#yp.prepRatio(hist)
# normalize to central value
#hist.Divide(hCentral)
hists.append(hist)
#for hist in hists: print hist.GetName()
# make stack/total syst hists
#total = yp.getTotal(hists)
stack = yp.getStack(hists)
#sqHist = yp.getSquaredSum(hists)
sqHist = yp.getSquaredSum(hists[::-1])
hCentralUncert = yp.getHistWithError(hCentral, sqHist)
hCentral.GetYaxis().SetRangeUser(0, 5.9)
hCentral.GetYaxis().SetTitleSize(0.15)
hCentral.GetYaxis().SetTitleOffset(0.17)
# save hists
allhists += hists + [hCentral, hCentralUncert, stack, sqHist]
canv = yp.plotHists(var + "_" + signame, [stack, sqHist],
[hCentral, hCentralUncert],
"TLC",
1200,
600,
nCols=2)
canv.SetName(canv.GetName() + "_Syst")
mcStack = yp.getStack(mcHists)
hTotal = yp.getTotal(mcHists)
# for MC closure
mcsamp = "EWK_poisson"
mcsamp = "background_poisson_QCDsubtr"
hMCpred = yp.makeSampHisto(yds,mcsamp,cat,"MC_prediction"); hMCpred.SetTitle("MC (Pred)")
# DATA
hDataPred = yp.makeSampHisto(yds,"data_QCDsubtr",cat,"Data_prediction"); hDataPred.SetTitle("Data (Pred)")
hData = yp.makeSampHisto(yds,"data_QCDsubtr","SR_MB","Data"); hData.SetTitle("Data")
## Append Systematics to prediction
print "Appending syst. unc. to prediction and total MC"
hDataPred = yp.getHistWithError(hDataPred, hKappaSysts, new = False)
hTotal = yp.getHistWithError(hTotal, hMCSysts, new = False)
# test MC
#hDataPred = hMCpred
# Ratio
#ratio = yp.getRatio(hTotal,hDataPred)
ratio = yp.getRatio(hData,hDataPred)
logY = True
#logY = False
cname = "Data_2p24fb_"+mask
canv = yp.plotHists("SR_MB_Prediction",[mcStack,hTotal,hDataPred,hData],ratio,'TM', 1200, 600, logY = logY)
#canv = yp.plotHists("MC_2p24fb_"+cat+'_'+mask,[mcStack,hTotal,hDataPred],ratio,'TM', 1200, 600, logY = logY)
# MC samps
samps = [(samp,cat) for samp in mcSamps]
mcHists = yp.makeSampHists(yds,samps)
# for MC closure
#mcsamp = "EWK_poisson"
#mcsamp = "background_poisson_QCDsubtr"
#hMCpred = yp.makeSampHisto(yds,mcsamp,cat,"MC_prediction"); hMCpred.SetTitle("MC (Pred)")
# DATA
hDataPred = yp.makeSampHisto(yds,"data_QCDsubtr",cat,"Data_prediction"); hDataPred.SetTitle("Prediction")
hData = yp.makeSampHisto(yds,"data_QCDsubtr","SR_MB","Data"); hData.SetTitle("Data")
## Append Systematics to prediction
print "Appending syst. unc. to prediction and total MC"
hDataPred = yp.getHistWithError(hDataPred, hKappaSysts, new = False)
# Do MC hists
scaleToHist(mcHists,hDataPred)
mcStack = yp.getStack(mcHists)
#hTotal = yp.getTotal(mcHists)
#hTotal = yp.getHistWithError(hTotal, hMCSysts, new = False)
hUncert = hDataPred.Clone("uncert")
hUncert.SetTitle("Pred. Uncertainty")
yp.setUnc(hUncert)
# test MC
#hDataPred = hMCpred
#hData.SetBinErrorOption(TH1.kPoisson)
hist.GetYaxis().SetTitleOffset(0.8)
#yp.prepKappaHist(hist)
#yp.prepRatio(hist)
# normalize to central value
#hist.Divide(hCentral)
hists.append(hist)
# make stack/total syst hists
#total = yp.getTotal(hists)
stack = yp.getStack(hists)
sqHist = yp.getSquaredSum(hists)
hCentralUncert = yp.getHistWithError(hCentral, sqHist)
canv = yp.plotHists(var+"_"+samp+"_Syst",[stack,sqHist],[hCentral,hCentralUncert],"TM", 1200, 600)
# canv = yp.plotHists(var+"_"+samp+"_Syst",[sqHist]+hists,[hCentral,hCentralUncert],"TM", 1200, 600)
# canv = yp.plotHists(var+"_"+samp+"_Stat",[stack,sqHist],hCentral,"TM", 1200, 600)
canvs.append(canv)
if not yp._batchMode: raw_input("Enter any key to exit")
# Save canvases
exts = [".pdf",".png",".root"]
#exts = [".pdf"]
#odir = "BinPlots/Syst/Combine/test/allSF_noPU_Wpol/Method1A/"
odir = "BinPlots/Syst/Combine/allSF_noPU_Wpol/Method1A/"
if not os.path.isdir(odir): os.makedirs(odir)