Skip to content

dream
dream
Commit 3c902673 authored by Georgios Dagkakis's avatar Georgios Dagkakis Committed by Sebastien Robin
Browse files
Options

all objects updated in order to output JSON also in multiple runs

parent 82ae41c1
Hide whitespace changes
Inline Side-by-side
Showing with 172 additions and 22 deletions
DREAM/dream/simulation/Assembly.py
View file @ 3c902673
...@@ -250,7 +250,46 @@ class Assembly(Process): ...@@ -250,7 +250,46 @@ class Assembly(Process):
json['results']['working_ratio']=100*self.totalWorkingTime/G.maxSimTime json['results']['working_ratio']=100*self.totalWorkingTime/G.maxSimTime
json['results']['blockage_ratio']=100*self.totalBlockageTime/G.maxSimTime json['results']['blockage_ratio']=100*self.totalBlockageTime/G.maxSimTime
json['results']['waiting_ratio']=100*self.totalWaitingTime/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) #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! #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): ...@@ -328,7 +328,43 @@ class Conveyer(Process):
json['results']['working_ratio']=100*self.totalWorkingTime/G.maxSimTime json['results']['working_ratio']=100*self.totalWorkingTime/G.maxSimTime
json['results']['blockage_ratio']=100*self.totalBlockageTime/G.maxSimTime json['results']['blockage_ratio']=100*self.totalBlockageTime/G.maxSimTime
json['results']['waiting_ratio']=100*self.totalWaitingTime/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) #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! #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): ...@@ -284,7 +284,44 @@ class Dismantle(Process):
json['results']['working_ratio']=100*self.totalWorkingTime/G.maxSimTime json['results']['working_ratio']=100*self.totalWorkingTime/G.maxSimTime
json['results']['blockage_ratio']=100*self.totalBlockageTime/G.maxSimTime json['results']['blockage_ratio']=100*self.totalBlockageTime/G.maxSimTime
json['results']['waiting_ratio']=100*self.totalWaitingTime/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) #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! #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 @@ ...@@ -3,7 +3,7 @@
"_class": "Dream.Configuration", "_class": "Dream.Configuration",
"numberOfReplications": "10", "numberOfReplications": "10",
"maxSimTime": "1440", "maxSimTime": "1440",
"trace": "Yes", "trace": "No",
"confidenceLevel": "0.95" "confidenceLevel": "0.95"
}, },
"modelResource": [ "modelResource": [
......
DREAM/dream/simulation/JSONInputs/Topology02.json
View file @ 3c902673
{"_class": "Dream.Simulation", {"_class": "Dream.Simulation",
"general": { "general": {
"_class": "Dream.Configuration", "_class": "Dream.Configuration",
"numberOfReplications": "1", "numberOfReplications": "10",
"maxSimTime": "1440", "maxSimTime": "1440",
"trace": "Yes", "trace": "No",
"confidenceLevel": "0.95" "confidenceLevel": "0.95"
}, },
"modelResource": [ "modelResource": [
...@@ -26,7 +26,7 @@ ...@@ -26,7 +26,7 @@
"interarrivalTime": "interarrivalTime":
{ {
"distributionType": "Fixed", "distributionType": "Fixed",
"mean": "2" "mean": "2"
}, },
"entity": "Frame", "entity": "Frame",
"successorList": ["Α1"] "successorList": ["Α1"]
...@@ -34,10 +34,14 @@ ...@@ -34,10 +34,14 @@
{"_class": "Dream.Assembly", {"_class": "Dream.Assembly",
"id": "A1", "id": "A1",
"name": "Assembly", "name": "Assembly",
"processingTime": { "processingTime":
"distributionType": "Fixed", {
"mean": "2" "distributionType": "Normal",
}, "mean": "2",
"stdev": "0.8",
"min": "1",
"max": "4"
},
"predecessorPartList": ["S1"], "predecessorPartList": ["S1"],
"predecessorFrameList": ["S2"], "predecessorFrameList": ["S2"],
"successorList": ["M1"] "successorList": ["M1"]
......
DREAM/dream/simulation/JSONInputs/Topology13.json
View file @ 3c902673
{"_class": "Dream.Simulation", {"_class": "Dream.Simulation",
