Operator and OperatedMachine added. Not tested yet

dream/simulation/Operator.py

+# ===========================================================================
+# Copyright 2013 University of Limerick
+#
+# This file is part of DREAM.
+#
+# DREAM is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# DREAM is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with DREAM.  If not, see <http://www.gnu.org/licenses/>.
+# ===========================================================================
+'''
+Created on 22 Nov 2012
+
+@author: Ioannis
+'''
+
+'''
+models a repairman that can fix a machine when it gets failures
+'''
+
+from SimPy.Simulation import Resource, now
+import xlwt
+import scipy.stats as stat
+from ObjectResource import ObjectResource
+
+# ===========================================================================
+#                 the resource that repairs the machines
+# ===========================================================================
+class Operator(ObjectResource):
+    
+    def __init__(self, id, name, capacity=1):   
+        self.id=id      
+        self.objName=name
+        self.capacity=capacity      # operator is an instance of resource
+        self.type="Operator"
+#         self.Res=Resource(self.capacity)
+        # lists to hold statistics of multiple runs
+        self.Waiting=[]             # holds the percentage of waiting time 
+        self.Working=[]             # holds the percentage of working time 
+        # list with the coreObjects that the Operator operates
+        self.coreObjectIds=[]
+                
+    # =======================================================================    
+    #            actions to be taken after the simulation ends
+    # =======================================================================
+    def postProcessing(self, MaxSimtime=None):
+        if MaxSimtime==None:
+            from Globals import G
+            MaxSimtime=G.maxSimTime
+        # if the repairman is currently working we have to count the time of this work  
+        if not self.isResourceFree():  
+#         if len(self.getResourceQueue())>0:
+            self.totalWorkingTime+=now()-self.timeLastOperationStarted
+                
+        # Repairman was idle when he was not in any other state
+        self.totalWaitingTime=MaxSimtime-self.totalWorkingTime   
+        # update the waiting/working time percentages lists
+        self.Waiting.append(100*self.totalWaitingTime/MaxSimtime)
+        self.Working.append(100*self.totalWorkingTime/MaxSimtime)
+    
+    # =======================================================================
+    #                    outputs data to "output.xls"
+    # =======================================================================
+    def outputResultsXL(self, MaxSimtime=None):
+        from Globals import G
+        if MaxSimtime==None:
+            MaxSimtime=G.maxSimTime
+        # if we had just one replication output the results to excel
+        if(G.numberOfReplications==1): 
+            G.outputSheet.write(G.outputIndex,0, "The percentage of working of "+self.objName +" is:")
+            G.outputSheet.write(G.outputIndex,1,100*self.totalWorkingTime/MaxSimtime)
+            G.outputIndex+=1
+            G.outputSheet.write(G.outputIndex,0, "The percentage of waiting of "+self.objName +" is:")
+            G.outputSheet.write(G.outputIndex,1,100*self.totalWaitingTime/MaxSimtime)
+            G.outputIndex+=1
+        #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    
+        else:
+            G.outputSheet.write(G.outputIndex,0, "CI "+str(G.confidenceLevel*100)+"% for the mean percentage of Working of "+self.objName +" is:")
+            if self.checkIfArrayHasDifValues(self.Working): 
+                G.outputSheet.write(G.outputIndex,1,stat.bayes_mvs(self.Working, G.confidenceLevel)[0][1][0])
+                G.outputSheet.write(G.outputIndex,2,stat.bayes_mvs(self.Working, G.confidenceLevel)[0][0])
+                G.outputSheet.write(G.outputIndex,3,stat.bayes_mvs(self.Working, G.confidenceLevel)[0][1][1])  
+            else: 
+                G.outputSheet.write(G.outputIndex,1,self.Working[0])
+                G.outputSheet.write(G.outputIndex,2,self.Working[0])
+                G.outputSheet.write(G.outputIndex,3,self.Working[0])                            
+            G.outputIndex+=1
+            G.outputSheet.write(G.outputIndex,0, "CI "+str(G.confidenceLevel*100)+"% for the mean percentage of Waiting of "+self.objName +" is:")            
+            if self.checkIfArrayHasDifValues(self.Waiting):
+                G.outputSheet.write(G.outputIndex,1,stat.bayes_mvs(self.Waiting, G.confidenceLevel)[0][1][0])
+                G.outputSheet.write(G.outputIndex,2,stat.bayes_mvs(self.Waiting, G.confidenceLevel)[0][0])
+                G.outputSheet.write(G.outputIndex,3,stat.bayes_mvs(self.Waiting, G.confidenceLevel)[0][1][1])
+            else: 
+                G.outputSheet.write(G.outputIndex,1,self.Waiting[0])
+                G.outputSheet.write(G.outputIndex,2,self.Waiting[0])
+                G.outputSheet.write(G.outputIndex,3,self.Waiting[0]) 
+            G.outputIndex+=1
+        G.outputIndex+=1
+
+    # =======================================================================
+    #                    outputs results to JSON File
+    # =======================================================================
+    def outputResultsJSON(self):
+        from Globals import G
+        # if we had just one replication output the results to JSON
+        if(G.numberOfReplications==1): 
+            json={}
+            json['_class'] = 'Dream.Repairman';
+            json['id'] = str(self.id)
+            json['results'] = {}
+            json['results']['working_ratio']=100*self.totalWorkingTime/G.maxSimTime
+            json['results']['waiting_ratio']=100*self.totalWaitingTime/G.maxSimTime
+        #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  
+        else:          
+            json={}
+            json['_class'] = 'Dream.Repairman';
+            json['id'] = str(self.id)
+            json['results'] = {}
+            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']['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['elementList'].append(json)
+