results of exit to work with CIs in multiple replications

dream/plugins/DefaultTabularExit.py

@@ -13,17 +13,45 @@ class DefaultTabularExit(plugin.OutputPreparationPlugin):
     """
 
     def postprocess(self, data):
-        node=data['graph']['node']
-        # create the titles of the columns
-        data['result']['result_list'][0]['exit_output'] = [['Exit Id','Throughput', 'Takt Time', 'Lifespan']]
-        # loop the results and search for elements that have 'Exit' as family
-        for record in data['result']['result_list'][0]['elementList']:
-            family=record.get('family',None)
-            # when found, add a row with the results of the specific exit
-            if family=='Exit':
-                exitId=record['id']
-                throughput=record['results'].get('throughput','undefined')
-                taktTime=record['results'].get('takt_time','undefined')
-                lifespan=record['results'].get('lifespan','undefined')
-                data['result']['result_list'][0]['exit_output'].append([exitId,throughput,taktTime,lifespan]) 
-        return data
\ No newline at end of file
+        numberOfReplications=data['general']['numberOfReplications']
+        confidenceLevel=data['general']['confidenceLevel']
+        if numberOfReplications==1:
+            # create the titles of the columns
+            data['result']['result_list'][0]['exit_output'] = [['Exit Id','Throughput', 'Takt Time', 'Lifespan']]
+            # loop the results and search for elements that have 'Exit' as family
+            for record in data['result']['result_list'][0]['elementList']:
+                family=record.get('family',None)
+                # when found, add a row with the results of the specific exit
+                if family=='Exit':
+                    exitId=record['id']
+                    throughput=record['results'].get('throughput','undefined')
+                    taktTime=record['results'].get('takt_time','undefined')
+                    lifespan=record['results'].get('lifespan','undefined')
+                    data['result']['result_list'][0]['exit_output'].append([exitId,throughput,taktTime,lifespan]) 
+        elif numberOfReplications>1:
+            # create the titles of the columns
+            data['result']['result_list'][0]['exit_output'] = [['Exit Id','','Throughput','' , '','Takt Time','','', 'Lifespan',''],
+                                                               ['','LB','AVG','RB','LB','AVG','RB','LB','AVG','RB']]
+            for record in data['result']['result_list'][0]['elementList']:
+                family=record.get('family',None)
+                # when found, add a row with the results of the specific exit
+                if family=='Exit':
+                    exitId=record['id']
+                    throughput=self.getConfidenceInterval(record['results'].get('throughput','undefined'),confidenceLevel)
+                    taktTime=self.getConfidenceInterval(record['results'].get('takt_time','undefined'),confidenceLevel)
+                    lifespan=self.getConfidenceInterval(record['results'].get('lifespan','undefined'),confidenceLevel)                 
+                    data['result']['result_list'][0]['exit_output'].append([exitId,
+                                                                            throughput['lb'],throughput['avg'],throughput['ub'],
+                                                                            taktTime['lb'],taktTime['avg'],taktTime['ub'],
+                                                                            lifespan['lb'],lifespan['avg'],lifespan['ub']]) 
+        return data
+    
+    def getConfidenceInterval(self, value_list, confidenceLevel):
+        from dream.KnowledgeExtraction.ConfidenceIntervals import Intervals
+        from dream.KnowledgeExtraction.StatisticalMeasures import BasicStatisticalMeasures
+        BSM=BasicStatisticalMeasures()
+        lb, ub = Intervals().ConfidIntervals(value_list, confidenceLevel)
+        return {'lb': lb,
+                'ub': ub,
+                'avg': BSM.mean(value_list) 
+            }
\ No newline at end of file