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
