# Initialisation AGN = createOzDESAGN(QSOtemplate) stack = Stacking() stats = Stats() conv = Convergence() plotOzDESAGN(QSOtemplate, plotIndividuals=False, binends=binends, name='MgII') # Create AGN AGN.generateAGN(limits, True, number, resDir, fName) output = AGN.generateLightCurveParameters(section, resDir+fName) AGN.printLightCurveParametersToFile(output, resDir+params) lag = printLagValueInParameterBin(resDir+params, limits, emLine=emLine) # Do the Javelin magic runJavelinOnFakeData(resDir+params, limits, number, resDir, string, emLine=emLine) # Stack and analyse LLH, lags, lagMaxes = stack.stackData(resDir, string, number, resDir+params, limits, meanTrueLag=lag, binsize=10, polydeg=20, plotLags=False, plotLLHs=False, plotStacked=True, indivStat=False) LLHmax, LLHmin, LLHplus = stats.getLikelihoodEstimates(lags, LLH, printResults=True) med1, med1min, med1max = stats.getMedianEstimates(LLH, lags, printResults=True) ##med2, med2min, med2max = stats.getMedianEstimates(lagMaxes, printResults=True) # Analysis of results plotAChain(chainDir+'MyChain'+string+'0.dat') R = conv.GelmanRubin(chainDir+'MyChain'+string+'0.dat', 10, 10, False) print 'Gelman-Rubin R =', R conv.plotGelmanRubin(chainDir+'MyChain'+string+'0.dat', 10, 10)