def getConfidenceInterval(self, value_list, confidenceLevel):
from dream.KnowledgeExtraction.ConfidenceIntervals import ConfidenceIntervals
from dream.KnowledgeExtraction.StatisticalMeasures import StatisticalMeasures
BSM = StatisticalMeasures()
lb, ub = ConfidenceIntervals().ConfidIntervals(value_list,
confidenceLevel)
return {'lb': lb, 'ub': ub, 'avg': BSM.mean(value_list)}
def getConfidenceInterval(self, value_list, confidenceLevel):
from dream.KnowledgeExtraction.ConfidenceIntervals import ConfidenceIntervals
from dream.KnowledgeExtraction.StatisticalMeasures import StatisticalMeasures
BSM = StatisticalMeasures()
lb, ub = ConfidenceIntervals().ConfidIntervals(value_list, confidenceLevel)
return {"lb": lb, "ub": ub, "avg": BSM.mean(value_list)}
C= DetectOutliers() 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= StatisticalMeasures() 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=ReplaceMissingValues()
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=StatisticalMeasures()
#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= DataManipulation()
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]
