, i, 4)   # Extracts metrics from the dynamic component
-                joinMetrcs(threatDynamic.metrics, metricList)            # Adds the metrics to the global metric list
+                metricList = joinMetrcs(threatDynamic.metrics, metricList)            # Adds the metrics to the global metric list@
+                
                 consequenceDynamic.associateBowtie(df, consequenceDynamic.type)    
-                print(consequenceDynamic, "\n")# Associate the dynamic with the correct components
-
+                dynamicsDict[df.Id[i]] = consequenceDynamic
                 
             elif df[const.textArea3][i] == const.AttackDynamic:
                 attackDynamic = dynamic.BowtieDynamic(
@@ -105,10 +107,11 @@ def parseDynamic(df, metricList):
                     df[const.textArea3][i]                              # Name of dynamic
                 )
                 attackDynamic.metrics = extractMetrics(df, i, 4)
-                joinMetrcs(threatDynamic.metrics, metricList)            # Adds the metrics to the global metric list
+                metricList = joinMetrcs(threatDynamic.metrics, metricList)            # Adds the metrics to the global metric list
+                
                 attackDynamic.associateBowtie(df,attackDynamic.type)                     # Associate the dynamic with the correct components
-                print(attackDynamic, "\n")
-                                                    
+                dynamicsDict[df.Id[i]] = attackDynamic 
+                                                                   
             elif df[const.textArea3][i] == const.ERDynamic:
                 erDynamic = dynamic.ERDynamic(
                     df[const.Id][i],                                        # Component ID LucidChart  
@@ -116,9 +119,11 @@ def parseDynamic(df, metricList):
                     df[const.textArea5][i]                                 # Description
                 )
                 erDynamic.metrics = extractMetrics(df, i, 8)
-                joinMetrcs(threatDynamic.metrics, metricList)            # Adds the metrics to the global metric list
+                metricList = joinMetrcs(threatDynamic.metrics, metricList)            # Adds the metrics to the global metric list
+                
                 erDynamic.associatER(df)
-                print(erDynamic , "\n")
+                dynamicsDict[df.Id[i]] = erDynamic
+    print("Number of dynamics: ", len(dynamicsDict))
                 
 
 def extractMetrics(df, index, startRange):
@@ -152,3 +157,5 @@ def joinMetrcs(localMetrics, globalMetrics):
                 globalMetrics.append(localMetrics[j])       # Add the local metric to the global metric list
                 log.info("New local metric added to global metric list, metric: ", localMetrics[j].name)
     log.info("Added all metrics in local list \n, number of duplicate metrics: ", duplicateMetrics, "\n Number of new metrics: ", len(localMetrics)-duplicateMetrics)
+    return globalMetrics
+