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
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
