#
# You should have received a copy of the GNU Lesser General Public License
# along with DREAM. If not, see <http://www.gnu.org/licenses/>.
# ===========================================================================
import xlrd
import json
from DistributionFitting import DistFittest
from ImportExceldata import Import_Excel
workbook = xlrd.open_workbook('Mockup_ProcessingTimes.xls') #Using xlrd library opens the Excel document with the input data
worksheets = workbook.sheet_names()
worksheet_ProcessingTimes = worksheets[0] #It defines the worksheet_ProcessingTimes as the first sheet of the Excel file
A=Import_Excel() #Call the Import_Excel object
B=DistFittest() #Call the Distribution Fitting object
ProcessingTimes= A.Input_data(worksheet_ProcessingTimes, workbook) #Create a dictionary with the imported data from the Excel file
jsonFile= open('BatchesInput.json','r') #open the json file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
nodes = data['nodes']
lista=[]
for (element_id, element) in nodes.iteritems(): #This loop appends in a list the id's of the json file
element['id'] = element_id
lista.append(element ['id'])
fittingDict={}
for element in ProcessingTimes: #This loop searches the elements of the Excel imported data and if these elements exist in json file append the distribution fitting results in a dictionary
if element in lista:
failures = data.Input_data(
worksheet_Fail, workbook
) #Create the failures dictionary with key the MTTF and MTTR data points
MTTF = failures.get('MTTF', [])
MTTR = failures.get('MTTR', [])
#======================= Fit data to probability distributions ================================#
#The Distributions and DistFittest objects are called to fit statistical distributions to the in scope data
dist = Distributions()
act2Proc = dist.Weibull_distrfit(Activity2_Proc)
act3Proc = dist.Weibull_distrfit(Activity3_Proc)
s1Times = dist.Exponential_distrfit(S1)
distFit = DistFittest()
act1MTTF = distFit.ks_test(MTTF)
act1MTTR = distFit.ks_test(MTTR)
#======================= Output preparation: output the updated values in the XML file of this example ================================#
datafile = ('Topology1.xml') #define the input xml file
tree = et.parse(datafile)
simul8 = Simul8Output() #Call the Simul8Output object
#Assign the statistical distribution calculated above in the XML file using methods of the Simul8Output object
interTimes = simul8.InterArrivalTime(tree, 'Source', s1Times)
procTimes2 = simul8.ProcTimes(interTimes, 'Activity 2', act2Proc)
procTimes3 = simul8.ProcTimes(procTimes2, 'Activity 3', act3Proc)
#Again assign the MTTF and MTTR probability distributions calling the relevant methods from the Simul8Output object
workbook = xlrd.open_workbook('inputsTwoServers.xls') #Using xlrd library opens the Excel document with the input data
worksheets = workbook.sheet_names()
worksheet_OperationTime = worksheets[0] #It creates a variable that holds the first Excel worksheet
X=Import_Excel() #Call the import_Excel object
OperationTimes= X.Input_data(worksheet_OperationTime,workbook) #It defines a Python dictionary, giving as name OpearationTimes and as value the returned dictionary from the import_Excel object
Machine1_OpearationTimes = OperationTimes.get('Machine1',[]) #Two lists are defined (Machine1_OpearationTimes, Machine2_OpearationTimes) with the operation times data of each machine
Machine2_OpearationTimes = OperationTimes.get('Machine2',[])
A=HandleMissingValues() #Call the HandleMissingValues object
Machine1_OpearationTimes= A.DeleteMissingValue(Machine1_OpearationTimes) #It deletes the missing values in the lists with the operation times data
Machine2_OpearationTimes= A.DeleteMissingValue(Machine2_OpearationTimes)
Dict={}
B=DistFittest() #It calls the DistFittest object
Dict['M1']=B.ks_test(Machine1_OpearationTimes) #It conducts the Kolmogorov-Smirnov test in the list with the operation times data
Dict['M2']=B.ks_test(Machine2_OpearationTimes)
M1=Dict.get('M1')
M2=Dict.get('M2')
#==================================== Output preparation: output the updated values in the CMSD information model of Topology10 ====================================================#
datafile=('CMSD_TwoServers.xml') #It defines the name or the directory of the XML file that is manually written the CMSD information model
tree = et.parse(datafile) #This file will be parsed using the XML.ETREE Python library
M1Parameters=[]
M1ParameterValue=[]
for index in list(Dict['M1'].keys()):
if index is not 'distributionType':
continue
MTTR_MILL1=[]
MTTR_MILL2=[]
for j in range(c.rowcount):
#get the next line
ind3=c.fetchone()
if ind3.stat_code == 'MILL1':
MTTR_MILL1.append(ind3.MTTR_hour)
elif ind3.stat_code == 'MILL2':
MTTR_MILL2.append(ind3.MTTR_hour)
else:
continue
#======================= Fit data to statistical distributions ================================#
dist_proctime = DistFittest()
distProcTime_MILL1 = dist_proctime.ks_test(procTime_MILL1)
