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Commit 3c902673 authored by Georgios Dagkakis's avatar Georgios Dagkakis Committed by Sebastien Robin
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all objects updated in order to output JSON also in multiple runs

parent 82ae41c1
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Showing with 172 additions and 22 deletions
DREAM/dream/simulation/Assembly.py
View file @ 3c902673
......@@ -250,7 +250,46 @@ class Assembly(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.Assembly';
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']['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!
......
DREAM/dream/simulation/Conveyer.py
View file @ 3c902673
......@@ -328,7 +328,43 @@ class Conveyer(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.Conveyer';
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']['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!
......
DREAM/dream/simulation/Dismantle.py
View file @ 3c902673
......@@ -284,7 +284,44 @@ class Dismantle(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.Dismantle';
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']['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!
......
DREAM/dream/simulation/JSONInputs/Topology01.json
View file @ 3c902673
......@@ -3,7 +3,7 @@
"_class": "Dream.Configuration",
"numberOfReplications": "10",
"maxSimTime": "1440",
"trace": "Yes",
"trace": "No",
"confidenceLevel": "0.95"
},
"modelResource": [
......
DREAM/dream/simulation/JSONInputs/Topology02.json
View file @ 3c902673
{"_class": "Dream.Simulation",
"general": {
"_class": "Dream.Configuration",
"numberOfReplications": "1",
"numberOfReplications": "10",
"maxSimTime": "1440",
"trace": "Yes",
"trace": "No",
"confidenceLevel": "0.95"
},
"modelResource": [
......@@ -26,7 +26,7 @@
"interarrivalTime":
{
"distributionType": "Fixed",
"mean": "2"
"mean": "2"
},
"entity": "Frame",
"successorList": ["Α1"]
......@@ -34,10 +34,14 @@
{"_class": "Dream.Assembly",
"id": "A1",
"name": "Assembly",
"processingTime": {
"distributionType": "Fixed",
"mean": "2"
},
"processingTime":
{
"distributionType": "Normal",
"mean": "2",
"stdev": "0.8",
"min": "1",
"max": "4"
},
"predecessorPartList": ["S1"],
"predecessorFrameList": ["S2"],
"successorList": ["M1"]
......
DREAM/dream/simulation/JSONInputs/Topology13.json
View file @ 3c902673
{"_class": "Dream.Simulation",
"general": {
"_class": "Dream.Configuration",
"numberOfReplications": "1",
"numberOfReplications": "8",
"maxSimTime": "1440",
"trace": "Yes",
"trace": "No",
"confidenceLevel": "0.95"
},
"modelResource": [
......@@ -62,8 +62,11 @@
"id": "D1",
"name": "Dismantle",
"processingTime": {
"distributionType": "Fixed",
"mean": "1"
"distributionType": "Normal",
"mean": "1",
"stdev": "0.3",
"min": "0.1",
"max": "4"
},
"successorPartList": ["E2"],
"successorFrameList": ["E1"],
......
DREAM/dream/simulation/JSONInputs/Topology15.json
View file @ 3c902673
{"_class": "Dream.Simulation",
"general": {
"_class": "Dream.Configuration",
"numberOfReplications": "1",
"numberOfReplications": "5",
"maxSimTime": "1440",
"trace": "Yes",
"trace": "No",
"confidenceLevel": "0.95"
},
"modelResource": [
......@@ -45,8 +45,11 @@
"id": "M2",
"name": "Machine2",
"processingTime": {
"distributionType": "Fixed",
"mean": "1.5"
"distributionType": "Normal",
"mean": "1.5",
"stdev": "0.3",
"min": "0.1",
"max": "4"
},
"failures":{
"failureDistribution": "Fixed",
......
DREAM/dream/simulation/LineGenerationJSON.py
View file @ 3c902673
......@@ -141,7 +141,7 @@ 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'))
predecessorPartList=core_object.get('predecessorPartList', 'not found')
predecessorFrameList=core_object.get('predecessorFrameList', 'not found')
successorList=core_object.get('successorList', 'not found')
......@@ -160,7 +160,7 @@ 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'))
successorPartList=core_object.get('successorPartList', 'not found')
successorFrameList=core_object.get('successorFrameList', 'not found')
predecessorList=core_object.get('predecessorList', 'not found')
......
DREAM/dream/simulation/Repairman.py
View file @ 3c902673
......@@ -94,7 +94,35 @@ class Repairman(object):
json['results'] = {}
json['results']['working_ratio']=100*self.totalWorkingTime/G.maxSimTime
json['results']['waiting_ratio']=100*self.totalWaitingTime/G.maxSimTime
G.outputJSON['modelResource'].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.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['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!
......
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