def go(fileList, varName):
hists = []
varBins = array.array('d', range(30, 110, 10) + range(125, 225, 25) + range(250, 450, 50))
counter = 0
histList = []
for iFile, mass in fileList:
hists.append(r.TH1F("hist%i" %counter, "", 20, 0, 1.0))
# hists.append(r.TH1F("hist%i" %counter, "", 30, 0, 300))
# hists.append(r.TH1F("hist%i" %counter, "", 44, -2.2, 2.2))
hists[counter].Sumw2()
hists[counter] = getHist(iFile, hists[counter], varName)
hists[counter].SetLineColor(counter + 1)
hists[counter].Scale(1/hists[counter].Integral(0, hists[counter].GetNbinsX()+1))
histList.append((hists[counter], "Z'(%s)#rightarrow #tau#tau" %(mass), 'l'))
counter += 1
c = r.TCanvas("c","Test", 400, 300)
max = hists[0].GetMaximum()
hists[0].SetTitle("; %s; A.U." %varName)
hists[0].GetYaxis().SetTitleOffset(1.1)
hists[0].Draw("EH")
for i in range(1, len(hists)):
hists[i].Draw("sameEH")
if hists[i].GetMaximum() > max:
max = hists[i].GetMaximum()
hists[0].SetMaximum(1.2*max)
position = (0.7, 0.85 - 0.06*4, 0.9, 0.85)
legends = tool.setMyLegend(position, histList)
legends.Draw('same')
psfile = '%s.pdf' %varName
c.Print('%s' %psfile)
def setLegend(position, histDict, observed, bins, signalName, drawWhichDY = 'DY_MC', category = '1M'):
histList = []
histList.append((observed, 'Observed'))
keyList = defaultOrder
if category == '0M':
keyList = defaultOrder0M
nbins = len(bins)
for ikey in keyList:
if ikey == 'signal':
if draw_cfg.addIntegrals:
histList.append((histDict[ikey], '%s (%.2f)' %(signalName, histDict[ikey].Integral(0, nbins+1))))
else:
histList.append((histDict[ikey], '%s' %signalName))
else:
if (ikey == 'Electroweak' or ikey == 'singleT'):
continue
if 'DY' in ikey and ikey != drawWhichDY:
continue
if drawWhichDY == 'DY+ttLep':
if ikey == 't#bar{t}':
continue
else:
if ikey == 't#bar{t}-ttLep':
continue
if draw_cfg.addIntegrals:
histList.append((histDict[ikey], '%s (%.2f)' %(getNames(ikey), histDict[ikey].Integral(0, nbins+1))))
else:
histList.append((histDict[ikey], '%s' %getNames(ikey)))
return tool.setMyLegend(position, histList)
def setLegend(position, histDict, option = 'width', mass = '500'):
histList = []
nbins = histDict['obs'].GetNbinsX()
if options.unblind:
if options.showIntegral:
histList.append((histDict['obs'], 'Observed (%.2f)' %histDict['obs'].Integral(0, nbins+1, option), 'lep'))
else:
histList.append((histDict['obs'], 'Observed', 'lep'))
else:
histList.append((histDict['obs'], 'Observed', 'lep'))
for ikey in reversed(stackOrder):
name = getName(ikey)
if options.showIntegral:
histList.append((histDict[ikey], '%s (%.2f)' %(name, histDict[ikey].Integral(0, nbins+1, option)), 'f'))
else:
histList.append((histDict[ikey], '%s' %(name), 'f'))
integral = histDict['signal'].Integral(0, nbins+1, option)
multiplication = ''
if getZPrimeXS(str(mass))[1] > 1:
multiplication = '%i x ' %getZPrimeXS(str(mass))[1]
if options.showIntegral:
histList.append((histDict['signal'], '%sZPrime_%s (%.2f)' %(multiplication, mass, integral), 'l'))
else:
histList.append((histDict['signal'], "%sZ'(%s)#rightarrow #tau#tau" %(multiplication, mass), 'l'))
return tool.setMyLegend(position, histList)
def setLegend(position, hist25, hist50, bins):
histList = []
nbins = len(bins)
histList.append((hist25, '25ns', 'L'))
histList.append((hist50, '50ns', 'L'))
return tool.setMyLegend(position, histList)
def setLegend(position, histDict, histDict2, bins, option = 'width'):
histList = []
nbins = len(bins)
if options.unblind:
histList.append((histDict['Observed'], 'Observed (%.2f)' %histDict['Observed'].Integral(0, nbins+1, option), 'lep'))
else:
histList.append((histDict['Observed'], 'Observed', 'lep'))
for ikey, iColor in reversed(defaultOrder):
name = ikey
if ikey == 'QCD':
ikey = 'QCD_D_for_A'
if ikey in histDict.keys():
if plots_cfg.addIntegrals:
if ikey == 'WJets':
if plots_cfg.unc:
integral, unc = IntegralAndError(histDict['WJets_OScontrol'], nbins, ikey, 'et')
histList.append((histDict[ikey], '%s (%.1f +/- %.1f)' %(name, integral, unc), 'f'))
else:
histList.append((histDict[ikey], '%s (%.2f)' %(name, histDict['WJets_OScontrol'].Integral(0, nbins+1, option)), 'f'))
# if plots_cfg.unc:
# integral, unc = IntegralAndError(histDict2[ikey], nbins)
# histList.append((histDict2[ikey], '%s (%.2f +/- %.2f)' %(name, integral, unc), 'f'))
else:
if plots_cfg.unc:
integral, unc = IntegralAndError(histDict2[ikey], nbins, ikey, 'et')
histList.append((histDict2[ikey], '%s (%.1f +/- %.1f)' %(ikey, integral, unc), 'f'))
else:
histList.append((histDict[ikey], '%s (%.2f)' %(name, histDict[ikey].Integral(0, nbins+1, option)), 'f'))
else:
histList.append((histDict[ikey], '%s' %name, 'f'))
if "ZPrime" in histDict.keys():
for iName, iSample, iCategory in plots_cfg.sampleList:
if "Z'" in iName:
signalName = iName
if signalScale != 1:
signalName += "x%i" %signalScale
if plots_cfg.addIntegrals:
if plots_cfg.unc:
integral, unc = IntegralAndError(histDict2["ZPrime"], nbins)
histList.append((histDict2["ZPrime"], "%s (%.1f +/- %.1f)" %(signalName, integral, unc), 'l'))
else:
histList.append((histDict["ZPrime"], "%s (%.2f)" %(signalName, histDict['ZPrime'].Integral(0, nbins+1, option)), 'l'))
else:
histList.append((histDict["ZPrime"], signalName, 'l'))
return tool.setMyLegend(position, histList)
def run(type = 'both'):
points = {"both_antiIso":([(0.2, 0.5, 2.546, 0.115), (0.5, 1.0, 2.370, 0.233), (1.0, 10, 2.279, 0.671)], r.kBlue),
"both_antiIso_MEt20":([(0.2, 0.5, 2.546, 0.151), (0.5, 1.0, 2.756, 0.360), (1.0, 10, 2.581, 0.895)], r.kRed),
"e_antiIso":([(0.15, 0.2, 1.668, 0.216), (0.2, 0.5, 1.933, 0.138), (0.5, 1.0, 2.924, 0.475), (1.0, 10, 1.178, 0.491)], r.kRed),
"e_antiIso_MEt20":([(0.15, 0.2, 1.596, 0.275), (0.2, 0.5, 1.508, 0.148), (0.5, 1.0, 2.976, 0.630), (1.0, 10, 3.118, 1.828)], r.kOrange),
"m_antiIso":([(0.15, 0.2, 1.707, 0.232), (0.2, 0.5, 1.819, 0.093), (0.5, 1.0, 1.798, 0.102), (1.0, 10, 1.528, 0.095)], r.kBlue),
"m_antiIso_MEt20":([(0.15, 0.2, 1.288, 0.237), (0.2, 0.5, 1.938, 0.133), (0.5, 1.0, 1.965, 0.145), (1.0, 10, 1.663, 0.126)], r.kCyan),
}
first = True
if type != 'both':
type = 'single'
psfile = 'QCD_scales_%s.pdf' %type
c = r.TCanvas("c","Test", 800, 600)
grs = []
histlist = []
for iKey in points.keys():
if type == 'both' and (not 'both' in iKey):
continue
elif type != 'both' and ('both' in iKey):
continue
grs.append(getGraph(points[iKey][0], points[iKey][1]))
histlist.append((r.TH1F('dummy_%s' %iKey, '', 1, 0 ,1), iKey, 'L'))
histlist[len(histlist)-1][0].SetLineColor(points[iKey][1])
if first:
grs[0].Draw("AP")
if type != 'both':
grs[0].SetMaximum(3.5)
grs[0].SetMinimum(0.8)
first = False
else:
grs[len(grs)-1].Draw("sameP")
r.gPad.SetTicky()
r.gPad.SetTickx()
r.gPad.SetLogx()
leg = tool.setMyLegend((0.5, 0.8 - 0.07*(len(histlist)-1), 0.85, 0.8), histlist)
leg.Draw("same")
c.Print('%s' %psfile)
c.Close()
def setLegend(position,
histDict,
observed,
bins,
signalName,
drawWhichDY='DY_MC',
category='1M'):
histList = []
histList.append((observed, 'Observed'))
keyList = defaultOrder
if category == '0M':
keyList = defaultOrder0M
nbins = len(bins)
for ikey in keyList:
if ikey == 'signal':
if draw_cfg.addIntegrals:
histList.append(
(histDict[ikey], '%s (%.2f)' %
(signalName, histDict[ikey].Integral(0, nbins + 1))))
else:
histList.append((histDict[ikey], '%s' % signalName))
else:
if (ikey == 'Electroweak' or ikey == 'singleT'):
continue
if 'DY' in ikey and ikey != drawWhichDY:
continue
if drawWhichDY == 'DY+ttLep':
if ikey == 't#bar{t}':
continue
else:
if ikey == 't#bar{t}-ttLep':
continue
if draw_cfg.addIntegrals:
histList.append(
(histDict[ikey], '%s (%.2f)' %
(getNames(ikey), histDict[ikey].Integral(0, nbins + 1))))
else:
histList.append((histDict[ikey], '%s' % getNames(ikey)))
return tool.setMyLegend(position, histList)
def setLegend(position, histDict, histDict2, bins, option = 'width'):
histList = []
nbins = len(bins)
if options.unblind:
histList.append((histDict['Observed'], 'Observed (%.2f)' %histDict['Observed'].Integral(0, nbins+1, option), 'lep'))
else:
histList.append((histDict['Observed'], 'Observed', 'lep'))
for ikey, iColor in reversed(defaultOrder):
name = ikey
if ikey == 'QCD':
ikey = 'QCD_C_for_A'
if ikey in histDict.keys():
if plots_cfg.addIntegrals:
if ikey == 'WJets':
histList.append((histDict[ikey], '%s (%.2f)' %(name, histDict['WJets_A_'].Integral(0, nbins+1, option)), 'f'))
