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
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
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
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
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
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
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]
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
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
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]