Machine and Exit updated so that they output in JSON also confidence...

Machine and Exit updated so that they output in JSON also confidence intervals. Got to check with Nex the notation

DREAM/dream/simulation/Exit.py

@@ -195,7 +195,43 @@ class Exit(Process):
             json['results']['throughput']=self.numOfExits
             json['results']['lifespan']=((self.totalLifespan)/self.numOfExits)/G.Base
             json['results']['takt_time']=((self.totalTaktTime)/self.numOfExits)/G.Base
-            G.outputJSON['coreObject'].append(json)
+        else: #if we had multiple replications we output confidence intervals to excel
+                #for some outputs the results may be the same for each run (eg model is stochastic but failures fixed
+                #so failurePortion will be exactly the same in each run). That will give 0 variability and errors.
+                #so for each output value we check if there was difference in the runs' results
+                #if yes we output the Confidence Intervals. if not we output just the fix value           
+            json={}
+            json['_class'] = 'Dream.Exit';
+            json['id'] = str(self.id)
+            json['results'] = {}
+            json['results']['throughput']={}
+            if self.checkIfArrayHasDifValues(self.Exits):
+                json['results']['throughput']['min']=stat.bayes_mvs(self.Exits, G.confidenceLevel)[0][1][0]
+                json['results']['throughput']['avg']=stat.bayes_mvs(self.Exits, G.confidenceLevel)[0][0]
+                json['results']['throughput']['max']=stat.bayes_mvs(self.Exits, G.confidenceLevel)[0][1][1]
+            else:
+                json['results']['throughput']['min']=self.Exits[0]
+                json['results']['throughput']['avg']=self.Exits[0]
+                json['results']['throughput']['max']=self.Exits[0]            
+            json['results']['lifespan']={}
+            if self.checkIfArrayHasDifValues(self.Lifespan):
+                json['results']['lifespan']['min']=stat.bayes_mvs(self.Lifespan, G.confidenceLevel)[0][1][0]
+                json['results']['lifespan']['avg']=stat.bayes_mvs(self.Lifespan, G.confidenceLevel)[0][0]
+                json['results']['lifespan']['max']=stat.bayes_mvs(self.Lifespan, G.confidenceLevel)[0][1][1]
+            else:
+                json['results']['lifespan']['min']=self.Lifespan[0]
+                json['results']['lifespan']['avg']=self.Lifespan[0]
+                json['results']['lifespan']['max']=self.Lifespan[0]                
+            json['results']['taktTime']={}            
+            if self.checkIfArrayHasDifValues(self.TaktTime):
+                json['results']['taktTime']['min']=stat.bayes_mvs(self.TaktTime, G.confidenceLevel)[0][1][0]
+                json['results']['taktTime']['avg']=stat.bayes_mvs(self.TaktTime, G.confidenceLevel)[0][0]
+                json['results']['taktTime']['max']=stat.bayes_mvs(self.TaktTime, G.confidenceLevel)[0][1][1]
+            else:
+                json['results']['taktTime']['min']=self.TaktTime[0]
+                json['results']['taktTime']['avg']=self.TaktTime[0]
+                json['results']['taktTime']['max']=self.TaktTime[0]        
+        G.outputJSON['coreObject'].append(json)
                
     #takes the array and checks if all its values are identical (returns false) or not (returns true) 
     #needed because if somebody runs multiple runs in deterministic case it would crash!          

DREAM/dream/simulation/JSONInputs/Topology01.json

 {"_class": "Dream.Simulation",
   "general": {
     "_class": "Dream.Configuration",
-    "numberOfReplications": "1",
+    "numberOfReplications": "10",
     "maxSimTime": "1440",
     "trace": "Yes",
     "confidenceLevel": "0.95"
@@ -19,7 +19,7 @@
       "name": "Raw Material",
       "interarrivalTime":
          {
-           "distributionType": "Fixed",
+           "distributionType": "Exp",
            "mean": "0.5"
          },
       "entity": "Part",
@@ -29,8 +29,11 @@
       "id": "M1",
       "name": "Machine1",
       "processingTime": {
-        "distributionType": "Fixed",
-        "mean": "0.25"
+        "distributionType": "Normal",
+        "mean": "0.25",
+		"stdev": "0.1",
+		"min": "0.1",
+		"max": "1"
         },
       "failures":{
            "failureDistribution": "Fixed",

