# Category
#cat = "CR_MB"
cats = ["SR_MB_predict"]
canvs = []
for cat in cats:
# Totals
#hMCpred = yp.makeSampHisto(yds,"background_QCDsubtr",cat,"MC_prediction"); hMCpred.SetTitle("MC (Pred)")
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")
# Ratio
#ratio = yp.getRatio(hTotal,hDataPred)
ratio = yp.getRatio(hData,hDataPred)
#ratio.GetYaxis().SetRangeUser(0,5)
if doPoisErr:
from CMGTools.TTHAnalysis.plotter.mcPlots import getDataPoissonErrors
hDataPois = getDataPoissonErrors(hData,True,True)
hDataPois.SetName("DataPois")
hDataPois.SetTitle("Data")
ratioPois = yp.getRatio(hDataPois,hDataPred)
hPredUnc = yp.getRatio(hDataPred,hDataPred)
col = yp.kGray
hPredUnc.SetName("PredictionUncertainty")
hPredUnc.SetLineColor(1)
# 1
yp.colorDict[samp + "_1_" + cat] = yp.kBlue
h1 = yp.makeSampHisto(yds1, samp, cat, samp + "_1_" + cat)
h1.SetTitle(samp + " (with dilep corr.)")
# 2
yp.colorDict[samp + "_2_" + cat] = yp.kRed
h2 = yp.makeSampHisto(yds2, samp, cat, samp + "_2_" + cat)
h2.SetTitle(samp + " (std. weights)")
#h2.SetTitle(samp+" (only btagSF 2)")
#h2.SetTitle(samp+" (All but PU)")
#h2.SetTitle(samp+" (PUwgt < 4.)")
#h2.SetTitle(samp+" (1 hadrFlav)")
ratio = yp.getRatio(h1, h2)
ratio.GetYaxis().SetRangeUser(0.9, 1.10)
for i in range(0, ratio.GetNbinsX()):
print ratio.GetBinContent(i)
ratio.SetBinError(i, 0.0)
#h1.GetYaxis().SetRangeUser(0.55,1.45)
if "WJets" in samp and "Kappa" in cat:
h2.GetYaxis().SetRangeUser(0.05, 3.45)
h1.GetYaxis().SetRangeUser(0.05, 3.45)
hists += [h1, h2, ratio]
canv = yp.plotHists(cat + "_" + samp, [h1, h2],
ratio,
"TM",
1200,
yp.colorDict["Data_"+cat] = yp.kBlack
hData = yp.makeSampHisto(ydsSele,"data",cat,"Data_"+cat)
hData.SetTitle("Data (sele)")
# Data Ele
yp.colorDict["DataEle_"+cat] = yp.kOrange-3
hDataEle = yp.makeSampHisto(ydsSeleEle,"data",cat,"DataEle_"+cat)
hDataEle.SetTitle("Data (ele)")
# Data Ele
yp.colorDict["DataMu_"+cat] = yp.kCyan-3
hDataMu = yp.makeSampHisto(ydsSeleMu,"data",cat,"DataMu_"+cat)
hDataMu.SetTitle("Data (#mu)")
# ratios and plots
ratMu = yp.getRatio(hMuQCDpred,hDataMu)
ratMu.GetYaxis().SetRangeUser(0,0.105)
ratEle = yp.getRatio(hEleQCDpred,hDataEle)
ratEle.GetYaxis().SetRangeUser(0,0.35)
## combined
ratEle.SetLineColor(hEleQCDpred.GetLineColor())
ratEle.SetMarkerColor(hEleQCDpred.GetMarkerColor())
ratMu.SetLineColor(hMuQCDpred.GetLineColor())
ratMu.SetMarkerColor(hMuQCDpred.GetMarkerColor())
'''
#ratio = yp.getRatio(hMuAnti,hEleQCDpred)
ratio = yp.getRatio(hMuAnti,hDataMu)
yp.colorDict[samp+"_1A_"+cat] = yp.kBlue
h1A = yp.makeSampHisto(yds1A,samp,cat,samp+"_1A_"+cat)
h1A.SetTitle(samp+" (Method 1A)")
#h1A.SetTitle(samp+" (1A mcFlav)")
#h1A.SetTitle(samp+" (All SF w/o PU)")
# 1B
yp.colorDict[samp+"_1B_"+cat] = yp.kRed
h1B = yp.makeSampHisto(yds1B,samp,cat,samp+"_1B_"+cat)
h1B.SetTitle(samp+" (Method 1B)")
#h1B.SetTitle(samp+" (only btagSF 1B)")
#h1B.SetTitle(samp+" (All but PU)")
#h1B.SetTitle(samp+" (PUwgt < 4.)")
