def main(test=0, JSONFileName='JSON_example.json',
                CMSDFileName='CMSD_ParallelStations.xml',
                DBFilePath = 'C:\Users\Panos\Documents\KE tool_documentation',
                file_path=None,
                jsonFile=None, cmsdFile=None):
    if not file_path:
        cnxn=ConnectionData(seekName='ServerData', file_path=DBFilePath, implicitExt='txt', number_of_cursors=3)
        cursors=cnxn.getCursors()
    
    a = cursors[0].execute("""
            select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
            from production_status
                    """)
    MILL1=[]
    MILL2=[]
    for j in range(a.rowcount):
        #get the next line
        ind1=a.fetchone() 
        if ind1.stat_code == 'MILL1':
            procTime=[]
            procTime.insert(0,ind1.TIMEIN)
            procTime.insert(1,ind1.TIMEOUT)
            MILL1.append(procTime)
        elif ind1.stat_code == 'MILL2':
            procTime=[]
            procTime.insert(0,ind1.TIMEIN)
            procTime.insert(1,ind1.TIMEOUT)
            MILL2.append(procTime)
        else:
            continue
        
    transform = Transformations()
    procTime_MILL1=[]
    for elem in MILL1:
        t1=[]
        t2=[]
        t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
        t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
        dt=transform.subtraction(t2, t1)
        procTime_MILL1.append(dt[0])
    
    procTime_MILL2=[]
    for elem in MILL2:
        t1=[]
        t2=[]
        t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
        t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
        dt=transform.subtraction(t2, t1)
        procTime_MILL2.append(dt[0])
    
    
    b = cursors[1].execute("""
            select stat_code, MTTF_hour
            from failures
                    """)
    
    c = cursors[2].execute("""
            select stat_code, MTTR_hour
            from repairs
                    """)         
    MTTF_MILL1=[]
    MTTF_MILL2=[]
    for j in range(b.rowcount):
        #get the next line
        ind2=b.fetchone() 
        if ind2.stat_code == 'MILL1':
            MTTF_MILL1.append(ind2.MTTF_hour)
        elif ind2.stat_code == 'MILL2':
            MTTF_MILL2.append(ind2.MTTF_hour)
        else:
            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 values prepared in the CMSD information model of this model ====================================================#
    if not cmsdFile:
        datafile=(os.path.join(os.path.dirname(os.path.realpath(__file__)), CMSDFileName))       #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
    
    exportCMSD=CMSDOutput()
    stationId1='M1'
    stationId2='M2'
    procTime1=exportCMSD.ProcessingTimes(tree, stationId1, distProcTime_MILL1) 
    procTime2=exportCMSD.ProcessingTimes(procTime1, stationId2, distProcTime_MILL2)
    
    TTF1=exportCMSD.TTF(procTime2, stationId1, distMTTF_MILL1)
    TTR1=exportCMSD.TTR(TTF1, stationId1, distMTTR_MILL1)
    
    TTF2=exportCMSD.TTF(TTR1, stationId2, distMTTF_MILL2)
    TTR2=exportCMSD.TTR(TTF2, stationId2, distMTTR_MILL2)
    
    TTR2.write('CMSD_ParallelStations_Output.xml',encoding="utf8")                         #It writes the element tree to a specified file, using the 'utf8' output encoding
    
    #======================= Output preparation: output the updated values in the JSON file of this example ================================#
    if not jsonFile:
        jsonFile = open(os.path.join(os.path.dirname(os.path.realpath(__file__)), JSONFileName),'r')      #It opens the JSON file 
        data = json.load(jsonFile)             #It loads the file
        jsonFile.close()
    else:
        data = json.load(jsonFile) 
    
    exportJSON=JSONOutput()
    stationId1='M1'
    stationId2='M2'
    data1=exportJSON.ProcessingTimes(data, stationId1, distProcTime_MILL1)
    data2=exportJSON.ProcessingTimes(data1, stationId2, distProcTime_MILL2)
    
    data3=exportJSON.TTF(data2, stationId1, distMTTF_MILL1)
    data4=exportJSON.TTR(data3, stationId1, distMTTR_MILL1)
    
    data5=exportJSON.TTF(data4, stationId2, distMTTF_MILL2)
    data6=exportJSON.TTR(data5, stationId2, distMTTR_MILL2)
    
    # if we run from test return the data6
    if test:
        return data6
        
    jsonFile = open('JSON_ParallelStations_Output.json',"w")     #It opens the JSON file
    jsonFile.write(json.dumps(data6, indent=True))               #It writes the updated data to the JSON file 
    jsonFile.close()                                             #It closes the file
    