# if plots_cfg.unc:
# integral, unc = IntegralAndError(histDict2[ikey], nbins)
# histList.append((histDict2[ikey], '%s (%.2f +/- %.2f)' %(name, integral, unc), 'f'))
else:
histList.append((histDict[ikey], '%s (%.2f)' %(name, histDict[ikey].Integral(0, nbins+1, option)), 'f'))
else:
histList.append((histDict[ikey], '%s' %name, 'f'))
if not (options.antiIso or options.antiEIso or options.antiMIso or options.noIso):
signalSampleName = 'ZPrime_500'
if plots_cfg.addIntegrals:
if plots_cfg.unc:
integral, unc = IntegralAndError(histDict2[signalSampleName], nbins)
histList.append((histDict2[signalSampleName], '%s (%.2f +/- %.2f)' %(signalSampleName, integral, unc), 'l'))
else:
histList.append((histDict[signalSampleName], '%s (%.2f)' %(signalSampleName, histDict[signalSampleName].Integral(0, nbins+1, option)), 'l'))
else:
histList.append((histDict[signalSampleName], signalSampleName, 'l'))
return tool.setMyLegend(position, histList)
def main():
file_13TeV = '/nfs_scratch/zmao/test/DYJetsToLL_all.root' #'/nfs_scratch/zmao/test/TTJets_all.root'#
file_8TeV = '/nfs_scratch/zmao/samples_Iso/tauESOn/normal/dy.root' #'/nfs_scratch/zmao/samples_Iso/tauESOff/normal/TT.root'#
vars2compare = [
("tau1Pt", [50, 30, 230]),
# ("tau2Pt", [50, 30, 230]),
# ("tau1Eta", [25, -2.5, 2.5]),
# ("tau2Eta", [25, -2.5, 2.5]),
# ("tau1Iso", [40, 0, 2.]),
# ("tau2Iso", [40, 0, 2.]),
# ("J1Pt", [50, 30, 230]),
# ("J2Pt", [50, 30, 230]),
# ("J1Eta", [40, -4, 4]),
# ("J2Eta", [40, -4, 4]),
# ("J1CSVbTag", [50, 0, 1]),
# ("J2CSVbTag", [50, 0, 1]),
#
# ("CSVJ1Pt", [50, 30, 230]),
# ("CSVJ2Pt", [50, 30, 230]),
# ("CSVJ1Eta", [40, -4, 4]),
# ("CSVJ2Eta", [40, -4, 4]),
# ("CSVJ1", [50, 0, 1]),
# ("CSVJ2", [50, 0, 1]),
#
# ("svMass", [35, 0, 350]),
# ("met", [25, 0, 200]),
# ("mJJ", [50, 0, 500])
]
histDicts = buildHistDict(vars2compare)
psfile = 'out.pdf'
c = r.TCanvas("c", "Test", 800, 600)
counter = 0
for iVar, Range in vars2compare:
unc13TeV = readFile(file=file_13TeV,
hist=histDicts[iVar]['13TeV'],
varName=iVar,
which='13TeV')
unc8TeV = readFile(file=file_8TeV,
hist=histDicts[iVar]['8TeV'],
varName=iVar,
which='8TeV')
#normalization
# histDicts[iVar]['13TeV'].Scale(1.0/histDicts[iVar]['13TeV'].Integral(0, histDicts[iVar]['13TeV'].GetNbinsX()+1))
# histDicts[iVar]['8TeV'].Scale(1.0/histDicts[iVar]['8TeV'].Integral(0, histDicts[iVar]['8TeV'].GetNbinsX()+1))
counter += 1
c.Clear()
histDicts[iVar]['13TeV'].SetTitle("13 TeV vs 8 TeV; %s; a.u" % iVar)
histDicts[iVar]['8TeV'].SetTitle("13 TeV vs 8 TeV; %s; a.u" % iVar)
histDicts[iVar]['8TeV'].SetLineColor(r.kRed)
if histDicts[iVar]['13TeV'].GetMaximum(
) >= histDicts[iVar]['8TeV'].GetMaximum():
histDicts[iVar]['13TeV'].Draw()
histDicts[iVar]['8TeV'].Draw('same')
else:
histDicts[iVar]['8TeV'].Draw()
histDicts[iVar]['13TeV'].Draw('same')
if counter == 1:
l = tool.setMyLegend(lPosition=[0.7, 0.85, 0.9, 0.7],
lHistList=[(histDicts[iVar]['13TeV'],
'13TeV'),
(histDicts[iVar]['8TeV'], '8TeV')])
l.Draw('same')
c.Print('%s(' % psfile)
if counter == len(vars2compare):
l.Draw('same')
c.Print('%s)' % psfile)
else:
l.Draw('same')
c.Print('%s' % psfile)
print "13TeV Integral: %.2f +- %.2f (fb)" % (
histDicts[iVar]['13TeV'].Integral(
0, histDicts[iVar]['13TeV'].GetNbinsX() + 1), unc13TeV)
print "8TeV Integral : %.2f +- %.2f (fb)" % (
histDicts[iVar]['8TeV'].Integral(
0, histDicts[iVar]['8TeV'].GetNbinsX() + 1), unc8TeV)
c.Close()
def setLegend(position, histDict, histDict2, bins, option='width'):
histList = []
nbins = len(bins)
if options.unblind:
histList.append(
(histDict['Observed'], 'Observed (%.2f)' %
histDict['Observed'].Integral(0, nbins + 1, option), 'lep'))
else:
histList.append((histDict['Observed'], 'Observed', 'lep'))
for ikey, iColor in reversed(defaultOrder):
name = ikey
if ikey == 'QCD':
ikey = 'QCD_D_for_A'
if ikey in histDict.keys():
if plots_cfg.addIntegrals:
if ikey == 'WJets':
if plots_cfg.unc:
integral, unc = IntegralAndError(
histDict['WJets_OScontrol'], nbins, ikey, 'et')
histList.append((histDict[ikey], '%s (%.1f +/- %.1f)' %
(name, integral, unc), 'f'))
else:
histList.append(
(histDict[ikey], '%s (%.2f)' %
(name, histDict['WJets_OScontrol'].Integral(
0, nbins + 1, option)), 'f'))
# if plots_cfg.unc:
# integral, unc = IntegralAndError(histDict2[ikey], nbins)
# histList.append((histDict2[ikey], '%s (%.2f +/- %.2f)' %(name, integral, unc), 'f'))
else:
if plots_cfg.unc:
integral, unc = IntegralAndError(
histDict2[ikey], nbins, ikey, 'et')
histList.append(
(histDict2[ikey],
'%s (%.1f +/- %.1f)' % (ikey, integral, unc),
'f'))
else:
histList.append(
(histDict[ikey],
'%s (%.2f)' % (name, histDict[ikey].Integral(
0, nbins + 1, option)), 'f'))
else:
histList.append((histDict[ikey], '%s' % name, 'f'))
if "ZPrime" in histDict.keys():
for iName, iSample, iCategory in plots_cfg.sampleList:
if "Z'" in iName:
signalName = iName
if signalScale != 1:
signalName += "x%i" % signalScale
if plots_cfg.addIntegrals:
if plots_cfg.unc:
integral, unc = IntegralAndError(histDict2["ZPrime"], nbins)
histList.append(
(histDict2["ZPrime"],
"%s (%.1f +/- %.1f)" % (signalName, integral, unc), 'l'))
else:
histList.append(
(histDict["ZPrime"],
"%s (%.2f)" % (signalName, histDict['ZPrime'].Integral(
0, nbins + 1, option)), 'l'))
else:
histList.append((histDict["ZPrime"], signalName, 'l'))
return tool.setMyLegend(position, histList)
#gr_unsliced2.Draw("sameP")
gr_unsliced_pu.SetFillStyle(3354)
gr_unsliced_pu.SetLineWidth(-802)
gr_unsliced_pu.SetFillColor(r.kBlue)
gr_unsliced_pu.SetLineColor(r.kBlue)
gr_unsliced_pu.Draw("same2")
gr_unsliced_pu2.SetLineWidth(-802)
gr_unsliced_pu2.SetLineColor(r.kBlue)
gr_unsliced_pu2.SetMarkerColor(r.kBlue)
gr_unsliced_pu2.Draw("sameP")
gr_sliced.SetLineColor(r.kGreen)
gr_sliced.SetMarkerColor(r.kGreen)
#gr_sliced.Draw("sameLP")
gr_sliced_pu.SetLineColor(r.kRed)
gr_sliced_pu.SetMarkerColor(r.kRed)
gr_sliced_pu.Draw("sameLP")
position = (0.15, 0.85 - 0.05 * 2, 0.55, 0.85)
histList = []
#histList.append((gr_unsliced, "wide mass bin without PU", 'f'))
histList.append((gr_unsliced_pu, "wide mass bin", 'f'))
#histList.append((gr_sliced, "narrow mass bin without PU", 'l'))
histList.append((gr_sliced_pu, "narrow mass bin", 'l'))
legends = tool.setMyLegend(position, histList)
legends.Draw("same")
c.Print('pdfSys_withUnc.pdf')
def compareSamples(fs):
varNameDict_et = {'m_eff': 'm(#tau_{e}, #tau_{h}, #slash{E}_{T}) [GeV]',
'ePt': 'Electron Pt [GeV]',
'tPt': 'Tau Pt [GeV]',
'nTruePU': 'nTruePU',
}
varNameDict_em = {'m_eff': 'm(#tau_{e}, #tau_{#mu}, #slash{E}_{T}) [GeV]',
'ePt': 'Electron Pt [GeV]',
'mPt': 'Muon Pt [GeV]',
'nTruePU': 'nTruePU',
}
varName = 'ePt'
varBins = 0
if varName == 'm_eff':
varBins = array.array('d', [0,100,110,120,130,140,150,160,170,180,190,200,225,250,275,300,400,600,900])
hist1 = r.TH1F("hist_1", "", len(varBins)-1, varBins)
hist2 = r.TH1F("hist_2", "", len(varBins)-1, varBins)
else:
hist1 = r.TH1F("hist_1", "", 50, 0, 500)
hist2 = r.TH1F("hist_2", "", 50, 0, 500)
loopOneFile(iSample = '/user_data/zmao/Jan13Production_signalRegion/ZPrime_500_all_SYNC_%s_noIso.root' %fs,
varName = varName,
hist = hist1,
fs = fs,
varBins = varBins)
loopOneFile(iSample = '/user_data/zmao/Jan13Production_signalRegion/ZPrime_500_2_all_SYNC_%s_noIso.root' %fs,
varName = varName,
hist = hist2,
fs = fs,
varBins = varBins)
position = (0.65, 0.85 - 0.06*2, 0.85, 0.85)
histList = []
histList.append((hist1, 'Old Zprime_500', 'lp'))
histList.append((hist2, 'New Zprime_500', 'lp'))
legend = tool.setMyLegend(position, histList)
c = r.TCanvas("c","Test", 800, 600)
hist1.SetMarkerSize(0.5)
hist1.SetLineColor(r.kBlue)
hist1.SetMarkerColor(r.kBlue)
hist2.SetMarkerSize(0.5)
hist2.SetLineColor(r.kRed)
hist2.SetMarkerColor(r.kRed)
if fs == 'et':
title = varNameDict_et[varName]
else:
title = varNameDict_em[varName]
hist1.SetTitle("ZPrime Sample Comparison; %s; Events/Initial Events" %title)
hist1.SetMinimum(0.000001)
hist1.Scale(1, 'width')
hist2.Scale(1, 'width')
hist1.Draw("E1")
hist2.Draw("E1same")
legend.Draw('same')
r.gPad.SetLogy()
r.gPad.SetTicky()
r.gPad.SetTickx()
c.Print('sampleCheck_%s_%s.pdf' %(fs, varName))
gr_unsliced_pu.SetFillColor(r.kBlue)
gr_unsliced_pu.SetLineColor(r.kBlue)
gr_unsliced_pu.Draw("same2")
gr_unsliced_pu2.SetLineWidth(-802)
gr_unsliced_pu2.SetLineColor(r.kBlue)
gr_unsliced_pu2.SetMarkerColor(r.kBlue)
gr_unsliced_pu2.Draw("sameP")
gr_sliced.SetLineColor(r.kGreen)