distProcTime_MILL2 = dist_proctime.ks_test(procTime_MILL2)
dist_MTTF = Distributions()
dist_MTTR = Distributions()
distMTTF_MILL1 = dist_MTTF.Weibull_distrfit(MTTF_MILL1)
distMTTF_MILL2 = dist_MTTF.Weibull_distrfit(MTTF_MILL2)
distMTTR_MILL1 = dist_MTTR.Poisson_distrfit(MTTR_MILL1)
distMTTR_MILL2 = dist_MTTR.Poisson_distrfit(MTTR_MILL2)
#======================= Output preparation: output the updated values in the JSON file of this example ================================#
jsonFile = open('JSON_example.json','r') #It opens the JSON file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
MTTF=A.Input_data(worksheet_MTTF, workbook) #Create the MTTF dictionary with key the Machine 1 and time-to-failure data
MTTR=A.Input_data(worksheet_MTTR, workbook) #Create the MTTR Quantity dictionary with key the Machine 1 and time-to-repair data
##Get from the above dictionaries the M1 key and define the following lists with data
ProcTime = ProcessingTimes.get('M1',[])
MTTF = MTTF.get('M1',[])
MTTR = MTTR.get('M1',[])
#Call the HandleMissingValues object and replace the missing values in the lists with the mean of the non-missing values
B =HandleMissingValues()
ProcTime = B.ReplaceWithMean(ProcTime)
MTTF = B.ReplaceWithMean(MTTF)
MTTR = B.ReplaceWithMean(MTTR)
C = Distributions() #Call the Distributions object
D = DistFittest() #Call the DistFittest object
ProcTime_dist = D.ks_test(ProcTime)
MTTF_dist = C.Exponential_distrfit(MTTF)
MTTR_dist = C.Exponential_distrfit(MTTR)
#================================= Output preparation: output the updated values in the JSON file of this example =========================================================#
jsonFile = open('JSON_AssembleDismantle.json','r') #It opens the JSON file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
nodes = data.get('nodes',[]) #It creates a variable that holds the 'nodes' dictionary
for element in nodes:
processingTime = nodes[element].get('processingTime',{}) #It creates a variable that gets the element attribute 'processingTime'
MTTF_Nodes = nodes[element].get('MTTF',{}) #It creates a variable that gets the element attribute 'MTTF'
MTTR_Nodes = nodes[element].get('MTTR',{}) #It creates a variable that gets the element attribute 'MTTR'
WIP[key].append(unitsToProcess)
elif WIP[key][0] == 'PaA':
secs = WIP[key][1].total_seconds()
minutes = int(secs / 60)
unitsToProcess = round(batchSize - (minutes / meanPaA_Proc))
WIP[key].append(unitsToProcess)
elif WIP[key][0] == 'PaB':
secs = WIP[key][1].total_seconds()
minutes = int(secs / 60)
unitsToProcess = round(batchSize - (minutes / meanPaB_Proc))
WIP[key].append(unitsToProcess)
except IndexError:
continue
# Call the DistFittest object and conduct Kolmogorov-Smirnov distribution fitting test in the processing times lists of each station
D = DistFittest()
dictProc = {
} #Create a dictionary that holds the statistical distributions of the processing times of each station
dictProc['MA'] = D.ks_test(MA_Proc)
dictProc['M1A'] = D.ks_test(M1A_Proc)
dictProc['M1B'] = D.ks_test(M1B_Proc)
dictProc['M2A'] = D.ks_test(M2A_Proc)
dictProc['M2B'] = D.ks_test(M2B_Proc)
dictProc['M3A'] = D.ks_test(M3A_Proc)
dictProc['M3B'] = D.ks_test(M3B_Proc)
dictProc['MM'] = D.ks_test(MM_Proc)
dictProc['PrA'] = D.ks_test(PrA_Proc)
dictProc['PrB'] = D.ks_test(PrB_Proc)
dictProc['PaA'] = D.ks_test(PaA_Proc)
dictProc['PaB'] = D.ks_test(PaB_Proc)
#Call the Distributions object and fit (using the Maximum Likelihood Estimation) the lists with the scrap quantity into a discrete statistical distribution, i.e. Geometric distribution
ProcessingTimes = inputData.Input_data(
worksheet_ProcessingTimes, workbook
) #Create the Processing Times dictionary with key Machines 1,2 and values the processing time data
##Get from the above dictionaries the M1 key and define the following lists with data
M1_ProcTime = ProcessingTimes.get('M1', [])
M2_ProcTime = ProcessingTimes.get('M2', [])
#Call the HandleMissingValues object and replace the missing values in the lists with the mean of the non-missing values
misValues = HandleMissingValues()
M1_ProcTime = misValues.ReplaceWithMean(M1_ProcTime)
M2_ProcTime = misValues.ReplaceWithMean(M2_ProcTime)
MLE = Distributions(
) #Call the Distributions object (Maximum Likelihood Estimation - MLE)
KS = DistFittest() #Call the DistFittest object (Kolmoghorov-Smirnov test)
M1ProcTime_dist = KS.ks_test(M1_ProcTime)
M2ProcTime_dist = MLE.Normal_distrfit(M2_ProcTime)
#======================= Output preparation: output the updated values in the JSON file of this example ================================#
jsonFile = open('JSON_TwoParallelStations.json', 'r') #It opens the JSON file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
nodes = data.get('nodes',
[]) #It creates a variable that holds the 'nodes' dictionary
for element in nodes:
processingTime = nodes[element].get(
'processingTime', {}
) #It creates a variable that gets the element attribute 'processingTime'
