Example #1
0
 def ReplaceWithMedian(self,mylist):       #Replace in the given list the missing values with the median value 
     list1=self.DeleteMissingValue(mylist) #Create a new list, which is the given list deleting the missing values (calling the DeleteMissingValue method)
     A=BasicStatisticalMeasures()          #Call the BasicStatisticalMeasures to calculate the median value
     median=A.median(list1)                #Calculate the median value of the new list
     i=0
     for value in mylist:
         if value is '' :
             mylist[i]=median                #For the values in the initial given list if there is a missing value replace it with the mean value of the new list
         i+=1 
     return mylist                         #Return the given list, in which the missing values are replaced with the mean value
Example #2
0
 def DeleteExtremeOutliers(self,mylist):            #Delete only the  extreme ouliers in a given data set
     A= BasicStatisticalMeasures()
     Q1= A.quantile(mylist)[1]
     Q3= A.quantile(mylist)[3]
     IQ= A.IQR(mylist)
     LOF= Q1 - 3*IQ 
     UOF= Q3 + 3*IQ
     i=0
     listx=[]
     for value in mylist:
         if not (value<LOF or value>UOF):           #If the value is  beyond the outer fence ([LOF,UOF]) on either side (extreme outlier) doesn't pass the control and deleted  
             listx.append(value)
         i+=1
     return listx
Example #3
0
 def DeleteOutliers(self,mylist):              #Delete the ouliers (both mild and extreme) in a given data set
     A= BasicStatisticalMeasures()             #Call the BasicStatisticalMeasures to calculate the quantiles and interquartile range
     Q1= A.quantile(mylist)[1]
     Q3= A.quantile(mylist)[3]
     IQ= A.IQR(mylist)
     LIF= Q1 - 1.5*IQ                        #Calculate the lower inner fence
     UIF= Q3 + 1.5*IQ                        #Calculate the upper inner fence
     LOF= Q1 - 3*IQ                          #Calculate the lower outer fence
     UOF= Q3 + 3*IQ                          #Calculate the upper outer fence
     i=0
     listx=[]
     for value in mylist:                       
         if not ((value<LOF or value>UOF) or (value<LIF or value>UIF)):    #If the value is beyond the inner fence ([LIF,UIF]) on either side (mild outlier) or beyond the outer fence ([LOF,UOF]) on either side (extreme outlier) doesn't pass the control and deleted  
             listx.append(value)
         i+=1 
     return listx
Example #4
0
P2_Proc= ProcessingTimes.get('P2',[])
P3_Proc=ProcessingTimes.get('P3',[])
P8_Proc=ProcessingTimes.get('P8',[])
P9_Proc= ProcessingTimes.get('P9',[])

#Call the HandleMissingValues object and replace with zero the missing values in the lists with the scrap quantity data 
B=HandleMissingValues()
P7_Scrap= B.ReplaceWithZero(P7_Scrap)
P1_Scrap= B.ReplaceWithZero(P1_Scrap)
P2_Scrap= B.ReplaceWithZero(P2_Scrap)
P3_Scrap= B.ReplaceWithZero(P3_Scrap)
P8_Scrap= B.ReplaceWithZero(P8_Scrap)
P9_Scrap= B.ReplaceWithZero(P9_Scrap)

# #Call the BasicSatatisticalMeasures object 
C=BasicStatisticalMeasures()
#Create a list with values the calculated mean value of scrap quantity on the different stations in the line
listScrap=[C.mean(P1_Scrap),C.mean(P2_Scrap),C.mean(P3_Scrap),C.mean(P1_Scrap),C.mean(P2_Scrap),C.mean(P3_Scrap),C.mean(P7_Scrap),C.mean(P8_Scrap),C.mean(P8_Scrap),C.mean(P9_Scrap), C.mean(P9_Scrap)] 
 
