for bin in range(1, hist.GetNbinsX() + 1):
hist.SetBinContent(bin, flats[syst])
#yp.prepKappaHist(hist)
#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",
# set error
for i in xrange(1,hPredUnc.GetNbinsX()+1):
try:
hPredUnc.SetBinError(i,hDataPred.GetBinError(i)/hDataPred.GetBinContent(i))
except ZeroDivisionError:
hPredUnc.SetBinError(i, 0.)
# MC samps
samps = [(samp,cat) for samp in mcSamps]
mcHists = yp.makeSampHists(yds,samps)
# Scale MC hists to Prediction
scaleToHist(mcHists,hDataPred)
mcStack = yp.getStack(mcHists)
hUncert = hDataPred.Clone("uncert")
hUncert.SetTitle("Statistical Uncertainty only")
yp.setUnc(hUncert)
#canv = plotHists("DataNJ45_"+cat,[stack,hMCpred,hDataPred,hData,total],ratio)
width = 1200
height = 600
legPos = "TM"
if doSquare == True:
width = 600
height = 600
legPos = "Square"
if doPoisErr:
canv = yp.plotHists("SR_MB_Prediction",[mcStack,hUncert,hDataPois],[hPredUnc,ratioPois],legPos, width, height, logY = True)
hist.GetYaxis().SetTitle("Relative uncertainty")
hist.GetYaxis().SetTitleSize(0.04)
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/"
mcSamps = ['DY','TTV','SingleT','WJets','TT']
#mcSamps = ['EWK']
# update colors
yp.colorDict["MC_prediction"] = yp.kRed
yp.colorDict["Data_prediction"] = yp.kRed
# Category
cat = "SR_MB_predict"
# MC samps
samps = [(samp,cat) for samp in mcSamps]
mcHists = yp.makeSampHists(yds,samps)
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)
#samps = ["TTJets","WJets","TTV","DY","SingleTop"]
# mcSamps = ["EWK","TTJets","WJets","SingleTop","DY","TTV"]
#mcSamps = ['TTdiLep','TTsemiLep','WJets','TTV','SingleT','DY']
mcSamps = ['DY', 'TTV', 'SingleT', 'WJets', 'TTdiLep', 'TTsemiLep']
cat = "SR_MB"
#logY = True
logY = False
print "Making plot for", cat
# MC samps
samps = [(samp, cat) for samp in mcSamps]
mcHists = yp.makeSampHists(ydsMC, samps)
hMC = yp.getStack(mcHists)
hTotal = yp.getTotal(mcHists)
# Signals
sighists = []
masses = []
mass = "mGo1500_mLSP100"
massName = "(1500,100)"
masses.append((mass, massName, yp.kMagenta))
mass = "mGo1200_mLSP800"
massName = "(1200,800)"
masses.append((mass, massName, yp.kBlack))
for (mass, massName, col) in masses:
