print "Plots for", patternnew
yds = yp.YieldStore("lepYields")
yds.addFromFiles(patternnew, ("lep", "sele"))
yds.showStats()
canvs = []
# Samples
samps = ["EWK"] #,"TT","TTdiLep","TTsemiLep","WJets"]
for samp in samps:
# RCS MB
yp.colorDict[samp + "_Rcs_MB"] = yp.kBlue
hRcsMB = yp.makeSampHisto(yds, samp, "Rcs_MB",
samp + "_Rcs_MB")
#hRcsMB.SetTitle("R_{CS} (MB)")
hRcsMB.SetTitle(
"R_{CS}^{MC}(n_{b-tag}, n_{jet}^{SR}, EWK)")
# hRcsMB.SetTitle("R_{CS}^{MC}(n_{b-tag}, n_{jet}#in [5], EWK)")
# RCS SB
yp.colorDict[samp + "_Rcs_SB"] = yp.kRed
hRcsSB = yp.makeSampHisto(yds, samp, "Rcs_SB",
samp + "_Rcs_SB")
#hRcsSB.SetTitle("R_{CS} (SB)")
hRcsSB.SetTitle(
"R_{CS}^{MC}(n_{b-tag}, n_{jet}#in [4,5], EWK)")
# hRcsSB.SetTitle("R_{CS}^{MC}(n_{b-tag}, n_{jet}#in [4], EWK)")
# Kappa
basename = os.path.basename(pattern)
mask = basename.replace("*","X_")
yds = yp.YieldStore("Sele")
yds.addFromFiles(pattern,("lep","sele"))
canvs = []
# Samples
samps = ["EWK","TT","TTdiLep","TTsemiLep","WJets"]
for samp in samps:
# RCS MB
yp.colorDict[samp+"_Rcs_MB"] = yp.kBlue
hRcsMB = yp.makeSampHisto(yds,samp,"Rcs_MB",samp+"_Rcs_MB")
hRcsMB.SetTitle("R_{CS} (MB)")
# RCS SB
yp.colorDict[samp+"_Rcs_SB"] = yp.kRed
hRcsSB = yp.makeSampHisto(yds,samp,"Rcs_SB",samp+"_Rcs_SB")
hRcsSB.SetTitle("R_{CS} (SB)")
# Kappa
yp.colorDict[samp+"_Kappa"] = yp.kBlack
hKappa = yp.makeSampHisto(yds,samp,"Kappa",samp+"_Kappa")
hKappa.SetTitle("#kappa")
yp.prepKappaHist(hKappa)
yp.prepRatio(hKappa)
#masses = ["mGo1200_mLSP800","mGo1500_mLSP100"]
var = "SR_MB"
# canvs
canvs = []
allhists = []
for mass in masses:
hists = []
# read in central value
signame = samp + "_" + mass
hCentral = yp.makeSampHisto(yds, signame, var, signame + "_central")
#print hCentral
yp.prepRatio(hCentral, True)
#hCentral.GetYaxis().SetRangeUser()
## Define flat values (i.e. for PU)
flats = {}
#flats["PU"] = 0.05
### Add flat systs (lumi, lepSF, triggEff, etc.)
#flats["stat"] = 0.1
flats["lepSF"] = 0.02
flats["lumi"] = 0.026
flats["iso"] = 0.04
flats["trig"] = 0.02
flats["PU"] = 0.1
# update colors
yp.colorDict["MC_prediction"] = yp.kGreen
yp.colorDict["Data_prediction"] = yp.kRed
# 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)
#
# samps = [signame]
cat = "Kappa"
#cat = "SR_MB"
#logY = True
logY = False
for samp in samps:
print "Making plot for", samp, cat
# 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)
print "Plots for", patternnew
yds = yp.YieldStore("lepYields")
yds.addFromFiles(patternnew,("lep","sele"))
yds.showStats()
canvs = []
# Samples
samps = ["EWK"]#,"TT","TTdiLep","TTsemiLep","WJets"]
for samp in samps:
# RCS MB
yp.colorDict[samp+"_Rcs_MB"] = yp.kBlue
hRcsMB = yp.makeSampHisto(yds,samp,"Rcs_MB",samp+"_Rcs_MB")
#hRcsMB.SetTitle("R_{CS} (MB)")
hRcsMB.SetTitle("R_{CS}^{MC}(n_{b-tag}, n_{jet}^{SR}, EWK)")
# RCS SB
yp.colorDict[samp+"_Rcs_SB"] = yp.kRed
hRcsSB = yp.makeSampHisto(yds,samp,"Rcs_SB",samp+"_Rcs_SB")
#hRcsSB.SetTitle("R_{CS} (SB)")
hRcsSB.SetTitle("R_{CS}^{MC}(n_{b-tag}, n_{jet}#in [4,5], EWK)")
# Kappa
yp.colorDict[samp+"_Kappa"] = yp.kRed
hKappa = yp.makeSampHisto(yds,samp,"Kappa",samp+"_Kappa")
hKappa.SetTitle("#kappa_{MC}")
yp.prepKappaHist(hKappa)
ydsSeleEle.addFromFiles(pattern,("ele","sele"))
ydsSeleMu = yp.YieldStore("MuSele")
ydsSeleMu.addFromFiles(pattern,("mu","sele"))
ydsAntiMu = yp.YieldStore("MuAnti")
ydsAntiMu.addFromFiles(pattern,("mu","anti"))
# Category
cats = ["CR_MB"]#,"CR_SB"]
for cat in cats:
# ele
yp.colorDict["Data_QCD_Ele_"+cat] = yp.kRed
hEleQCDpred = yp.makeSampHisto(ydsSeleEle,"data_QCDpred",cat,"Data_QCD_Ele_"+cat)
hEleQCDpred.SetTitle("QCD pred. (ele)")
# muons
'''
yp.colorDict["Data_QCD_Mu_"+cat] = yp.kBlue
hMuAnti = yp.makeSampHisto(ydsAntiMu,"data",cat,"Data_QCD_Mu_"+cat)
hMuAnti.SetTitle("Anti-Sele (#mu)")
# apply "F-ratio" = 10%
hMuAnti.Scale(1/10.); hMuAnti.SetTitle("Anti-Sele (#mu) x0.1")
'''
# mu
yp.colorDict["Data_QCD_Mu_"+cat] = yp.kBlue
hMuQCDpred = yp.makeSampHisto(ydsSeleMu,"data_QCDpred",cat,"Data_QCD_Mu_"+cat)
hMuQCDpred.SetTitle("QCD pred. (#mu)")
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)
# test MC
#hDataPred = hMCpred
# Ratio
#ratio = yp.getRatio(hTotal,hDataPred)
samps = ["EWK","TTJets","WJets","SingleTop","DY","TTV"]
samps = ["EWK"]
cat = "Kappa"
#cat = "CR_MB"
#logY = True
logY = False
for samp in samps:
print "Making plot for", samp, cat
# 1A
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)
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)