    #=================== Calling the ExcelOutput object, outputs the outcomes of the statistical analysis in xls files ==========================#
    export=ExcelOutput()
    
    export.PrintStatisticalMeasures(procTime_MILL1,'procTimeMILL1_StatResults.xls')   
    export.PrintStatisticalMeasures(procTime_MILL2,'procTimeMILL2_StatResults.xls')
    export.PrintStatisticalMeasures(MTTF_MILL1,'MTTFMILL1_StatResults.xls')   
    export.PrintStatisticalMeasures(MTTF_MILL2,'MTTFMILL2_StatResults.xls')
    export.PrintStatisticalMeasures(MTTR_MILL1,'MTTRMILL1_StatResults.xls')   
    export.PrintStatisticalMeasures(MTTR_MILL2,'MTTRMILL2_StatResults.xls')
    
    export.PrintDistributionFit(procTime_MILL1,'procTimeMILL1_DistFitResults.xls')
    export.PrintDistributionFit(procTime_MILL2,'procTimeMILL2_DistFitResults.xls')
    export.PrintDistributionFit(MTTF_MILL1,'MTTFMILL1_DistFitResults.xls')
    export.PrintDistributionFit(MTTF_MILL2,'MTTFMILL2_DistFitResults.xls')
    export.PrintDistributionFit(MTTR_MILL1,'MTTRMILL1_DistFitResults.xls')
    export.PrintDistributionFit(MTTR_MILL2,'MTTRMILL2_DistFitResults.xls')
def main(test=0,
         simul8XMLFileName='ParallelStations.xml',
         DBFilePath='C:\Users\Panos\Documents\KE tool_documentation',
         file_path=None,
         simul8XMLFile=None):
    if not file_path:
        cnxn = ConnectionData(seekName='ServerData',
                              file_path=DBFilePath,
                              implicitExt='txt',
                              number_of_cursors=3)
        cursors = cnxn.getCursors()


#Database queries used to extract the required data, in this example the processing times are given subtracting the TIME IN data point from the TIME OUT data point
    a = cursors[0].execute("""
            select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
            from production_status
                    """)
    MILL1 = []  #Initialization of MILL1 list
    MILL2 = []  #Initialization of MILL2 list
    for j in range(a.rowcount):
        #get the next line
        ind1 = a.fetchone()
        if ind1.stat_code == 'MILL1':
            procTime = []
            procTime.insert(0, ind1.TIMEIN)
            procTime.insert(1, ind1.TIMEOUT)
            MILL1.append(procTime)
        elif ind1.stat_code == 'MILL2':
            procTime = []
            procTime.insert(0, ind1.TIMEIN)
            procTime.insert(1, ind1.TIMEOUT)
            MILL2.append(procTime)
        else:
            continue
    #The  BasicTransformations object is called to conduct some data transformations
    transform = Transformations()
    procTime_MILL1 = []
    for elem in MILL1:
        t1 = []
        t2 = []
        t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
                  elem[0].second)
        t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
                  elem[1].second)
        dt = transform.subtraction(t2, t1)
        procTime_MILL1.append(dt[0])

    procTime_MILL2 = []
    for elem in MILL2:
        t1 = []
        t2 = []
        t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
                  elem[0].second)
        t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
                  elem[1].second)
        dt = transform.subtraction(t2, t1)
        procTime_MILL2.append(dt[0])
    #Database queries used again to extract the MTTF and MTTR data points
    b = cursors[1].execute("""
            select stat_code, MTTF_hour
            from failures
                    """)

    c = cursors[2].execute("""
            select stat_code, MTTR_hour
            from repairs
                    """)
    MTTF_MILL1 = [
    ]  #Initialization of the list that will contain the MTTF data points for MILL1
    MTTF_MILL2 = [
    ]  #Initialization of the list that will contain the MTTF data points for MILL2
    for j in range(b.rowcount):
        #get the next line
        ind2 = b.fetchone()
        if ind2.stat_code == 'MILL1':
            MTTF_MILL1.append(ind2.MTTF_hour)
        elif ind2.stat_code == 'MILL2':
            MTTF_MILL2.append(ind2.MTTF_hour)
        else:
            continue