gr_sliced.SetMarkerColor(r.kGreen)
#gr_sliced.Draw("sameLP")
gr_sliced_pu.SetLineColor(r.kRed)
gr_sliced_pu.SetMarkerColor(r.kRed)
gr_sliced_pu.Draw("sameLP")
position = (0.15, 0.85 - 0.05*2, 0.55, 0.85)
histList = []
#histList.append((gr_unsliced, "wide mass bin without PU", 'f'))
histList.append((gr_unsliced_pu, "wide mass bin", 'f'))
#histList.append((gr_sliced, "narrow mass bin without PU", 'l'))
histList.append((gr_sliced_pu, "narrow mass bin", 'l'))
legends = tool.setMyLegend(position, histList)
legends.Draw("same")
c.Print('pdfSys_withUnc.pdf')
def run(mass, FS):
inputFile = '/user_data/elaird/svSkim-sep18/ZPrime_%s_all_SYNC_%s_inclusive.root' %(mass, FS)
bins = array.array('d', range(0, 1000, 50) + range(1000, 4000, 100))
bins2 = array.array('d', range(0, 1000, 50) + range(1000, 8000, 100))
maximum = 0.005
if mass == "2000":
bins = array.array('d', range(0, 1000, 50) + range(1000, 3000, 100))
if mass == '1000':
bins = array.array('d', range(0, 1000, 50) + range(1000, 1500, 100))
maximum = 0.01
varsDict = {"mass_vis": (r.TH1D("mass_vis_%s_%s" %(mass, FS), "", len(bins)-1, bins), r.kRed),
"mass_gen": (r.TH1D("mass_gen_%s_%s" %(mass, FS), "", len(bins)-1, bins), r.kBlue),
"mass_withMEt": (r.TH1D("mass_withMEt_%s_%s" %(mass, FS), "", len(bins)-1, bins), r.kGreen),
"mass_svfit": (r.TH1D("mass_svfit_%s_%s" %(mass, FS), "", len(bins)-1, bins), r.kBlack),
}
varsDict2 = {"mass_vis": (r.TH1D("mass2_vis_%s_%s" %(mass, FS), "", len(bins2)-1, bins2), r.kRed),
"mass_gen": (r.TH1D("mass2_gen_%s_%s" %(mass, FS), "", len(bins2)-1, bins2), r.kBlue),
"mass_withMEt": (r.TH1D("mass2_withMEt_%s_%s" %(mass, FS), "", len(bins2)-1, bins2), r.kGreen),
"mass_svfit": (r.TH1D("mass2_svfit_%s_%s" %(mass, FS), "", len(bins2)-1, bins2), r.kBlack),
}
nbins = 60
varsDict3 = {"mass_vis": (r.TH1D("mass3_vis_%s_%s" %(mass, FS), "", nbins, -1.5, 1.5), r.kRed),
"mass_withMEt": (r.TH1D("mass3_withMEt_%s_%s" %(mass, FS), "", nbins, -1.5, 1.5), r.kGreen),
"mass_svfit": (r.TH1D("mass3_svfit_%s_%s" %(mass, FS), "", nbins, -1.5, 1.5), r.kBlack),
}
varsDict = loop_through_files(inputFile, varsDict, FS)
varsDict2 = loop_through_files(inputFile, varsDict2, FS)
varsDict3 = loop_through_files(inputFile, varsDict3, FS, True)
psfile = 'mass_compare_%s_%s.pdf' %(mass, FS)
histlist = []
histlist3 = []
c = r.TCanvas("c","Test", 800, 600)
r.gPad.SetTicky()
r.gPad.SetTickx()
print ''
for iKey in varsDict.keys():
varsDict[iKey][0].Scale(1.0/varsDict[iKey][0].Integral(0, len(bins) + 1), 'width')
varsDict[iKey][0].SetLineColor(varsDict[iKey][1])
histlist.append((varsDict[iKey][0], "%s: #bar{x} = %.2f, RMS = %.2f, RMS/#bar{x} = %.1f%%" %(iKey, varsDict2[iKey][0].GetMean(), varsDict2[iKey][0].GetRMS(), varsDict2[iKey][0].GetRMS()/varsDict2[iKey][0].GetMean()*100), 'l'))
if len(histlist) == 1:
varsDict[iKey][0].SetMaximum(maximum)
varsDict[iKey][0].Draw()
varsDict[iKey][0].SetTitle("Zprime %s (%s); %s mass; A.U" %(mass, FS, FS))
else:
varsDict[iKey][0].Draw("same")
if mass != '500':
leg = tool.setMyLegend((0.1, 0.8 - 0.07*(len(histlist)-1), 0.6, 0.8), histlist)
else:
leg = tool.setMyLegend((0.35, 0.8 - 0.07*(len(histlist)-1), 0.85, 0.8), histlist)
leg.Draw("same")
c.Print('%s(' %psfile)
c.Clear()
for iKey in varsDict3.keys():
varsDict3[iKey][0].Scale(1.0/varsDict3[iKey][0].Integral(0, nbins + 1), 'width')
varsDict3[iKey][0].SetLineColor(varsDict3[iKey][1])
histlist3.append((varsDict3[iKey][0], "%s" %(iKey), 'l'))
if len(histlist3) == 1:
varsDict3[iKey][0].SetMaximum(3)
varsDict3[iKey][0].Draw()
varsDict3[iKey][0].SetTitle("Zprime %s (%s); %s mass resolution; A.U" %(mass, FS, FS))
else:
varsDict3[iKey][0].Draw("same")
leg2 = tool.setMyLegend((0.6, 0.8 - 0.07*(len(histlist3)-1), 0.85, 0.8), histlist3)
leg2.Draw("same")
c.Print('%s)' %psfile)
c.Close()
def getHistos(varName, signalSelection, logY, sigBoost, nbins, useData, max, rangeMin, rangeMax, location, bTag, predict, predictPtBin, region, thirdLeptonVeto, SF, yMax, relaxedRegionOption, usePU):
r.gStyle.SetOptStat(0)
SF = float(SF)
fileList = draw_cfg.MCFileList
histList = []
histList_4QCD = []
QCDHistList = []
QCDHistList_4KS = []
QCDHistList_withScale = []
varRange = [nbins, rangeMin, rangeMax]
nBins = 10000
Lumi = 19.7
legendHistos = []
var_background = []
scaleMCPt = 1.0
tmpFile = []
tmpTree = []
var_data_4KS = []
var_data = []
var_data_4QCD = []
histList_4KS = []
MC_Counts_0 = 0.0
MC_Counts_1 = 0.0
MC_Counts_2 = 0.0
for i in range(6):
var_data.append(r.TH1F('data_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
var_data_4KS.append(r.TH1F('data_4KS_%i' %(i),"", nBins, varRange[1], varRange[2]))
if i < 5:
var_data_4QCD.append(r.TH1F('data_4QCD_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
dataName = draw_cfg.dataFile
fData = r.TFile(dataName)
treeData = fData.Get('eventTree')
print 'Adding events from: %s ...' %dataName
for iEntry in range(treeData.GetEntries()):
treeData.GetEntry(iEntry)
select = passCut(treeData, bTag, region, thirdLeptonVeto, relaxedRegionOption)
if (select == 0) or (select > 6):
continue
if (select == 1) and dontUnblind(treeData):
continue
var_data[select-1].Fill(varsList.findVar(treeData, varName))
if (select != 1):
var_data_4KS[select-2].Fill(varsList.findVar(treeData, varName))
if select == 2:
var_data_4QCD[0].Fill(varsList.findVar(treeData, varName), 1.0)
elif select == 3:
var_data_4QCD[1].Fill(varsList.findVar(treeData, varName), SF)
elif select == 4:
var_data_4QCD[2].Fill(varsList.findVar(treeData, varName), 1.0)
var_data_4QCD[3].Fill(varsList.findVar(treeData, varName), SF)
legendHistos.append([])
for j in range(6):
var_data[j].SetMarkerStyle(8)
var_data[j].SetMarkerSize(0.9)
legendHistos.append([])
integral = 'observed'
if j != 0 or (region != 'tight') or (bTag == 'None'):
integral = 'observed (%.0f)' %var_data[j].Integral(0, varRange[0]+1)
legendHistos[j].append((var_data[j], integral))
for i in range(len(fileList)):
for j in range(6):
histList_4KS.append(r.TH1F('%s_%i_KS' %(fileList[i][0],j),fileList[i][0], nBins, varRange[1], varRange[2]))
histList.append(r.TH1F('%s_%i' %(fileList[i][0],j),fileList[i][0], varRange[0], varRange[1], varRange[2]))
histList_4QCD.append(r.TH1F('%s_%i_2' %(fileList[i][0],j),fileList[i][0], varRange[0], varRange[1], varRange[2]))
print 'Adding events from: %s ...' %(fileList[i][1])
tmpFile.append(r.TFile(fileList[i][1]))
tmpTree.append(tmpFile[i].Get('eventTree'))
for iEntry in range(tmpTree[i].GetEntries()):
tmpTree[i].GetEntry(iEntry)
select = passCut(tmpTree[i], bTag, region, thirdLeptonVeto, relaxedRegionOption)
if (not select) or (select > 6):
continue
if usePU:
allWeights = tmpTree[i].triggerEff*tmpTree[i].PUWeight
else:
allWeights = tmpTree[i].triggerEff
histList[6*i+select-1].Fill(varsList.findVar(tmpTree[i], varName), allWeights*tmpTree[i].xs/(tmpTree[i].initEvents))
histList_4KS[6*i+select-1].Fill(varsList.findVar(tmpTree[i], varName), allWeights*tmpTree[i].xs/(tmpTree[i].initEvents))
if select == 2:
histList_4QCD[6*i].Fill(varsList.findVar(tmpTree[i], varName), allWeights*1.0*tmpTree[i].xs/(tmpTree[i].initEvents))
elif select == 3:
histList_4QCD[6*i+1].Fill(varsList.findVar(tmpTree[i], varName), allWeights*SF*tmpTree[i].xs/(tmpTree[i].initEvents))
elif select == 4:
histList_4QCD[6*i+2].Fill(varsList.findVar(tmpTree[i], varName), allWeights*1.0*tmpTree[i].xs/(tmpTree[i].initEvents))
histList_4QCD[6*i+3].Fill(varsList.findVar(tmpTree[i], varName), allWeights*SF*tmpTree[i].xs/(tmpTree[i].initEvents))
for j in range(6):
var_background.append(r.THStack())
histList[6*i+j].SetFillColor(fileList[i][2])
histList[6*i+j].Scale(Lumi)
histList_4QCD[6*i+j].Scale(Lumi)
histList_4KS[6*i+j].Scale(Lumi)
var_background[j].Add(histList[6*i+j])
legendHistos[j].append((histList[6*i+j], '%s (%.2f)' %(fileList[i][0], histList[6*i+j].Integral(0, varRange[0]+1))))
data_i = []
MC_i = []
data_r = []
MC_r = []
e = []
MC_List = []
for i in range(3):
QCDHistList.append(r.TH1F('QCD_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
QCDHistList_4KS.append(r.TH1F('QCD_%i_KS' %(i),"", nBins, varRange[1], varRange[2]))
MC_List.append(r.TH1F('MC_total_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
for j in range(varRange[0]+2):
dataValue = var_data[i+1].GetBinContent(j)
dataError = var_data[i+1].GetBinError(j)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList[6*k+1+i].GetBinContent(j)
if i == 0:
data_i.append(dataValue)
e.append(dataError)
MC_i.append(MCValue)
if i == 2:
data_r.append(dataValue)
MC_r.append(MCValue)
MC_List[i].SetBinContent(j, MCValue)
if dataValue - MCValue > 0:
QCDHistList[i].SetBinContent(j, dataValue - MCValue)
QCDHistList[i].SetBinError(j, dataError)
MC_List[i].Sumw2()
for j in range(nBins+2):
dataValue4KS = var_data_4KS[i].GetBinContent(j)
MCValue4KS = 0
for k in range(len(fileList)):
MCValue4KS += histList_4KS[6*k+1+i].GetBinContent(j)
if dataValue4KS - MCValue4KS > 0:
QCDHistList_4KS[i].SetBinContent(j, dataValue4KS - MCValue4KS)
ss_t = QCDHistList[0].Integral(0, varRange[0]+1)
ss_l = QCDHistList[2].Integral(0, varRange[0]+1)
os_l = QCDHistList[1].Integral(0, varRange[0]+1)
os_l_data = var_data[2].Integral(0, varRange[0]+1)
print "QCD in SS_T: %.4f" %ss_t
print "QCD in SS_L: %.4f" %ss_l
print "QCD in OS_L: %.4f" %os_l
print "Data in OS_L:%.4f" %os_l_data
print "SF: %.4f" %(ss_t/ss_l)
print "SF qcd/data: %.4f" %(os_l/os_l_data)
for i in range(4):
QCDHistList_withScale.append(r.TH1F('QCD_withScale_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
for j in range(varRange[0]+2):
dataValue = var_data_4QCD[i].GetBinContent(j)
dataError = var_data_4QCD[i].GetBinError(j)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList_4QCD[6*k+i].GetBinContent(j)
if dataValue - MCValue > 0:
QCDHistList_withScale[i].SetBinContent(j, dataValue - MCValue)
QCDDiff = r.TH1F('QCD_diff',"", varRange[0], varRange[1], varRange[2])
QCDDiff2 = r.TH1F('QCD_diff2',"", varRange[0], varRange[1], varRange[2])
QCDDiff_R2T = r.TH1F('QCDDiff_R2T',"", varRange[0], varRange[1], varRange[2])
# QCDDiff2.Sumw2()
fit1 = r.TF1("fit1","[0]", varRange[1],varRange[2])
fit1.SetParName(0,'scale')
fit1.FixParameter(0,1.0)
QCDDiff.Fit('fit1', '0EM')
fit1.SetLineStyle(2)
fit1.SetLineColor(r.kRed)
fit2 = r.TF1("fit2","[0]", varRange[1],varRange[2])
fit2.SetParName(0,'scale')
fit2.FixParameter(0,1.0)
QCDDiff2.Fit('fit2', '0EM')
fit2.SetLineStyle(2)
fit2.SetLineColor(r.kRed)
DrawSignal = False
if signalSelection != '':
var_signal = []
var_signal_4KS = []
for i in range(6):
var_signal.append(r.TH1F('%s_%i' %(signalSelection,i),"", varRange[0], varRange[1], varRange[2]))
var_signal_4KS.append(r.TH1F('%s_%i_4KS' %(signalSelection,i),"", nBins, varRange[1], varRange[2]))
signalDict = draw_cfg.signalDict
if signalSelection in signalDict:
fSignal = r.TFile(signalDict[signalSelection])
treeSignal = fSignal.Get('eventTree')
print 'Adding events from: %s ...' %(signalDict[signalSelection])
for iEntry in range(treeSignal.GetEntries()):
treeSignal.GetEntry(iEntry)
select = passCut(treeSignal, bTag, region, thirdLeptonVeto, relaxedRegionOption)
if (not select) or (select > 6):
continue
if usePU:
allWeights = treeSignal.triggerEff*treeSignal.PUWeight
else:
allWeights = treeSignal.triggerEff
var_signal[select-1].Fill(varsList.findVar(treeSignal, varName), allWeights*treeSignal.xs/(treeSignal.initEvents))
var_signal_4KS[select-1].Fill(varsList.findVar(treeSignal, varName), allWeights*treeSignal.xs/(treeSignal.initEvents))
initNEventsSignal = fSignal.Get('preselection')
for i in range(6):
var_signal[i].SetLineStyle(7)
var_signal[i].SetLineWidth(4)
var_signal[i].Scale(sigBoost*Lumi)
if sigBoost != 1:
sum = var_signal[i].Integral(0, var_signal[i].GetNbinsX()+1)
legendHistos[i].append((var_signal[i], '%sx%0.f (%.2f)' %(signalSelection, sigBoost, var_signal[i].Integral(0, varRange[0]+1))))
else:
legendHistos[i].append((var_signal[i], '%s (%.2f)' %(signalSelection, var_signal[i].Integral(0, varRange[0]+1))))
DrawSignal = True
else:
print '%s not supported, please use H260, H300 or H350' %signalSelection
scale_SS2OS = fit1.GetParameter(0)
scale_er_SS2OS = fit1.GetParError(0)
scale_relaxed2Tight = fit2.GetParameter(0)
scale_er_relaxed2Tight = fit2.GetParError(0)
QCDHistList_withScale[0].Scale(scale_SS2OS)
QCDHistList_withScale[1].Scale(scale_relaxed2Tight)
QCDHistList_withScale[2].Scale(scale_SS2OS)
QCDHistList_withScale[3].Scale(scale_relaxed2Tight)
QCDHistList_withScale[3].SetFillColor(r.TColor.GetColor(250,202,255))
QCDHistList_withScale[2].SetFillColor(r.TColor.GetColor(250,202,255))
QCDHistList_withScale[0].SetLineColor(r.TColor.GetColor(250,202,255))
QCDHistList_withScale[0].SetLineWidth(2)
QCDHistList_withScale[1].SetLineStyle(2)
QCDHistList_withScale[1].SetLineColor(r.TColor.GetColor(250,202,255))
QCDHistList_withScale[1].SetLineWidth(2)
var_background[1].Add(QCDHistList_withScale[3])
var_background[2].Add(QCDHistList_withScale[2])
legendHistos[1].append((QCDHistList_withScale[3], 'QCD (%.2f)' %QCDHistList_withScale[3].Integral(0, varRange[0]+1)))
legendHistos[2].append((QCDHistList_withScale[2], 'QCD (%.2f)' %QCDHistList_withScale[2].Integral(0, varRange[0]+1)))
allStacked = var_background[0].Clone()
QCDPredict = QCDHistList_withScale[1].Clone()
QCDPredict.SetLineStyle(1)
QCDPredict.SetLineWidth(1)
QCDPredict.SetLineColor(r.kBlack)
legendHistos[0].append((QCDPredict, 'QCD (%.0f, SF = %.3f)' %(QCDPredict.Integral(0, varRange[0]+1), SF)))
QCDPredict.SetFillColor(r.TColor.GetColor(250,202,255))
allStacked.Add(QCDPredict)
QCDHistList_withScale[1] = tool.addFakeTHStack(QCDHistList_withScale[1],var_background[0])
QCDHistList_withScale[0] = tool.addFakeTHStack(QCDHistList_withScale[0],var_background[0])
# var_data[1].Sumw2()
# MC_List[1].Sumw2()
for i in range(varRange[0]):
oldValue = var_data[2].GetBinContent(i+1)
mcValue = MC_List[1].GetBinContent(i+1)
if oldValue - mcValue > 0:
QCDDiff2.SetBinContent(i+1, (oldValue - mcValue)/oldValue)
QCDDiff2.SetBinError(i+1, MC_List[1].GetBinError(i+1)/oldValue)
print QCDDiff2.Integral(0, varRange[0]+1)
QCDDiff = var_data[2].Clone()
QCDDiff_sub = QCDHistList_withScale[2].Clone() + MC_List[1].Clone()
QCDDiff.Divide(QCDDiff_sub)
QCDDiff_R2T = var_data[1].Clone()
QCDDiff_R2T_sub = QCDHistList_withScale[3].Clone() + MC_List[0].Clone()
QCDDiff_R2T.Divide(QCDDiff_R2T_sub)
legendPosition = (0.6, 0.7, 0.90, 0.88)
l = []
r.gROOT.SetBatch(True) # to suppress canvas pop-outs
if bTag == 'True':
titleName = '1 Medium 1 Loose b-tag'
fileName = 'bTag'
elif bTag == 'False':
titleName = ''
fileName = 'all'
elif bTag == '2M':
titleName = '2 Medium b-tags'
fileName = '2MbTag'
elif bTag == '1M':
titleName = '1 Medium b-tag'
fileName = '1MbTag'
elif bTag == '1M1NonM':
titleName = '1 Medium 1 Anti-Medium b-tag'
fileName = '1M1NonMbTag'
elif bTag == 'None':
titleName = '0 b-tag'
fileName = '0bTag'
KS1 = QCDHistList_4KS[0].KolmogorovTest(QCDHistList_4KS[2])
KS2 = QCDHistList_4KS[1].KolmogorovTest(QCDHistList_4KS[2])
ks_values = []
tmpHists = []
ks_values2 = []
tmpHists2 = []
nTimes = 10000
# for i in range(nTimes):
# tool.printProcessStatus(i, nTimes, processName = 'Making Sample Histograms')
# tmpHists.append(r.TH1F('tmpHist_%i' %(i),"", nBins, varRange[1], varRange[2]))
# tmpHists[i].FillRandom(QCDHistList_4KS[2], 100)
# ks_values.append(QCDHistList_4KS[0].KolmogorovTest(tmpHists[i]))
# tmpHists2.append(r.TH1F('tmpHist2_%i' %(i),"", nBins, varRange[1], varRange[2]))
# tmpHists2[i].FillRandom(QCDHistList_4KS[2], 100)
# ks_values2.append(QCDHistList_4KS[2].KolmogorovTest(tmpHists[i]))
print ''
print 'KS Test 1: %.3f' %KS1
print 'KS Test 2: %.3f' %KS2
fakeHist = r.TH1F()
fakeHist.SetLineColor(0)
usePUWeightName = ''
if usePU:
usePUWeightName = '_usePU'
psfile = '%s_%s_%s_%s_%s%s.pdf' %(varName, fileName, signalSelection, region, relaxedRegionOption, usePUWeightName)
c = r.TCanvas("c","Test", 800, 900)
#ps = r.TPDF(psfile,112)
c.Divide(2,3)
drawOpt = ''
QCDDiff.SetTitle('Data/Background OS Relaxed Events %s (%.1f fb^{-1}); %s; Data/Background' %(titleName, Lumi,varName))
QCDDiff.SetMarkerStyle(8)
QCDDiff.SetMarkerSize(0.9)
QCDDiff.SetMaximum(4)
QCDDiff.SetMinimum(0)
QCDDiff_R2T.SetTitle('Data/Background OS Relaxed Events %s (%.1f fb^{-1}); %s; Data/Background' %(titleName, Lumi,varName))
QCDDiff_R2T.SetMarkerStyle(8)
QCDDiff_R2T.SetMarkerSize(0.9)
QCDDiff_R2T.SetMaximum(4)
QCDDiff_R2T.SetMinimum(0)
pl_1 = r.TPad("pl_1","pl_1",0.,1,0.5,0.65)
pl_1_delta = r.TPad("pl_1_delta","pl_1_delta",0.,0.65,0.5,0.45)
pl_2 = r.TPad("pl_2","pl_2",0.,0.45,0.5,0.0)
pl_1.SetMargin(1, 1, 0, 1)
pl_1_delta.SetMargin(1, 1, 0.2, 0.05)
pr_1 = r.TPad("pr_1","pr_1",0.5,1,1.,0.65)
pr_1_delta = r.TPad("pr_1_delta","pr_1_delta",0.5,0.65,1.0,0.45)
pr_2 = r.TPad("pr_2","pr_2",0.5,0.45,1.0,0.0)
pr_1.SetMargin(1, 1, 0, 1)