"general": { "general": {
"_class": "Dream.Configuration", "_class": "Dream.Configuration",
"numberOfReplications": "1", "numberOfReplications": "8",
"maxSimTime": "1440", "maxSimTime": "1440",
"trace": "Yes", "trace": "No",
"confidenceLevel": "0.95" "confidenceLevel": "0.95"
}, },
"modelResource": [ "modelResource": [
...@@ -62,8 +62,11 @@ ...@@ -62,8 +62,11 @@
"id": "D1", "id": "D1",
"name": "Dismantle", "name": "Dismantle",
"processingTime": { "processingTime": {
"distributionType": "Fixed", "distributionType": "Normal",
"mean": "1" "mean": "1",
"stdev": "0.3",
"min": "0.1",
"max": "4"
}, },
"successorPartList": ["E2"], "successorPartList": ["E2"],
"successorFrameList": ["E1"], "successorFrameList": ["E1"],
......
DREAM/dream/simulation/JSONInputs/Topology15.json
View file @ 3c902673
{"_class": "Dream.Simulation", {"_class": "Dream.Simulation",
"general": { "general": {
"_class": "Dream.Configuration", "_class": "Dream.Configuration",
"numberOfReplications": "1", "numberOfReplications": "5",
"maxSimTime": "1440", "maxSimTime": "1440",
"trace": "Yes", "trace": "No",
"confidenceLevel": "0.95" "confidenceLevel": "0.95"
}, },
"modelResource": [ "modelResource": [
...@@ -45,8 +45,11 @@ ...@@ -45,8 +45,11 @@
"id": "M2", "id": "M2",
"name": "Machine2", "name": "Machine2",
"processingTime": { "processingTime": {
"distributionType": "Fixed", "distributionType": "Normal",
"mean": "1.5" "mean": "1.5",
"stdev": "0.3",
"min": "0.1",
"max": "4"
}, },
"failures":{ "failures":{
"failureDistribution": "Fixed", "failureDistribution": "Fixed",
......
DREAM/dream/simulation/LineGenerationJSON.py
View file @ 3c902673
...@@ -141,7 +141,7 @@ def createObjects(): ...@@ -141,7 +141,7 @@ def createObjects():
mean=float(processingTime.get('mean', '0')) mean=float(processingTime.get('mean', '0'))
stdev=float(processingTime.get('stdev', '0')) stdev=float(processingTime.get('stdev', '0'))
min=float(processingTime.get('min', '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') predecessorPartList=core_object.get('predecessorPartList', 'not found')
predecessorFrameList=core_object.get('predecessorFrameList', 'not found') predecessorFrameList=core_object.get('predecessorFrameList', 'not found')
successorList=core_object.get('successorList', 'not found') successorList=core_object.get('successorList', 'not found')
...@@ -160,7 +160,7 @@ def createObjects(): ...@@ -160,7 +160,7 @@ def createObjects():
mean=float(processingTime.get('mean', '0')) mean=float(processingTime.get('mean', '0'))
stdev=float(processingTime.get('stdev', '0')) stdev=float(processingTime.get('stdev', '0'))
min=float(processingTime.get('min', '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') successorPartList=core_object.get('successorPartList', 'not found')
successorFrameList=core_object.get('successorFrameList', 'not found') successorFrameList=core_object.get('successorFrameList', 'not found')
predecessorList=core_object.get('predecessorList', 'not found') predecessorList=core_object.get('predecessorList', 'not found')
......
DREAM/dream/simulation/Repairman.py
View file @ 3c902673
...@@ -94,7 +94,35 @@ class Repairman(object): ...@@ -94,7 +94,35 @@ class Repairman(object):
json['results'] = {} json['results'] = {}
json['results']['working_ratio']=100*self.totalWorkingTime/G.maxSimTime json['results']['working_ratio']=100*self.totalWorkingTime/G.maxSimTime
json['results']['waiting_ratio']=100*self.totalWaitingTime/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) #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! #needed because if somebody runs multiple runs in deterministic case it would crash!
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment
GitLab Nexedi Edition | About GitLab | About Nexedi | 沪ICP备2021021310号-2 | 沪ICP备2021021310号-7