# 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("Pred.")
#hDataPred = yp.makeSampHisto(yds,"data",cat,"Data_prediction"); hDataPred.SetTitle("Prediction")
hData = yp.makeSampHisto(yds, "data", "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)
# Sample and variable
#samp = "EWK"
samps = ["EWK", "TTJets", "WJets", "SingleTop", "DY", "TTV"]
#samps = ['T_tWch','TToLeptons_tch','TBar_tWch', 'TToLeptons_sch',"EWK"]
samp = samps[0]
var = "Kappa"
#var = "SR_SB"
# canvs and hists
hists = []
canvs = []
# read in central value
hCentral = yp.makeSampHisto(yds, samp, var)
print "hCentral done"
yp.prepRatio(hCentral)
for i, syst in enumerate(systs):
yp.colorDict[syst + "_syst"] = sysCols[i]
sname = samp + "_" + syst + "_syst"
print "Making hist for", sname
hist = yp.makeSampHisto(yds, sname, var, syst + "_syst")
if syst in systNames: hist.SetTitle(systNames[syst])
else: hist.SetTitle(syst)
hist.GetYaxis().SetTitle("Relative uncertainty")
hist.GetYaxis().SetTitleSize(0.04)
# Sample and variable
samp = "EWK"
#samps = ["TTJets","WJets","SingleTop","DY","TTV"]
#samps = ['T_tWch','TToLeptons_tch','TBar_tWch', 'TToLeptons_sch',"EWK"]
#samp = samps[4]
var = "Kappa"
#var = "SR_SB"
# canvs and hists
hists = []
canvs = []
# read in central value
hCentral = yp.makeSampHisto(yds,samp,var)
yp.prepRatio(hCentral)
for i,syst in enumerate(systs):
yp.colorDict[syst+"_syst"] = sysCols[i]
sname = samp+"_"+syst+"_syst"
print "Making hist for", sname
hist = yp.makeSampHisto(yds,sname,var,syst+"_syst")
if syst in systNames: hist.SetTitle(systNames[syst])
else: hist.SetTitle(syst)
hist.GetYaxis().SetTitle("Relative uncertainty")
hist.GetYaxis().SetTitleSize(0.04)
hist.GetYaxis().SetTitleOffset(0.8)
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)
# 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")
# Signal
hSig1 = yp.makeSampHisto(ydsSig, "T1tttt_Scan_mGo1900_mLSP100", "SR_MB",
"T1tttt (1.9/0.1)")
hSig1.SetTitle("T1tttt (1.9/0.1)")
hSig2 = yp.makeSampHisto(ydsSig, "T1tttt_Scan_mGo1400_mLSP1100", "SR_MB",
"T1tttt (1.4/1.1)")
hSig2.SetTitle("T1tttt (1.4/1.1)")
## Append Systematics to prediction
print "Appending syst. unc. to prediction and total MC"
hDataPred = yp.getHistWithError(hDataPred, hKappaSysts, new=False)
# update colors
yp.colorDict["MC_prediction"] = yp.kGreen
yp.colorDict["Data_prediction"] = yp.kRed
# 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")
ydsSeleEle.addFromFiles(pattern,("ele","sele"))
ydsSeleMu = yp.YieldStore("MuSele")
ydsSeleMu.addFromFiles(pattern,("mu","sele"))
ydsAntiMu = yp.YieldStore("MuAnti")
ydsAntiMu.addFromFiles(pattern,("mu","anti"))
# Category
cats = ["CR_SB"]#,"CR_SB"]
for cat in cats:
# ele
yp.colorDict["Data_QCD_Ele_"+cat] = yp.kRed
hEleQCDpred = yp.makeSampHisto(ydsSeleEle,"data_QCDpred",cat,"Data_QCD_Ele_"+cat)
hEleQCDpred.SetTitle("QCD pred. (ele)")
# muons
yp.colorDict["Data_QCD_Mu_"+cat] = yp.kBlue
hMuAnti = yp.makeSampHisto(ydsAntiMu,"data",cat,"Data_QCD_Mu_"+cat)
hMuAnti.SetTitle("Anti-Sele (#mu)")
# apply "F-ratio" = 10%
hMuAnti.Scale(1/10.); hMuAnti.SetTitle("Anti-Sele (#mu) x0.1")
# Data
yp.colorDict["Data_"+cat] = yp.kBlack
hData = yp.makeSampHisto(ydsSele,"data",cat,"Data_"+cat)
hData.SetTitle("Data (sele)")
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:
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,