#h1B.SetTitle(samp+" (1A hadrFlav)")
ratio = yp.getRatio(h1A,h1B)
#ratio.GetYaxis().SetRangeUser(0.75,1.25)
#h1A.GetYaxis().SetRangeUser(0.55,1.45)
if "WJets" in samp and "Kappa" in cat:
h1B.GetYaxis().SetRangeUser(0.05,3.45)
h1A.GetYaxis().SetRangeUser(0.05,3.45)
hists += [h1A,h1B,ratio]
canv = yp.plotHists(cat+"_"+samp,[h1A,h1B],ratio,"TM", 1200, 600, logY = logY)
canvs.append(canv)
if not yp._batchMode:
answ = raw_input("Enter 'q' to exit: ")
if "q" in answ: exit(0)
# 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)
canv.SetName(cname + canv.GetName())
if logY: canv.SetName(canv.GetName() + "_log")
canvs.append(canv)
if not yp._batchMode: raw_input("Enter any key to exit")
# Save canvases
# Data Ele
yp.colorDict["DataMu_"+cat] = yp.kCyan-3
hDataMu = yp.makeSampHisto(ydsSeleMu,"data",cat,"DataMu_"+cat)
hDataMu.SetTitle("Data (#mu)")
## MC
#yp.colorDict["MC_Ele_"+cat] = kOrange-3
#hMCele = yp.makeSampHisto(ydsSeleEle,"background",cat,"MC_Ele_"+cat)
#hMCele.SetTitle("MC (ele)")
#yp.colorDict["MC_Mu_"+cat] = kCyan
#hMCmu = yp.makeSampHisto(ydsSeleMu,"background",cat,"MC_Mu_"+cat)
#yp.hMCmu.SetTitle("MC (#mu)")
#ratio = yp.getRatio(hMuAnti,hEleQCDpred)
ratio = yp.getRatio(hMuAnti,hDataMu)
#ratio.GetYaxis().SetRangeUser(-0.45,0.45)
#ratio.GetYaxis().SetRangeUser(0,0.95)
ratio.GetYaxis().SetRangeUser(0,0.105)
#canv = yp.plotHists("Sele_QCDvsMC_Lep_"+cat,[hMCele,hMCmu,hEle,hMu])
#canv = yp.plotHists("Data_vs_QCD_"+cat,[hDataEle,hDataMu,hEleQCDpred,hMuAnti],ratio)
canv = yp.plotHists("Data_vs_QCD_Mu_"+cat,[hDataMu,hMuAnti],ratio)
if not yp._batchMode: raw_input("Enter any key to exit")
exts = [".pdf",".png"]
#exts = [".pdf"]
for ext in exts:
canv.SaveAs("BinPlots/QCD/lumi2p1/DataCompare/"+mask+canv.GetName()+ext)
masses.append((mass, massName, yp.kBlack))
for (mass, massName, col) in masses:
signame = "T1tttt_Scan_" + mass
yp.colorDict[signame + "_Sig_" + cat] = col
hSig = yp.makeSampHisto(ydsSig, signame, cat, signame + "_Sig_" + cat)
#hSig.SetTitle("T1tttt_"+mass)
hSig.SetTitle("T1tttt " + massName)
sighists.append(hSig)
#print sighists
ratio = yp.getRatio(hSig, hTotal)
ratio.SetName("SigOvMC")
#ratio.GetYaxis().SetRangeUser(0.75,1.25)
hists += [hMC, hTotal, ratio] + sighists
#canv = yp.plotHists(cat,[hMC,hSig],ratio,"TM", 1200, 600, logY = True)
canv = yp.plotHists(cat, [hMC] + sighists,
None,
"TM",
1200,
600,
logY=True)
yp.gPad.RedrawAxis()
cname = "MC_vs_T1tttt_" + cat
canv.SetName(cname)