    MTTR_MILL1 = [
    ]  #Initialization of the list that will contain the MTTR data points for MILL1
    MTTR_MILL2 = [
    ]  #Initialization of the list that will contain the MTTR data points for MILL1
    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 ================================#
    #The Distributions object is called to fit statistical distributions to the in scope data
    dist_proctime = Distributions()
    distProcTime_MILL1 = dist_proctime.Lognormal_distrfit(procTime_MILL1)
    distProcTime_MILL2 = dist_proctime.Weibull_distrfit(procTime_MILL2)

    dist_MTTF = Distributions()
    dist_MTTR = Distributions()
    distMTTF_MILL1 = dist_MTTF.Exponential_distrfit(MTTF_MILL1)
    distMTTF_MILL2 = dist_MTTF.Exponential_distrfit(MTTF_MILL2)

    distMTTR_MILL1 = dist_MTTR.Normal_distrfit(MTTR_MILL1)
    distMTTR_MILL2 = dist_MTTR.Normal_distrfit(MTTR_MILL2)

    #======================= Output preparation: output the updated values in the XML file of this example ================================#

    if not simul8XMLFile:
        datafile = (os.path.join(os.path.dirname(os.path.realpath(__file__)),
                                 simul8XMLFileName)
                    )  #It defines the name or the directory of the XML file
        tree = et.parse(datafile)
    else:
        datafile = simul8XMLFile
        tree = et.parse(datafile)

    simul8 = Simul8Output()  #Call the Simul8Output object
    #Assign the statistical distribution found above in the XML file using methods of the Simul8Output object
    procTimes1 = simul8.ProcTimes(tree, 'MILL1', distProcTime_MILL1)
    procTimes2 = simul8.ProcTimes(procTimes1, 'MILL2', distProcTime_MILL2)
    #Again assign the MTTF and MTTR probability distributions calling the relevant methods from the Simul8Output object
    MTTF1 = simul8.MTBF(procTimes2, 'MILL1', distMTTF_MILL1)
    MTTR1 = simul8.MTTR(MTTF1, 'MILL1', distMTTR_MILL1)

    MTTF2 = simul8.MTBF(MTTR1, 'MILL2', distMTTF_MILL2)
    MTTR2 = simul8.MTTR(MTTF2, 'MILL2', distMTTR_MILL2)
    #Output the XML file with the processed data
    output = MTTR2.write('KEtool_ParallelStations.xml')

    if test:
        output = et.parse('KEtool_ParallelStations.xml')
        return output
def main(test=0, simul8XMLFileName='ParallelStations.xml',
                DBFilePath = 'C:\Users\Panos\Documents\KE tool_documentation',
                file_path=None, simul8XMLFile=None):
    if not file_path:
        cnxn=ConnectionData(seekName='ServerData', file_path=DBFilePath, implicitExt='txt', number_of_cursors=3)
        cursors=cnxn.getCursors()
#Database queries used to extract the required data, in this example the processing times are given subtracting the TIME IN data point from the TIME OUT data point
    a = cursors[0].execute("""
            select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
            from production_status
                    """)
    MILL1=[] #Initialization of MILL1 list
    MILL2=[] #Initialization of MILL2 list
    for j in range(a.rowcount):
        #get the next line
        ind1=a.fetchone() 
        if ind1.stat_code == 'MILL1':
            procTime=[]
            procTime.insert(0,ind1.TIMEIN)
            procTime.insert(1,ind1.TIMEOUT)
            MILL1.append(procTime)
        elif ind1.stat_code == 'MILL2':
            procTime=[]
            procTime.insert(0,ind1.TIMEIN)
            procTime.insert(1,ind1.TIMEOUT)
            MILL2.append(procTime)
        else:
            continue
    #The  BasicTransformations object is called to conduct some data transformations     
    transform = Transformations()
    procTime_MILL1=[]
    for elem in MILL1:
        t1=[]
        t2=[]
        t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
        t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
        dt=transform.subtraction(t2, t1)
        procTime_MILL1.append(dt[0])
    
    procTime_MILL2=[]
    for elem in MILL2:
        t1=[]
        t2=[]
        t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
        t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
        dt=transform.subtraction(t2, t1)
        procTime_MILL2.append(dt[0])
    #Database queries used again to extract the MTTF and MTTR data points
    b = cursors[1].execute("""
            select stat_code, MTTF_hour
            from failures
                    """)
    
    c = cursors[2].execute("""
            select stat_code, MTTR_hour
            from repairs
                    """)         
    MTTF_MILL1=[] #Initialization of the list that will contain the MTTF data points for MILL1  
    MTTF_MILL2=[] #Initialization of the list that will contain the MTTF data points for MILL2
    for j in range(b.rowcount):
        #get the next line
        ind2=b.fetchone() 
        if ind2.stat_code == 'MILL1':
            MTTF_MILL1.append(ind2.MTTF_hour)
        elif ind2.stat_code == 'MILL2':
            MTTF_MILL2.append(ind2.MTTF_hour)
        else:
            continue
    