pr_1_delta.SetMargin(1, 1, 0.2, 0.05)
pl_1.Draw()
pl_1_delta.Draw()
pl_2.Draw()
pr_1.Draw()
pr_1_delta.Draw()
pr_2.Draw()
pl_1.cd()
r.gPad.SetTicky()
signSelection, iso = conditions(1, region)
allStacked.SetMaximum(int(yMax))
allStacked.SetTitle('CMS Preliminary %.1f fb^{-1} at 8 TeV; %s; events / bin' %(Lumi,varName))
allStacked.Draw()
var_data[0].Draw('PE same')
legendPosition = (0.43, 0.9 - 0.06*len(legendHistos[0]), 0.87, 0.9)
l.append(tool.setMyLegend(lPosition=legendPosition, lHistList=getNewLegend(legendHistos[0], fakeHist)))
l[0].Draw('same')
var_signal[0].Draw('same')
pl_1_delta.cd()
r.gPad.SetTicky()
r.gPad.SetTickx()
pl_1_delta.SetGridy(1)
bkgEst = QCDHistList_withScale[1].Clone()
delta = var_data[0].Clone()
delta.Sumw2()
bkgEst.Sumw2()
delta.Divide(bkgEst)
delta.SetMinimum(0.5)
delta.SetMaximum(1.5)
delta.GetXaxis().SetTitle(varName)
delta.GetXaxis().SetLabelSize(0.07)
delta.GetXaxis().SetTitleSize(0.07)
delta.GetYaxis().SetLabelSize(0.07)
delta.GetYaxis().SetNdivisions(5,5,0)
delta.Draw()
pList = [pr_1, pl_2, pr_2]
for k in range(1, len(pList)+1):
pList[k-1].cd()
r.gPad.SetTicky()
if k > 1 and logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k+1, region)
var_background[k].SetTitle('%s %s Events %s (%.1f fb^{-1}); %s; events / bin' %(signSelection, iso, titleName, Lumi,varName))
if k < 2:
var_background[k].SetMaximum(int(yMax))
else:
var_background[k].SetMaximum(max)
var_background[k].SetMinimum(0.01)
var_background[k].Draw()
if useData == 'True':
var_data[k].Draw('PE same')
legendPosition = (0.47, 0.9 - 0.06*len(legendHistos[k]), 0.87, 0.9)
l.append(tool.setMyLegend(lPosition=legendPosition, lHistList=getNewLegend(legendHistos[k], fakeHist)))
if k == 1:
ksLegend1 = tool.setMyLegend((0.2, 0.8, 0.5, 0.9), [(QCDHistList_withScale[3], 'KS Test: %.3f' %KS1)])
ksLegend1.Draw('same')
if k == 2:
ksLegend2 = tool.setMyLegend((0.2, 0.8, 0.5, 0.9), [(QCDHistList_withScale[2], 'KS Test: %.3f' %KS2)])
ksLegend2.Draw('same')
l[k].Draw('same')
var_signal[k].Draw('same')
pr_1_delta.cd()
r.gPad.SetTicky()
r.gPad.SetTickx()
pr_1_delta.SetGridy(1)
bkgEst2 = QCDHistList_withScale[3].Clone()
delta2 = var_data[1].Clone()
delta2.Sumw2()
bkgEst2.Sumw2()
delta2.Divide(bkgEst2)
delta2.SetMinimum(0.5)
delta2.SetMaximum(1.5)
delta2.GetXaxis().SetTitle(varName)
delta2.GetXaxis().SetLabelSize(0.07)
delta2.GetXaxis().SetTitleSize(0.07)
delta2.GetYaxis().SetLabelSize(0.07)
delta2.GetYaxis().SetNdivisions(5,5,0)
delta2.Draw()
c.Update()
c.Print('%s(' %psfile)
c.Clear()
p1 = r.TPad("p1","p1",0.,1,1.,0.4)
p1_r = r.TPad("p1_r","p1_r",0.,0.39,1.,0.06)
p1.SetMargin(1, 1, 0, 1)
p1_r.SetMargin(1, 1, 0.2, 1)
p1.Draw()
p1_r.Draw()
p1.cd()
r.gPad.SetTicky()
allStacked.Draw()
var_data[0].Draw('PE same')
l[0].Draw('same')
var_signal[0].Draw('same')
p1_r.cd()
r.gPad.SetTicky()
r.gPad.SetTickx()
p1_r.SetGridy(1)
delta.Draw()
c.Print('%s)' %psfile)
print "Plot saved at %s" %(psfile)
c.Close()
sampleName = options.inputFile[options.inputFile.find('TMVARegApp_'): options.inputFile.rfind('_new')]
c = r.TCanvas("c","Test", 800, 600)
c.Divide(2,2)
ps = r.TPDF(psfile,112)
c.cd(1)
met.SetTitle('%s met (%s); met (GeV); events / bin' %(sampleName,options.cut))
met.Draw()
met.SetMaximum(int(options.max))
met.SetFillColor(r.kAzure+8)
met.SetLineColor(r.kAzure+8)
met.SetFillStyle(3002)
metReg.Draw('same')
metReg.SetLineColor(r.kRed)
metReg.SetLineWidth(2)
l1 = tool.setMyLegend(lPosition=legendPosition3, lHistList=legendHistos1)
l1.Draw("same")
c.Update()
c.cd(2)
metInTauPair.SetTitle('%s met projected in tau pair direction (%s); met (GeV); events / bin' %(sampleName,options.cut))
metInTauPair.Draw()
metInTauPair.SetMaximum(int(options.max))
metInTauPair.SetFillColor(r.kAzure+8)
metInTauPair.SetLineColor(r.kAzure+8)
metInTauPair.SetFillStyle(3002)
metRegInTauPair.Draw('same')
metRegInTauPair.SetLineColor(r.kRed)
metRegInTauPair.SetLineWidth(2)
l2 = tool.setMyLegend(lPosition=legendPosition3, lHistList=legendHistos2)
l2.Draw("same")
c.Update()
def compareSamples(fs):
varNameDict_et = {
'm_eff': 'm(#tau_{e}, #tau_{h}, #slash{E}_{T}) [GeV]',
'ePt': 'Electron Pt [GeV]',
'tPt': 'Tau Pt [GeV]',
'nTruePU': 'nTruePU',
}
varNameDict_em = {
'm_eff': 'm(#tau_{e}, #tau_{#mu}, #slash{E}_{T}) [GeV]',
'ePt': 'Electron Pt [GeV]',
'mPt': 'Muon Pt [GeV]',
'nTruePU': 'nTruePU',
}
varName = 'ePt'
varBins = 0
if varName == 'm_eff':
varBins = array.array('d', [
0, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250,
275, 300, 400, 600, 900
])
hist1 = r.TH1F("hist_1", "", len(varBins) - 1, varBins)
hist2 = r.TH1F("hist_2", "", len(varBins) - 1, varBins)
else:
hist1 = r.TH1F("hist_1", "", 50, 0, 500)
hist2 = r.TH1F("hist_2", "", 50, 0, 500)
loopOneFile(
iSample=
'/user_data/zmao/Jan13Production_signalRegion/ZPrime_500_all_SYNC_%s_noIso.root'
% fs,
varName=varName,
hist=hist1,
fs=fs,
varBins=varBins)
loopOneFile(
iSample=
'/user_data/zmao/Jan13Production_signalRegion/ZPrime_500_2_all_SYNC_%s_noIso.root'
% fs,
varName=varName,
hist=hist2,
fs=fs,
varBins=varBins)
position = (0.65, 0.85 - 0.06 * 2, 0.85, 0.85)
histList = []
histList.append((hist1, 'Old Zprime_500', 'lp'))
histList.append((hist2, 'New Zprime_500', 'lp'))
legend = tool.setMyLegend(position, histList)
c = r.TCanvas("c", "Test", 800, 600)
hist1.SetMarkerSize(0.5)
hist1.SetLineColor(r.kBlue)
hist1.SetMarkerColor(r.kBlue)
hist2.SetMarkerSize(0.5)
hist2.SetLineColor(r.kRed)
hist2.SetMarkerColor(r.kRed)
if fs == 'et':
title = varNameDict_et[varName]
else:
title = varNameDict_em[varName]
hist1.SetTitle("ZPrime Sample Comparison; %s; Events/Initial Events" %
title)
hist1.SetMinimum(0.000001)
hist1.Scale(1, 'width')
hist2.Scale(1, 'width')
hist1.Draw("E1")
hist2.Draw("E1same")
legend.Draw('same')
r.gPad.SetLogy()
r.gPad.SetTicky()
r.gPad.SetTickx()
c.Print('sampleCheck_%s_%s.pdf' % (fs, varName))
print ''
psfile = 'LTauMCValidation_%s.pdf' %lepton
c = r.TCanvas("c","Test", 800, 600)
stack.SetTitle('MC Validation 19.7 fb^{-1}; tau phi; events / bin')
stack.SetMaximum(yMax)
stack.Draw()
stack.GetYaxis().SetTitleOffset(1.4)
data.SetMarkerStyle(8)
data.SetMarkerSize(0.9)
data.Draw('sameE')
l1 = tool.setMyLegend(lPosition=legendPosition, lHistList=legendHistos)
l1.Draw("same")
print 'data/MC %f'%(data.Integral()/totalBkg.Integral())
c.Update()
c.Print('%s(' %psfile)
c.Clear()
tauInfo = data.Clone()
tauInfo.Sumw2()
tauInfo.Divide(totalBkg)
tauInfo.SetMarkerStyle(8)
tauInfo.SetMarkerSize(0.9)
tauInfo.SetMaximum(1.5)
tauInfo.SetMinimum(0.5)
tauInfo.Draw('E')
fmErr.SetPoint(1, ptBins[2],ratio.GetBinContent(2)+ratio.GetBinError(2))
fmErr.SetPoint(2, ptBins[2],ratio.GetBinContent(2)-ratio.GetBinError(2))
fmErr.SetFillStyle(3001)
fmErr.SetFillColor(r.kBlue)
fhErr = r.TGraph(4)
fhErr.SetPoint(0, ptBins[2],ratio.GetBinContent(3)+ratio.GetBinError(3))
fhErr.SetPoint(3, ptBins[2],ratio.GetBinContent(3)-ratio.GetBinError(3))
fhErr.SetPoint(1, ptBins[3],ratio.GetBinContent(3)+ratio.GetBinError(3))
fhErr.SetPoint(2, ptBins[3],ratio.GetBinContent(3)-ratio.GetBinError(3))
fhErr.SetFillStyle(3001)
fhErr.SetFillColor(r.kGreen)
flErr.Draw('fsame')
fmErr.Draw('fsame')
fhErr.Draw('fsame')
lFit1 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(flErr,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(ratio.GetBinContent(1), ratio.GetBinError(1))),
(fmErr,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(ratio.GetBinContent(2), ratio.GetBinError(2))),
(fhErr,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(ratio.GetBinContent(3), ratio.GetBinError(3)))])
lFit1.Draw('same')
c.cd(4)
chargeRatio.SetTitle('+ / - Ratio; %s; +/-' %varName)
chargeRatio.SetMaximum(2.5)
chargeRatio.Draw('PE')
lFit2 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(chargeRatio,'Scale between +/- in relaxed region: %.2f \pm %.2f' %(chargeRatio.GetBinContent(1), chargeRatio.GetBinError(1))),
(chargeRatio,'Scale between +/- in relaxed region: %.2f \pm %.2f' %(chargeRatio.GetBinContent(2), chargeRatio.GetBinError(2))),
(chargeRatio,'Scale between +/- in relaxed region: %.2f \pm %.2f' %(chargeRatio.GetBinContent(3), chargeRatio.GetBinError(3)))])
lFit2.Draw('same')
# chargeRatio2.Draw('samePE')
c.Update()
c.Print('QCDratio_%s_%s.pdf' %(bTag,varName))
def compareSamples(fs):
varNameDict_et = {'m_eff': 'm(#tau_{e}, #tau_{h}, #slash{E}_{T}) [GeV]',
'ePt': 'Electron Pt [GeV]',
'tPt': 'Tau Pt [GeV]',
