Python BasicStatisticalMeasures.mean Examples

Example #1

File: ProdLine_example.py Project: cpcdevelop/dream

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]
dictScrap['P8']= listScrap[7]
dictScrap['P9']= listScrap[8]

Example #2

File: Ass_Line_example.py Project: PanosBarlas/dream

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
#With the loop statement in the outcome of the currentWIP method, which is a dictionary with the name WIP, with a series of calculations the units to be processed are calculated by the WIP batches in the stations   

Example #3

File: Ass_Line_example.py Project: PanosBarlas/dream

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

Example #4

File: ProdLine_example.py Project: bchiroma/DreamProject_2

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]
dictScrap['P8']= listScrap[7]
dictScrap['P9']= listScrap[8]