    MTTR_MILL1=[] #Initialization of the list that will contain the MTTR data points for MILL1 
    MTTR_MILL2=[] #Initialization of the list that will contain the MTTR data points for MILL1 
    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 ================================#
    #The Distributions object is called to fit statistical distributions to the in scope data
    dist_proctime = Distributions() 
    distProcTime_MILL1 = dist_proctime.Lognormal_distrfit(procTime_MILL1)
    distProcTime_MILL2 = dist_proctime.Weibull_distrfit(procTime_MILL2)
    
    dist_MTTF = Distributions()
    dist_MTTR = Distributions()
    distMTTF_MILL1 = dist_MTTF.Exponential_distrfit(MTTF_MILL1)
    distMTTF_MILL2 = dist_MTTF.Exponential_distrfit(MTTF_MILL2)
    
    distMTTR_MILL1 = dist_MTTR.Normal_distrfit(MTTR_MILL1)
    distMTTR_MILL2 = dist_MTTR.Normal_distrfit(MTTR_MILL2) 
    
    #======================= Output preparation: output the updated values in the XML file of this example ================================#
    
    if not simul8XMLFile:
        datafile=(os.path.join(os.path.dirname(os.path.realpath(__file__)), simul8XMLFileName))       #It defines the name or the directory of the XML file 
        tree = et.parse(datafile)    
    else:
        datafile=simul8XMLFile
        tree = et.parse(datafile)
         
    simul8 = Simul8Output()    #Call the Simul8Output object
    #Assign the statistical distribution found above in the XML file using methods of the Simul8Output object
    procTimes1 = simul8.ProcTimes(tree,'MILL1',distProcTime_MILL1)
    procTimes2 = simul8.ProcTimes(procTimes1,'MILL2',distProcTime_MILL2)
    #Again assign the MTTF and MTTR probability distributions calling the relevant methods from the Simul8Output object
    MTTF1 = simul8.MTBF(procTimes2,'MILL1',distMTTF_MILL1)
    MTTR1 = simul8.MTTR(MTTF1,'MILL1',distMTTR_MILL1)
    
    MTTF2 = simul8.MTBF(MTTR1,'MILL2',distMTTF_MILL2)
    MTTR2 = simul8.MTTR(MTTF2,'MILL2',distMTTR_MILL2)
    #Output the XML file with the processed data 
    output= MTTR2.write('KEtool_ParallelStations.xml')
    
    if test:
        output=et.parse('KEtool_ParallelStations.xml')
        return output 
示例#4
0
def main(test=0,
         JSONFileName='JSON_example.json',
         CMSDFileName='CMSD_ParallelStations.xml',
         DBFilePath='C:\Users\Panos\Documents\KE tool_documentation',
         file_path=None,
         jsonFile=None,
         cmsdFile=None):
    if not file_path:
        cnxn = ConnectionData(seekName='ServerData',
                              file_path=DBFilePath,
                              implicitExt='txt',
                              number_of_cursors=3)
        cursors = cnxn.getCursors()

    a = cursors[0].execute("""
            select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
            from production_status
                    """)
    MILL1 = []
    MILL2 = []
    for j in range(a.rowcount):
        #get the next line
        ind1 = a.fetchone()
        if ind1.stat_code == 'MILL1':
            procTime = []
            procTime.insert(0, ind1.TIMEIN)
            procTime.insert(1, ind1.TIMEOUT)
            MILL1.append(procTime)
        elif ind1.stat_code == 'MILL2':
            procTime = []
            procTime.insert(0, ind1.TIMEIN)
            procTime.insert(1, ind1.TIMEOUT)
            MILL2.append(procTime)
        else:
            continue

    transform = Transformations()
    procTime_MILL1 = []
    for elem in MILL1:
        t1 = []
        t2 = []
        t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
                  elem[0].second)
        t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
                  elem[1].second)
        dt = transform.subtraction(t2, t1)
        procTime_MILL1.append(dt[0])

    procTime_MILL2 = []
    for elem in MILL2:
        t1 = []
        t2 = []
        t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
                  elem[0].second)
        t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
                  elem[1].second)
        dt = transform.subtraction(t2, t1)
        procTime_MILL2.append(dt[0])

    b = cursors[1].execute("""
            select stat_code, MTTF_hour
            from failures
                    """)