'nTruePU': 'nTruePU',
'genHT': 'gen HT [GeV]',
'X_to_ll': 'gen Mass [GeV]',
}
varNameDict_em = {'m_eff': 'm(#tau_{e}, #tau_{#mu}, #slash{E}_{T}) [GeV]',
'ePt': 'Electron Pt [GeV]',
'mPt': 'Muon Pt [GeV]',
'nTruePU': 'nTruePU',
}
varNameDict_mt = {'m_eff': 'm(#tau_{#mu}, #tau_{h}, #slash{E}_{T}) [GeV]',
'tPt': 'Tau Pt [GeV]',
'mPt': 'Muon Pt [GeV]',
'genHT': 'gen HT [GeV]',
'nTruePU': 'nTruePU',
'X_to_ll': 'gen Mass [GeV]',
}
varName = 'X_to_ll'
varBins = 0
if varName == 'm_eff':
varBins = array.array('d', [0,100,110,120,130,140,150,160,170,180,190,200,225,250,275,300,400,600,900,1200, 1700])
else:
varBins = array.array('d', range(0, 1000, 10))
hist1 = r.TH1F("hist_1", "", len(varBins)-1, varBins)
hist2 = r.TH1F("hist_2", "", len(varBins)-1, varBins)
hist3 = r.TH1F("hist_3", "", len(varBins)-1, varBins)
hist4 = r.TH1F("hist_4", "", len(varBins)-1, varBins)
hist5 = r.TH1F("hist_5", "", len(varBins)-1, varBins)
hist6 = r.TH1F("hist_6", "", len(varBins)-1, varBins)
dir = "/user_data/zmao/2016_signalRegionNoPZeta_fixMuon/"
# dir = "/user_data/zmao/inclusive/"
loopOneFile(iSample = '%s/DY-50_LO_all_SYNC_%s_noIso.root' %(dir, fs),
varName = varName,
hist = hist1,
fs = fs,
varBins = varBins)
loopOneFile(iSample = '%s/DY-50to150_LO_all_SYNC_%s_noIso.root' %(dir, fs),
varName = varName,
hist = hist2,
fs = fs,
varBins = varBins)
loopOneFile(iSample = '%s/DY-150_LO_all_SYNC_%s_noIso.root' %(dir, fs),
varName = varName,
hist = hist3,
fs = fs,
varBins = varBins)
# loopOneFile(iSample = '%s/DY-50_LO_HT-200to400_all_SYNC_%s_noIso.root' %(dir, fs),
# varName = varName,
# hist = hist4,
# fs = fs,
# varBins = varBins)
# loopOneFile(iSample = '%s/DY-50_LO_HT-400to600_all_SYNC_%s_noIso.root' %(dir, fs),
# varName = varName,
# hist = hist5,
# fs = fs,
# varBins = varBins)
# loopOneFile(iSample = '%s/DY-50_LO_HT-600toInf_all_SYNC_%s_noIso.root' %(dir, fs),
# varName = varName,
# hist = hist6,
# fs = fs,
# varBins = varBins)
position = (0.7, 0.9 - 0.06*5, 0.9, 0.88)
histList = []
histList.append((hist1, 'Inclusive', 'lp'))
histList.append((hist2, 'M-50to150', 'f'))
histList.append((hist3, 'M-150', 'f'))
# histList.append((hist4, 'HT-200to400', 'f'))
# histList.append((hist5, 'HT-400to600', 'f'))
# histList.append((hist6, 'HT-600toInf', 'f'))
legend = tool.setMyLegend(position, histList)
if fs == 'et':
title = varNameDict_et[varName]
elif fs == 'em':
title = varNameDict_em[varName]
elif fs == 'mt':
title = varNameDict_mt[varName]
colorList = [r.kBlack, 46, 41, 47, 30, 38]
c = r.TCanvas("c","Test", 800, 600)
hist1.SetMarkerSize(0.5)
hist1.SetMarkerStyle(8)
hist1.SetLineColor(colorList[0])
hist1.SetMarkerColor(colorList[0])
hist2.SetMarkerSize(0.5)
hist2.SetLineColor(r.kBlack)
hist2.SetFillColor(colorList[1])
hist2.SetMarkerColor(colorList[1])
hist3.SetMarkerSize(0.5)
hist3.SetLineColor(r.kBlack)
hist3.SetFillColor(colorList[2])
hist3.SetMarkerColor(colorList[2])
hist4.SetMarkerSize(0.5)
hist4.SetLineColor(r.kBlack)
hist4.SetFillColor(colorList[3])
hist4.SetMarkerColor(colorList[3])
hist5.SetMarkerSize(0.5)
hist5.SetLineColor(r.kBlack)
hist5.SetFillColor(colorList[4])
hist5.SetMarkerColor(colorList[4])
hist6.SetMarkerSize(0.5)
hist6.SetLineColor(r.kBlack)
hist6.SetFillColor(colorList[5])
hist6.SetMarkerColor(colorList[5])
if varName == 'm_eff':
hist1.Scale(1, 'width')
hist2.Scale(1, 'width')
hist3.Scale(1, 'width')
hist4.Scale(1, 'width')
hist5.Scale(1, 'width')
hist6.Scale(1, 'width')
hist1.SetTitle("DY Inclusive/Exclusive Comparison; %s; Events/GeV" %title)
else:
hist1.SetTitle("DY Inclusive/Exclusive Comparison; %s; Events" %title)
stack = r.THStack()
# stack.Add(hist6)
# stack.Add(hist5)
# stack.Add(hist4)
stack.Add(hist3)
stack.Add(hist2)
uncBand = buildUncBand(varBins, [hist2, hist3])
hist1.SetMinimum(0.0001)
hist1.Draw("E1")
stack.Draw('hist H same')
uncBand.Draw("E2 same")
hist1.Draw("E1 same")
# else:
# hist2.Draw("E1same")
# hist3.Draw("E1same")
# hist4.Draw("E1same")
# hist5.Draw("E1same")
# hist6.Draw("E1same")
legend.Draw('same')
r.gPad.RedrawAxis()
r.gPad.SetLogy()
r.gPad.SetTicky()
r.gPad.SetTickx()
c.Print('sampleCheck_%s_%s.pdf' %(fs, varName))
def run(varName, bins, unit, cat = 'em', eID = 'MVANonTrig80_2', fit = False, region = 'barrel', triggerLeg = 'e'):
dir = '2016_trigStudy'
dataFunc = setFunc('dataFunc')
append_name = ''
files_mc = [# ("SUSY", '/nfs_scratch/zmao/13TeV_samples_25ns_newSplitting_noChargeMatch/SYNC_SUSY_HToTauTau_M-160_%s_%s.root' %(cat, append_name), r.kRed)
]
hist_mc_all = {}
hist_mc_pass = {}
file_data = '/user_data/zmao/%s/%s/data_Electron_all_SYNC_em_noIso.root' %(dir, eID)#'/nfs_scratch/zmao/supy-output//slice/ee//data_Electron_events.root'# %(cat, append_name)
hist_data_all = r.TH1F('hist_data_all', '', len(bins)-1, bins)
hist_data_pass = r.TH1F('hist_data_pass', '', len(bins)-1, bins)
for iName, iSample, iColor in files_mc:
if iName not in hist_mc_all.keys():
hist_mc_all[iName] = r.TH1F("hist_%s_all" %iName, "", len(bins)-1, bins)
hist_mc_pass[iName] = r.TH1F("hist_%s_pass" %iName, "", len(bins)-1, bins)
loop_one_sample(iSample, varName, hist_mc_all[iName], hist_mc_pass[iName], cat, False, region, triggerLeg)
if file_data != '':
loop_one_sample(file_data, varName, hist_data_all, hist_data_pass, cat, True, region, triggerLeg)
g_mc = []
histList = []
funcs = []
i = 0
for iKey in hist_mc_all.keys():
g_mc.append(r.TGraphAsymmErrors())
g_mc[i].BayesDivide(hist_mc_pass[iKey], hist_mc_all[iKey])
g_mc[i].SetMarkerStyle(8)
g_mc[i].SetMarkerSize(0.9)
color = getColor(iKey)
g_mc[i].SetMarkerColor(color)
g_mc[i].SetLineColor(color)
if iKey == 'ttbar' and fit:
funcs.append(setFunc('%sFunc' %iKey))
funcs[i].SetLineColor(color)
funcs[i].SetLineStyle(2)
g_mc[i].Fit('%sFunc' %iKey, "R")
histList.append((g_mc[i], "%s, plateau: %.3f +- %.3f" %(iKey, funcs[i].GetParameter(0), funcs[i].GetParError(0)), 'ep'))
else:
funcs.append(None)
if len(bins) == 3:
x = r.Double(0.0)
y1 = r.Double(0.0)
y2 = r.Double(0.0)
g_mc[i].GetPoint(0, x, y1)
g_mc[i].GetPoint(1, x, y2)
histList.append((g_mc[i], "%s (%.3f +/- %.3f, %.3f +/- %.3f)" %(iKey, y1, g_mc[i].GetErrorY(0), y2, g_mc[i].GetErrorY(1)), 'ep'))
else:
histList.append((g_mc[i], iKey, 'ep'))
i += 1
g_data = r.TGraphAsymmErrors()
g_data.BayesDivide(hist_data_pass, hist_data_all)
dataFunc.SetLineColor(r.kBlack)
dataFunc.SetLineStyle(2)
fakeHist = r.TH1F("fakeHist", "", 1, 0,1)
fakeHist.SetLineColor(r.kWhite)
if fit:
g_data.Fit("dataFunc", "R");
histList.append((g_data, 'Observed, plateau: %.3f +/- %.3f' %(dataFunc.GetParameter(0), dataFunc.GetParError(0)), "ep"))
histList.append((fakeHist, ' halfpoint: %.1f +/- %.1f' %(dataFunc.GetParameter(1), dataFunc.GetParError(1)), "l"))
histList.append((fakeHist, ' slope: %.3f +/- %.3f' %(dataFunc.GetParameter(2), dataFunc.GetParError(2)), "l"))
else:
if len(bins) == 3:
x = r.Double(0.0)
data_y1 = r.Double(0.0)
data_y2 = r.Double(0.0)
g_data.GetPoint(0, x, data_y1)
g_data.GetPoint(1, x, data_y2)
histList.append((g_data, "Observed (%.3f +/- %.3f, %.3f +/- %.3f)" %(data_y1, g_data.GetErrorY(0), data_y2, g_data.GetErrorY(1)), 'ep'))
else:
histList.append((g_data, 'Observed', 'ep'))
fakeHist = r.TH1D("fakeHist", "", 1, 0, 1)
fakeHist.SetLineColor(r.kWhite)
g_data.SetMarkerStyle(8)
g_data.SetMarkerSize(0.9)
g_data.SetMarkerColor(r.kBlack)
length = 800
# length = 1800
c = r.TCanvas("c","Test", length, 800)
null = r.TH2F("null","", len(bins)-1, bins, 1, 0, 1.1)
null.SetMaximum(1.2)
if triggerLeg == 'e':
psfile = 'eleTrigTurnOnCurve_%s_%s_%s_%s.pdf' %(varName, cat, eID, region)
null.SetTitle("electron trigger turn-on curve; %s; Effeciency" %unit)
else:
psfile = 'muonTrigTurnOnCurve_%s_%s_%s_%s.pdf' %(varName, cat, eID, region)
null.SetTitle("muon trigger turn-on curve; %s; Effeciency" %unit)
null.SetStats(False)
if len(bins) == 3:
leg = tool.setMyLegend((0.1, 0.4 - 0.06*4, 0.95, 0.4), histList)
else:
leg = tool.setMyLegend((0.45, 0.4 - 0.06*4, 0.9, 0.4), histList)
r.gPad.SetTicky()
r.gPad.SetTickx()
r.gPad.SetTicky()
null.Draw()
for i in range(len(hist_mc_all.keys())):
g_mc[i].Draw('same PE')
g_data.Draw('same PE')
leg.Draw('same')
c.Print('%s' %psfile)
c.Close()
tool.unitNormHists(HistNameList)
legendPosition = (0.58, 0.7, 0.88, 0.80)
legendHist1 = [(jetCSV1_signal,"H -> hh -> #tau^{+}#tau^{-} b#bar{b}"), (jetCSV1_ZZ,"ZZ + jets -> 2q 2l"), (jetCSV1_tt,"t#bar{t} -> b#bar{b} ll")]