DREAM/dream/simulation/LineGenerationJSON.py

@@ -89,7 +89,7 @@ def createObjects():
             mean=float(processingTime.get('mean', '0'))  
             stdev=float(processingTime.get('stdev', '0'))  
             min=float(processingTime.get('min', '0')) 
-            max=float(processingTime.get('stdev', '0'))
+            max=float(processingTime.get('max', '0'))
             failures=core_object.get('failures', 'not found')  
             failureDistribution=failures.get('failureDistribution', 'not found')
             MTTF=float(failures.get('MTTF', '0'))   

DREAM/dream/simulation/Machine.py

@@ -432,8 +432,55 @@ class Machine(Process):
             json['results']['working_ratio']=100*self.totalWorkingTime/G.maxSimTime
             json['results']['blockage_ratio']=100*self.totalBlockageTime/G.maxSimTime
             json['results']['waiting_ratio']=100*self.totalWaitingTime/G.maxSimTime
-            G.outputJSON['coreObject'].append(json)
-        
+        else: #if we had multiple replications we output confidence intervals to excel
+                #for some outputs the results may be the same for each run (eg model is stochastic but failures fixed
+                #so failurePortion will be exactly the same in each run). That will give 0 variability and errors.
+                #so for each output value we check if there was difference in the runs' results
+                #if yes we output the Confidence Intervals. if not we output just the fix value           
+            json={}
+            json['_class'] = 'Dream.Machine';
+            json['id'] = str(self.id)
+            json['results'] = {}
+            json['results']['failure_ratio']={}
+            if self.checkIfArrayHasDifValues(self.Failure):
+                json['results']['failure_ratio']['min']=stat.bayes_mvs(self.Failure, G.confidenceLevel)[0][1][0]
+                json['results']['failure_ratio']['avg']=stat.bayes_mvs(self.Failure, G.confidenceLevel)[0][0]
+                json['results']['failure_ratio']['max']=stat.bayes_mvs(self.Failure, G.confidenceLevel)[0][1][1]
+            else:
+                json['results']['failure_ratio']['min']=self.Failure[0]
+                json['results']['failure_ratio']['avg']=self.Failure[0]
+                json['results']['failure_ratio']['max']=self.Failure[0] 
+            json['results']['working_ratio']={}
+            if self.checkIfArrayHasDifValues(self.Working):
+                json['results']['working_ratio']['min']=stat.bayes_mvs(self.Working, G.confidenceLevel)[0][1][0]
+                json['results']['working_ratio']['avg']=stat.bayes_mvs(self.Working, G.confidenceLevel)[0][0]
+                json['results']['working_ratio']['max']=stat.bayes_mvs(self.Working, G.confidenceLevel)[0][1][1]
+            else:
+                json['results']['working_ratio']['min']=self.Working[0]
+                json['results']['working_ratio']['avg']=self.Working[0]
+                json['results']['working_ratio']['max']=self.Working[0]   
+            json['results']['blockage_ratio']={}
+            if self.checkIfArrayHasDifValues(self.Blockage):
+                json['results']['blockage_ratio']['min']=stat.bayes_mvs(self.Blockage, G.confidenceLevel)[0][1][0]
+                json['results']['blockage_ratio']['avg']=stat.bayes_mvs(self.Blockage, G.confidenceLevel)[0][0]
+                json['results']['blockage_ratio']['max']=stat.bayes_mvs(self.Blockage, G.confidenceLevel)[0][1][1]
+            else:
+                json['results']['blockage_ratio']['min']=self.Blockage[0]
+                json['results']['blockage_ratio']['avg']=self.Blockage[0]
+                json['results']['blockage_ratio']['max']=self.Blockage[0]                 
+            json['results']['waiting_ratio']={}
+            if self.checkIfArrayHasDifValues(self.Waiting):
+                json['results']['waiting_ratio']['min']=stat.bayes_mvs(self.Waiting, G.confidenceLevel)[0][1][0]
+                json['results']['waiting_ratio']['avg']=stat.bayes_mvs(self.Waiting, G.confidenceLevel)[0][0]
+                json['results']['waiting_ratio']['max']=stat.bayes_mvs(self.Waiting, G.confidenceLevel)[0][1][1]
+            else:
+                json['results']['waiting_ratio']['min']=self.Waiting[0]
+                json['results']['waiting_ratio']['avg']=self.Waiting[0]
+                json['results']['waiting_ratio']['max']=self.Waiting[0] 
+                
+        G.outputJSON['coreObject'].append(json)
+    
+    
     #takes the array and checks if all its values are identical (returns false) or not (returns true) 
     #needed because if somebody runs multiple runs in deterministic case it would crash!          
     def checkIfArrayHasDifValues(self, array):