TwoServersStochastic example updated to match the new notation. Also in the documentation

dream/simulation/Examples/TwoServersStochastic.py

 from dream.simulation.imports import Machine, Source, Exit, Part, G, Repairman, Queue, Failure 
 from dream.simulation.imports import simpy
 
-G.env=simpy.Environment()   # define a simpy environment
-                            # this is where all the simulation object 'live'
-
 #define the objects of the model
 R=Repairman('R1', 'Bob') 
 S=Source('S1','Source', interarrivalTime={'distributionType':'Exp','mean':0.5}, entity='Dream.Part')
@@ -31,10 +28,16 @@ G.maxSimTime=1440.0     #set G.maxSimTime 1440.0 minutes (1 day)
 G.numberOfReplications=10   #set 10 replications
 G.confidenceLevel=0.99      #set the confidence level. 0.99=99%
 
-#run the replications
-for i in range(G.numberOfReplications):
+def main():
+    throughputList=[]   # a list to hold the throughput of each replication
+
+    #run the replications
+    for i in range(G.numberOfReplications):
         G.seed+=1       #increment the seed so that we get different random numbers in each run.
         
+        G.env=simpy.Environment()   # define a simpy environment
+                                    # this is where all the simulation object 'live'
+        
         #initialize all the objects
         for object in G.ObjList:
             object.initialize()
@@ -61,10 +64,21 @@ for i in range(G.numberOfReplications):
         for objectResource in G.ObjectResourceList:
             objectResource.postProcessing()
 
-#output data to excel for every object        
-for object in G.ObjList:
-    object.outputResultsXL()      
-R.outputResultsXL()        
+        # append the numbe of exits in the throughputList
+        throughputList.append(E.numOfExits)
+        
+    print 'The exit of each replication is:'
+    print throughputList
     
-G.outputFile.save("output.xls")      
+    # calculate confidence interval using the Knowledge Extraction tool
+    from dream.KnowledgeExtraction.ConfidenceIntervals import Intervals
+    from dream.KnowledgeExtraction.StatisticalMeasures import BasicStatisticalMeasures
+    BSM=BasicStatisticalMeasures()
+    lb, ub = Intervals().ConfidIntervals(throughputList, 0.95)
+    print 'the 95% confidence interval for the throughput is:'
+    print 'lower bound:', lb 
+    print 'mean:', BSM.mean(throughputList)
+    print 'upper bound:', ub       
 
+if __name__ == '__main__':
+    main()
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