# 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)