    c = cursors[2].execute("""
            select stat_code, MTTR_hour
            from repairs
                    """)
    MTTF_MILL1 = []
    MTTF_MILL2 = []
    for j in range(b.rowcount):
        #get the next line
        ind2 = b.fetchone()
        if ind2.stat_code == 'MILL1':
            MTTF_MILL1.append(ind2.MTTF_hour)
        elif ind2.stat_code == 'MILL2':
            MTTF_MILL2.append(ind2.MTTF_hour)
        else:
            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 values prepared in the CMSD information model of this model ====================================================#
    if not cmsdFile:
        datafile = (
            os.path.join(os.path.dirname(os.path.realpath(__file__)),
                         CMSDFileName)
        )  #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

    exportCMSD = CMSDOutput()
    stationId1 = 'M1'
    stationId2 = 'M2'
    procTime1 = exportCMSD.ProcessingTimes(tree, stationId1,
                                           distProcTime_MILL1)
    procTime2 = exportCMSD.ProcessingTimes(procTime1, stationId2,
                                           distProcTime_MILL2)

    TTF1 = exportCMSD.TTF(procTime2, stationId1, distMTTF_MILL1)
    TTR1 = exportCMSD.TTR(TTF1, stationId1, distMTTR_MILL1)

    TTF2 = exportCMSD.TTF(TTR1, stationId2, distMTTF_MILL2)
    TTR2 = exportCMSD.TTR(TTF2, stationId2, distMTTR_MILL2)

    TTR2.write(
        'CMSD_ParallelStations_Output.xml', encoding="utf8"
    )  #It writes the element tree to a specified file, using the 'utf8' output encoding

    #======================= Output preparation: output the updated values in the JSON file of this example ================================#
    if not jsonFile:
        jsonFile = open(
            os.path.join(os.path.dirname(os.path.realpath(__file__)),
                         JSONFileName), 'r')  #It opens the JSON file
        data = json.load(jsonFile)  #It loads the file
        jsonFile.close()
    else:
        data = json.load(jsonFile)

    exportJSON = JSONOutput()
    stationId1 = 'M1'
    stationId2 = 'M2'
    data1 = exportJSON.ProcessingTimes(data, stationId1, distProcTime_MILL1)
    data2 = exportJSON.ProcessingTimes(data1, stationId2, distProcTime_MILL2)

    data3 = exportJSON.TTF(data2, stationId1, distMTTF_MILL1)
    data4 = exportJSON.TTR(data3, stationId1, distMTTR_MILL1)

    data5 = exportJSON.TTF(data4, stationId2, distMTTF_MILL2)
    data6 = exportJSON.TTR(data5, stationId2, distMTTR_MILL2)

    # if we run from test return the data6
    if test:
        return data6

    jsonFile = open('JSON_ParallelStations_Output.json',
                    "w")  #It opens the JSON file
    jsonFile.write(json.dumps(
        data6, indent=True))  #It writes the updated data to the JSON file
    jsonFile.close()  #It closes the file

    #=================== Calling the ExcelOutput object, outputs the outcomes of the statistical analysis in xls files ==========================#
    export = ExcelOutput()

    export.PrintStatisticalMeasures(procTime_MILL1,
                                    'procTimeMILL1_StatResults.xls')
    export.PrintStatisticalMeasures(procTime_MILL2,
                                    'procTimeMILL2_StatResults.xls')
    export.PrintStatisticalMeasures(MTTF_MILL1, 'MTTFMILL1_StatResults.xls')
    export.PrintStatisticalMeasures(MTTF_MILL2, 'MTTFMILL2_StatResults.xls')
    export.PrintStatisticalMeasures(MTTR_MILL1, 'MTTRMILL1_StatResults.xls')
    export.PrintStatisticalMeasures(MTTR_MILL2, 'MTTRMILL2_StatResults.xls')

    export.PrintDistributionFit(procTime_MILL1,
                                'procTimeMILL1_DistFitResults.xls')
    export.PrintDistributionFit(procTime_MILL2,
                                'procTimeMILL2_DistFitResults.xls')
    export.PrintDistributionFit(MTTF_MILL1, 'MTTFMILL1_DistFitResults.xls')
    export.PrintDistributionFit(MTTF_MILL2, 'MTTFMILL2_DistFitResults.xls')
    export.PrintDistributionFit(MTTR_MILL1, 'MTTRMILL1_DistFitResults.xls')
    export.PrintDistributionFit(MTTR_MILL2, 'MTTRMILL2_DistFitResults.xls')