F=DataManagement()
 
listScrap=F.round(listScrap)       #Round the mean values of the list so as to get integers

dictScrap={}
dictScrap['P1']= listScrap[0]
dictScrap['P2']= listScrap[1]
dictScrap['P3']= listScrap[2]
dictScrap['P4']= listScrap[3]
dictScrap['P5']= listScrap[4]
dictScrap['P6']= listScrap[5]
dictScrap['P7']= listScrap[6]
Example #5
0
C= HandleOutliers()
MA_Proc= C.DeleteOutliers(MA_Proc)
M1A_Proc= C.DeleteOutliers(M1A_Proc)
M1B_Proc= C.DeleteOutliers(M1B_Proc)
M2A_Proc= C.DeleteOutliers(M2A_Proc)
M2B_Proc= C.DeleteOutliers(M2B_Proc)
M3A_Proc= C.DeleteOutliers(M3A_Proc)
M3B_Proc= C.DeleteOutliers(M3B_Proc)
MM_Proc= C.DeleteOutliers(MM_Proc)
PrA_Proc= C.DeleteOutliers(PrA_Proc)
PrB_Proc= C.DeleteOutliers(PrB_Proc)
PaA_Proc= C.DeleteOutliers(PaA_Proc)
PaB_Proc= C.DeleteOutliers(PaB_Proc)

#Call the BasicStatisticalMeasures object and calculate the mean value of the processing times for each station 
E= BasicStatisticalMeasures()
meanMA_Proc= E.mean(MA_Proc)
meanM1A_Proc= E.mean(M1A_Proc)
meanM2A_Proc= E.mean(M2A_Proc)
meanM3A_Proc= E.mean(M3A_Proc)
meanM1B_Proc= E.mean(M1B_Proc)
meanM2B_Proc= E.mean(M2B_Proc)
meanM3B_Proc= E.mean(M3B_Proc)
meanMM_Proc= E.mean(MM_Proc)
meanPrA_Proc= E.mean(PrA_Proc)
meanPrB_Proc= E.mean(PrB_Proc)
meanPaA_Proc= E.mean(PaA_Proc)
meanPaB_Proc= E.mean(PaB_Proc)

stopTime= datetime.datetime(2014,3,27,8,40,00)   #Give the stop time, based on this the WIP levels in the assembly line are identified calling the WIP method 
WIP=currentWIP(processStory, stopTime) #Call the currentWIP method, giving as attributes the processStory dict and the stopTime
Example #6
0
B=HandleMissingValues()

P1_Scrap= B.ReplaceWithZero(P1_Scrap)
P2_Scrap= B.ReplaceWithZero(P2_Scrap)
P3_Scrap= B.ReplaceWithZero(P3_Scrap)
P4_Scrap= B.ReplaceWithZero(P4_Scrap)
P5_Scrap= B.ReplaceWithZero(P5_Scrap)
P6_Scrap= B.ReplaceWithZero(P6_Scrap)
P7_Scrap= B.ReplaceWithZero(P7_Scrap)
P8_Scrap= B.ReplaceWithZero(P8_Scrap)
P9_Scrap= B.ReplaceWithZero(P9_Scrap)
P10_Scrap= B.ReplaceWithZero(P10_Scrap)
P11_Scrap= B.ReplaceWithZero(P11_Scrap)

# #Call the BasicSatatisticalMeasures object 
C=BasicStatisticalMeasures()
#Create a list with values the calculated mean value of scrap quantity on the different stations in the line
listScrap=[C.mean(P1_Scrap),C.mean(P2_Scrap),C.mean(P3_Scrap),C.mean(P4_Scrap),C.mean(P5_Scrap),C.mean(P6_Scrap),C.mean(P7_Scrap),C.mean(P8_Scrap),C.mean(P9_Scrap),C.mean(P10_Scrap), C.mean(P11_Scrap)] 
 
D= DataManagement()
 
listScrap=D.round(listScrap)       #Round the mean values of the list so as to get integers

dictScrap={}
dictScrap['P1']= listScrap[0]
dictScrap['P2']= listScrap[1]
dictScrap['P3']= listScrap[2]
dictScrap['P4']= listScrap[3]
dictScrap['P5']= listScrap[4]
dictScrap['P6']= listScrap[5]
dictScrap['P7']= listScrap[6]