legendHist2 = [(jetCSV2_signal,"H -> hh -> #tau^{+}#tau^{-} b#bar{b}"), (jetCSV2_ZZ,"ZZ + jets -> 2q 2l"), (jetCSV2_tt,"t#bar{t} -> b#bar{b} ll")]
jetCSV1_signal.SetTitle("%s; CSV1; Unit Normalized" % (Title))
jetCSV2_ZZ.SetTitle("%s; CSV2; Unit Normalized" % (Title))
jetCSV1_signal.GetYaxis().SetTitleOffset(1.3)
jetCSV2_ZZ.GetYaxis().SetTitleOffset(1.3)
c = r.TCanvas("c","Test", 1400, 600)
r.gPad.SetTickx()
r.gPad.SetTicky()
psfile = os.environ['PYPATH']+'/Plots/'+psfile
ps = r.TPostScript(psfile,112)
c.Divide(2,1)
c.cd(1)
tool.setDrawHists(sigHist=jetCSV1_signal, ttHist=jetCSV1_tt, ZZHist=jetCSV1_ZZ)
l1 = tool.setMyLegend(lPosition=legendPosition, lHistList=legendHist1)
l1.Draw("same")
c.cd(2)
tool.setDrawHists(sigHist=jetCSV2_signal, ttHist=jetCSV2_tt, ZZHist=jetCSV2_ZZ)
l2 = tool.setMyLegend(lPosition=legendPosition, lHistList=legendHist2)
l2.Draw("same")
ps.Close()
print "Plot saved at %s" %(psfile)
def getHistos(signalSelection, sigBoost, location, bTag, tail):
r.gStyle.SetOptStat(0)
preFix = 'ClassApp_both_ClassApp_QCD_ClassApp_EWK_TMVARegApp_'
fileList = [('ZZ', preFix + 'ZZ_eff_all.root', 2500, 5, r.kYellow),
('tt_full_lep',preFix+'tt_eff_all.root', 26197.5, r.kRed-7),
('tt_semi_lep',preFix+'tt_semi_eff_all.root', 109281, r.kAzure+7),
# ('DYJetsToLL', 'DYJetsToLL_eff_all.root', 3504000, r.kGreen-7),
('DY2JetsToLL', preFix+'DY2JetsToLL_eff_all.root', 181000, r.kGreen-7),
('DY3JetsToLL', preFix+'DY3JetsToLL_eff_all.root', 51100, r.kGreen-7),
('W1JetsToLNu', preFix+'W1JetsToLNu_eff_all.root', 5400000, r.kMagenta-9),
('W2JetsToLNu', preFix+'W2JetsToLNu_eff_all.root', 1750000, r.kMagenta-9),
('W3JetsToLNu', preFix+'W3JetsToLNu_eff_all.root', 519000, r.kMagenta-9)]
histList = []
QCDHistList = []
varRange = [100, -1, 1]
varRange2 = [20, -1, 1]
varRange3 = [100, 0, 1]
varRange4 = [20, -1, 1]
Lumi = 19.0
initNEventsList = []
legendHistos = []
rootFileName = '%s/BDTEfficiency_%s.root' %(location, tail)
oFile = r.TFile(rootFileName,"RECREATE")
tmpFile = []
tmpTree = []
var_data = []
signal_EWK = r.TH1F('signal_EWK',"", varRange[0], varRange[1], varRange[2])
signal_QCD = r.TH1F('signal_QCD',"", varRange[0], varRange[1], varRange[2])
signal_both = r.TH1F('signal_both',"", varRange[0], varRange[1], varRange[2])
signal_2D = r.TH2F('signal_2D',"", varRange[0], varRange[1], varRange[2], varRange[0], varRange[1], varRange[2])
lkr_2D = r.TH2F('lkr_2D',"", varRange2[0], varRange2[1], varRange2[2], varRange2[0], varRange2[1], varRange2[2])
eff_QCD = r.TH1F('eff_QCD', '', varRange3[0], varRange3[1], varRange3[2])
eff_EWK = r.TH1F('eff_EWK', '', varRange3[0], varRange3[1], varRange3[2])
eff_both = r.TH1F('eff_both', '', varRange3[0], varRange3[1], varRange3[2])
eff_2D = r.TH1F('eff_2D', '', varRange3[0], varRange3[1], varRange3[2])
background_EWK = r.TH1F('background_EWK',"", varRange[0], varRange[1], varRange[2])
background_QCD = r.TH1F('background_QCD',"", varRange[0], varRange[1], varRange[2])
background_both = r.TH1F('background_both',"", varRange[0], varRange[1], varRange[2])
background_2D = r.TH2F('background_2D',"", varRange[0], varRange[1], varRange[2], varRange[0], varRange[1], varRange[2])
total_2D = r.TH2F('total_2D',"", varRange4[0], varRange4[1], varRange4[2], varRange4[0], varRange4[1], varRange4[2])
MC_EWK = r.TH1F('MC_EWK',"", varRange[0], varRange[1], varRange[2])
MC_QCD = r.TH1F('MC_QCD',"", varRange[0], varRange[1], varRange[2])
MC_both = r.TH1F('MC_both',"", varRange[0], varRange[1], varRange[2])
MC_2D = r.TH2F('MC_2D',"", varRange[0], varRange[1], varRange[2], varRange[0], varRange[1], varRange[2])
data_EWK = r.TH1F('data_EWK',"", varRange[0], varRange[1], varRange[2])
data_QCD = r.TH1F('data_QCD',"", varRange[0], varRange[1], varRange[2])
data_both = r.TH1F('data_both',"", varRange[0], varRange[1], varRange[2])
data_2D = r.TH2F('data_2D',"", varRange[0], varRange[1], varRange[2], varRange[0], varRange[1], varRange[2])
dataName = preFix + 'dataTotal_all.root'
fData = r.TFile(dataName)
treeData = fData.Get('eventTree')
print 'Adding events from: %s ...' %dataName
for iEntry in range(treeData.GetEntries()):
treeData.GetEntry(iEntry)
select = passCut(treeData, bTag)
if select == 3:
data_EWK.Fill(varsList.findVar(treeData, 'BDT_EWK'))
data_QCD.Fill(varsList.findVar(treeData, 'BDT_QCD'))
data_both.Fill(varsList.findVar(treeData, 'BDT_both'))
data_2D.Fill(varsList.findVar(treeData, 'BDT_EWK'), varsList.findVar(treeData, 'BDT_QCD'))
for i in range(len(fileList)):
print 'Adding events from: %s ...' %(fileList[i][1])
tmpFile.append(r.TFile(fileList[i][1]))
tmpTree.append(tmpFile[i].Get('eventTree'))
initNEventsList.append(tmpFile[i].Get('preselection'))
tmpScale = fileList[i][2]*Lumi/initNEventsList[i].GetBinContent(1)
for iEntry in range(tmpTree[i].GetEntries()):
tmpTree[i].GetEntry(iEntry)
select = passCut(tmpTree[i], bTag)
if select == 1:
background_EWK.Fill(varsList.findVar(tmpTree[i], 'BDT_EWK'), tmpTree[i].triggerEff*tmpScale)
background_QCD.Fill(varsList.findVar(tmpTree[i], 'BDT_QCD'), tmpTree[i].triggerEff*tmpScale)
background_both.Fill(varsList.findVar(tmpTree[i], 'BDT_both'), tmpTree[i].triggerEff*tmpScale)
background_2D.Fill(varsList.findVar(tmpTree[i], 'BDT_EWK'), varsList.findVar(tmpTree[i], 'BDT_QCD'), tmpTree[i].triggerEff*tmpScale)
if select == 3:
MC_EWK.Fill(varsList.findVar(tmpTree[i], 'BDT_EWK'), tmpTree[i].triggerEff*tmpScale)
MC_QCD.Fill(varsList.findVar(tmpTree[i], 'BDT_QCD'), tmpTree[i].triggerEff*tmpScale)
MC_both.Fill(varsList.findVar(tmpTree[i], 'BDT_both'), tmpTree[i].triggerEff*tmpScale)
MC_2D.Fill(varsList.findVar(tmpTree[i], 'BDT_EWK'), varsList.findVar(tmpTree[i], 'BDT_QCD'), tmpTree[i].triggerEff*tmpScale)
if signalSelection != '':
var_signal = []
for i in range(4):
var_signal.append(r.TH1F('%s_%i' %(signalSelection,i),"", varRange[0], varRange[1], varRange[2]))
signalDict = {'H260': (preFix+'H2hh260_all.root', 14.76),
'H300': (preFix+'H2hh300_all.root', 15.9),
'H350': (preFix+'H2hh350_all.root', 8.57)}
if signalSelection in signalDict:
fSignal = r.TFile(signalDict[signalSelection][0])
treeSignal = fSignal.Get('eventTree')
print 'Adding events from: %s ...' %(signalDict[signalSelection][0])
initNEventsSignal = fSignal.Get('preselection')
scaleSignal = signalDict[signalSelection][1]*sigBoost*Lumi/initNEventsSignal.GetBinContent(1)
for iEntry in range(treeSignal.GetEntries()):
treeSignal.GetEntry(iEntry)
select = passCut(treeSignal, bTag)
if select == 1:
signal_EWK.Fill(varsList.findVar(treeSignal, 'BDT_EWK'), treeSignal.triggerEff)
signal_QCD.Fill(varsList.findVar(treeSignal, 'BDT_QCD'), treeSignal.triggerEff)
signal_both.Fill(varsList.findVar(treeSignal, 'BDT_both'), treeSignal.triggerEff)
signal_2D.Fill(varsList.findVar(treeSignal, 'BDT_EWK'), varsList.findVar(treeSignal, 'BDT_QCD'), treeSignal.triggerEff)
signal_EWK.Scale(scaleSignal)
signal_QCD.Scale(scaleSignal)
signal_both.Scale(scaleSignal)
signal_2D.Scale(scaleSignal)
else:
print '%s not supported, please use H260, H300 or H350' %signalSelection
background_EWK = estBkg(MC_EWK, data_EWK, background_EWK, 0.025)
background_QCD = estBkg(MC_QCD, data_QCD, background_QCD, 0.025)
background_both = estBkg(MC_both, data_both, background_both, 0.025)
background_2D = estBkg2(MC_2D, data_2D, background_2D, 0.025)
total_2D = combineSigBkg2(signal_2D, background_2D, total_2D)
eff_EWK = calcBDTEff(signal_EWK, background_EWK, eff_EWK)
eff_QCD = calcBDTEff(signal_QCD, background_QCD, eff_QCD)
eff_both = calcBDTEff(signal_both, background_both, eff_both)
lkr_2D = calcBDTLikelyhoodRatio2D(signal_2D, background_2D, lkr_2D)
eff_2D = calcEffFromLKR(signal_2D, background_2D, lkr_2D, eff_2D)
r.gROOT.SetBatch(True) # to suppress canvas pop-outs
if bTag == 'True':
titleName = '1 Medium 1 Loose b-tag'
fileName = 'bTag'
elif bTag == 'False':
titleName = ''
fileName = 'all'
elif bTag == 'Revert':
titleName = 'Revert b-tag'
fileName = 'revert_bTag'
psfile = '%s/BDT_Eff_%s.pdf' %(location, fileName)
c = r.TCanvas("c","Test", 800, 600)
r.gPad.SetTickx()
r.gPad.SetTicky()
eff_EWK.SetTitle('Background Rejection VS Signal Efficiency ; Background Rejection; Signal Efficiency')
eff_both.SetTitle('Background Rejection VS Signal Efficiency ; Background Rejection; Signal Efficiency')
eff_EWK.SetMarkerColor(r.kBlue)
eff_QCD.SetMarkerColor(r.kRed)
eff_both.SetMarkerColor(r.kBlack)
eff_2D.SetMarkerColor(8)
eff_EWK.SetMarkerSize(1.5)
