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
D=Distributions()
dictScrap={} #Create a dictionary that holds the Geometric, which is a discrete statistical distribution of the processing times of each station
dictScrap['MA']= D.Geometric_distrfit(MA_Scrap)
dictScrap['M1A']= D.Geometric_distrfit(M1A_Scrap)
dictScrap['M1B']= D.Geometric_distrfit(M1B_Scrap)
dictScrap['M2A']= D.Geometric_distrfit(M2A_Scrap)
dictScrap['M2B']= D.Geometric_distrfit(M2B_Scrap)
dictScrap['M3A']= D.Geometric_distrfit(M3A_Scrap)
dictScrap['M3B']= D.Geometric_distrfit(M3B_Scrap)
dictScrap['MM']= D.Geometric_distrfit(MM_Scrap)
dictScrap['PrA']= D.Geometric_distrfit(PrA_Scrap)
dictScrap['PrB']= D.Geometric_distrfit(PrB_Scrap)
dictScrap['PaA']= D.Geometric_distrfit(PaA_Scrap)
dictScrap['PaB']= D.Geometric_distrfit(PaB_Scrap)
#Call the CMSDOutput object giving as attributes the dictionaries with the processing times distributions and the scrap quantities distributions
tree = et.parse("CMSD_example.xml") #This file will be parsed using the XML.ETREE Python library
exportCMSD=CMSDOutput()
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['CB'] = D.ks_test(CB_Proc)
dictProc['MM'] = D.ks_test(MM_Proc)
dictProc['FL'] = D.ks_test(FL_Proc)
dictProc['PrA'] = D.ks_test(PrA_Proc)
dictProc['PrB'] = D.ks_test(PrB_Proc)
dictProc['PaA'] = D.ks_test(PaA_Proc)
dictProc['Pb'] = D.ks_test(Pb_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
D = Distributions()
dictScrap = {
} #Create a dictionary that holds the Geometric, which is a discrete statistical distribution of the processing times of each station
dictScrap['MA'] = D.Geometric_distrfit(MA_Scrap)
dictScrap['M1A'] = D.Geometric_distrfit(M1A_Scrap)
dictScrap['M1B'] = D.Geometric_distrfit(M1B_Scrap)
dictScrap['M2A'] = D.Geometric_distrfit(M2A_Scrap)
dictScrap['M2B'] = D.Geometric_distrfit(M2B_Scrap)
dictScrap['M3A'] = D.Geometric_distrfit(M3A_Scrap)
dictScrap['M3B'] = D.Geometric_distrfit(M3B_Scrap)
dictScrap['CB'] = D.Geometric_distrfit(CB_Scrap)
dictScrap['MM'] = D.Geometric_distrfit(MM_Scrap)
dictScrap['FL'] = D.Geometric_distrfit(FL_Scrap)
dictScrap['PrA'] = D.Geometric_distrfit(PrA_Scrap)
dictScrap['PrB'] = D.Geometric_distrfit(PrB_Scrap)
dictScrap['PaA'] = D.Geometric_distrfit(PaA_Scrap)
dictScrap['Pb'] = D.Geometric_distrfit(Pb_Scrap)