eff_QCD.SetMarkerSize(1.5)
eff_both.SetMarkerSize(1.5)
eff_2D.SetMarkerSize(1.5)
eff_EWK.SetMarkerStyle(2)
eff_QCD.SetMarkerStyle(2)
eff_both.SetMarkerStyle(2)
eff_2D.SetMarkerStyle(2)
eff_EWK.Draw('P')
eff_QCD.Draw('SAMEP')
eff_both.Draw('sameP')
eff_2D.Draw('sameP')
legendPosition = (0.2, 0.6, 0.45, 0.75)
l = tool.setMyLegend(lPosition=legendPosition, lHistList=[(eff_EWK, "EWK"), (eff_QCD, "QCD"), (eff_both, "Combined"), (eff_2D, "EWK+QCD 2D cut")])
l.Draw('same')
c.Update()
c.Print('%s(' %psfile)
c.Clear()
eff_both.Draw('P')
eff_2D.Draw('Psame')
legendPosition = (0.2, 0.6, 0.45, 0.75)
l_2 = tool.setMyLegend(lPosition=legendPosition, lHistList=[(eff_both, "Combined"), (eff_2D, "EWK+QCD 2D cut")])
l_2.Draw('same')
c.Update()
c.Print('%s' %psfile)
c.Clear()
c.Divide(2,2)
c.cd(1)
r.gPad.SetLogz()
signal_2D.SetTitle('OS tight Events 1 Medium 1 Loose b-Tag (%.1f fb^{-1}); BDT_EWK;BDT_QCD; events / bin' %(Lumi))
signal_2D.Draw("CONTZ")
legendPosition = (0.65, 0.75, 0.9, 0.9)
l1 = tool.setMyLegend(lPosition=legendPosition, lHistList=[(signal_2D, "signal x%.0f (%.2f)" %(sigBoost,signal_2D.Integral()))])
l1.Draw('same')
c.cd(2)
r.gPad.SetLogz()
background_2D.SetTitle('OS tight Events 1 Medium 1 Loose b-Tag (%.1f fb^{-1}); BDT_EWK;BDT_QCD; events / bin' %(Lumi))
background_2D.Draw("CONTZ")
l2 = tool.setMyLegend(lPosition=legendPosition, lHistList=[(background_2D, "background (%.2f)" %background_2D.Integral())])
l2.Draw('same')
c.cd(3)
total_2D.SetTitle('OS tight Events 1 Medium 1 Loose b-Tag (%.1f fb^{-1}); BDT_EWK;BDT_QCD; events / bin' %(Lumi))
total_2D.Draw("CONTZ")
l3 = tool.setMyLegend(lPosition=legendPosition, lHistList=[(total_2D, "total (%.2f)" %total_2D.Integral())])
l3.Draw('same')
c.cd(4)
r.gPad.SetLogz()
lkr_2D.SetTitle('Signal/Background ratio; BDT_EWK;BDT_QCD; events / bin')
lkr_2D.Draw('CONTZ')
c.Update()
c.Print('%s)' %psfile)
print "Plot saved at %s" %(psfile)
oFile.cd()
eff_EWK.Write()
eff_QCD.Write()
eff_both.Write()
eff_2D.Write()
lkr_2D.Write()
oFile.Close()
print "ROOT file saved at %s" %(rootFileName)
def getHistos(varName, signalSelection, logY, sigBoost, nbins, useData, max, rangeMin, rangeMax, location, bTag, predict, predictPtBin, scale, region):
r.gStyle.SetOptStat(0)
preFix = 'ClassApp_both_ClassApp_QCD_ClassApp_EWK_TMVARegApp_'
fileList = [('ZZ', preFix + 'ZZ_eff_all.root', 2500, 5),
('WZJetsTo2L2Q', preFix + 'WZJetsTo2L2Q_eff_all.root', 2207, 5),
('tt_full_lep',preFix + 'tt_eff_all.root', 26197.5, r.kRed-7),
('tt_semi_lep',preFix + 'tt_semi_eff_all.root', 109281, r.kAzure+7),
#('DYJetsToLL', 'TMVARegApp_DYJetsToLL_eff_all.root', 3504000, r.kGreen-7),
('DY1JetsToLL', preFix + 'DY1JetsToLL_eff2_all.root', 561000, r.kGreen-7),
('DY2JetsToLL', preFix + 'DY2JetsToLL_eff2_all.root', 181000, r.kGreen-7),
('DY3JetsToLL', preFix + 'DY3JetsToLL_eff2_all.root', 51100, r.kGreen-7),
('W1JetsToLNu', preFix + 'W1JetsToLNu_eff2_all.root', 5400000, r.kMagenta-9),
('W2JetsToLNu', preFix + 'W2JetsToLNu_eff2_all.root', 1750000, r.kMagenta-9),
('W3JetsToLNu', preFix + 'W3JetsToLNu_eff2_all.root', 519000, r.kMagenta-9)]
histList = []
QCDHistList = []
varRange = [nbins, rangeMin, rangeMax]
Lumi = 19.0
initNEventsList = []
legendHistos = []
var_background = []
if scale == 1:
ptBins = [varRange[1], 70, 120, varRange[2]]
else:
ptBins = [varRange[1], varRange[2], varRange[2], varRange[2]]
bins = array('f', ptBins)
scaleMCPt = 1
tmpFile = []
tmpTree = []
var_data = []
for i in range(3):
var_data.append(r.TH1F('data_%i' %(i),"", len(ptBins)-1, bins))
dataName = preFix + 'dataTotal_all.root'
fData = r.TFile(dataName)
treeData = fData.Get('eventTree')
print 'Adding events from: %s ...' %dataName
for iEntry in range(treeData.GetEntries()):
treeData.GetEntry(iEntry)
select = passCut(treeData, bTag, region)
if (select == 0) or (select == 1) or (select > 4):
continue
if region == 'LT':
var_data[select-2].Fill(treeData.pt1.at(0))
else:
var_data[select-2].Fill(treeData.pt2.at(0))
legendHistos.append([])
for j in range(3):
var_data[j].SetMarkerStyle(8)
var_data[j].SetMarkerSize(0.9)
legendHistos.append([])
legendHistos[j+1].append((var_data[j], 'observed (%.0f)' %var_data[j].Integral()))
for i in range(len(fileList)):
for j in range(4):
histList.append(r.TH1F('%s_%i' %(fileList[i][0],j),fileList[i][0], len(ptBins)-1, bins))
print 'Adding events from: %s ...' %(fileList[i][1])
tmpFile.append(r.TFile(fileList[i][1]))
tmpTree.append(tmpFile[i].Get('eventTree'))
for iEntry in range(tmpTree[i].GetEntries()):
tmpTree[i].GetEntry(iEntry)
select = passCut(tmpTree[i], bTag, region)
if (not select) or (select > 4):
continue
if region == 'LT':
histList[4*i+select-1].Fill(tmpTree[i].pt1.at(0)*scaleMCPt, tmpTree[i].triggerEff)
else:
histList[4*i+select-1].Fill(tmpTree[i].pt2.at(0)*scaleMCPt, tmpTree[i].triggerEff)
initNEventsList.append(tmpFile[i].Get('preselection'))
for j in range(4):
var_background.append(r.THStack())
histList[4*i+j].SetFillColor(fileList[i][3])
histList[4*i+j].Scale(fileList[i][2]*Lumi/initNEventsList[i].GetBinContent(1))
var_background[j].Add(histList[4*i+j])
legendHistos[j].append((histList[4*i+j], '%s (%.2f)' %(fileList[i][0], histList[4*i+j].Integral())))
data_i = []
MC_i = []
data_r = []
MC_r = []
e = []
for i in range(3):
QCDHistList.append(r.TH1F('QCD_%i' %(i),"", len(ptBins)-1, bins))
for j in range(varRange[0]):
dataValue = var_data[i].GetBinContent(j+1)
dataError = var_data[i].GetBinError(j+1)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList[4*k+1+i].GetBinContent(j+1)
if i == 0:
data_i.append(dataValue)
e.append(dataError)
MC_i.append(MCValue)
if i == 2:
data_r.append(dataValue)
MC_r.append(MCValue)
if dataValue - MCValue > 0:
QCDHistList[i].SetBinContent(j+1, dataValue - MCValue)
QCDHistList[i].SetBinError(j+1, dataError)
QCDDiff = r.TH1F('QCD_diff',"", len(ptBins)-1, bins)
QCDDiff2 = r.TH1F('QCD_diff2',"", len(ptBins)-1, bins)
QCDDiff = QCDHistList[1].Clone()
QCDDiff.Divide(QCDHistList[2])
QCDDiff.Sumw2()
QCDDiff2 = QCDHistList[0].Clone()
QCDDiff2.Divide(QCDHistList[2])
QCDDiff2.Sumw2()
DrawSignal = False
if signalSelection != '':
var_signal = []
for i in range(4):
var_signal.append(r.TH1F('%s_%i' %(signalSelection,i),"", len(ptBins)-1, bins))
signalDict = {'H260': (preFix + 'H2hh260_all.root', 14.76),
'H300': (preFix + 'H2hh300_all.root', 15.9),
'H350': (preFix + 'H2hh350_all.root', 8.57)}
if signalSelection in signalDict:
fSignal = r.TFile(signalDict[signalSelection][0])
treeSignal = fSignal.Get('eventTree')
print 'Adding events from: %s ...' %(signalDict[signalSelection][0])
for iEntry in range(treeSignal.GetEntries()):
treeSignal.GetEntry(iEntry)
select = passCut(treeSignal, bTag, region)
if (not select) or (select > 4):
continue
if region == 'LT':
var_signal[select-1].Fill(treeSignal.pt1.at(0), treeSignal.triggerEff)
else:
var_signal[select-1].Fill(treeSignal.pt2.at(0), treeSignal.triggerEff)
initNEventsSignal = fSignal.Get('preselection')
for i in range(4):
var_signal[i].SetLineStyle(7)
var_signal[i].SetLineWidth(4)
var_signal[i].Scale(signalDict[signalSelection][1]*sigBoost*Lumi/initNEventsSignal.GetBinContent(1))
if sigBoost != 1:
legendHistos[i].append((var_signal[i], '%sx%0.f (%.2f)' %(signalSelection, sigBoost, var_signal[i].Integral())))
else:
legendHistos[i].append((var_signal[i], '%s (%.2f)' %(signalSelection, var_signal[i].Integral())))
DrawSignal = True
else:
print '%s not supported, please use H260, H300 or H350' %signalSelection
if predict == 'True':
relaxed2Tight = r.TH1F('relaxed2Tight','', len(ptBins)-1, bins)
SS2OS = r.TH1F('SS2OS','', len(ptBins)-1, bins)
relaxed2Signal = r.TH1F('relaxed2Signal','', len(ptBins)-1, bins)
SS2OS_signal = r.TH1F('SS2OS_signal','', len(ptBins)-1, bins)
fullHistTranslate = r.TH1F('fullHistTranslate','', len(ptBins)-1, bins)
scale_SS2OS_l = QCDDiff.GetBinContent(1)
scale_er_SS2OS_l = QCDDiff.GetBinError(1)
scale_relaxed2Tight_l = QCDDiff2.GetBinContent(1)
scale_er_relaxed2Tight_l = QCDDiff2.GetBinError(1)
scale_SS2OS_m = QCDDiff.GetBinContent(2)
scale_er_SS2OS_m = QCDDiff.GetBinError(2)
scale_relaxed2Tight_m = QCDDiff2.GetBinContent(2)
scale_er_relaxed2Tight_m = QCDDiff2.GetBinError(2)
scale_SS2OS_h = QCDDiff.GetBinContent(3)
scale_er_SS2OS_h = QCDDiff.GetBinError(3)
scale_relaxed2Tight_h = QCDDiff2.GetBinContent(3)
scale_er_relaxed2Tight_h = QCDDiff2.GetBinError(3)
for i in range(varRange[0]):
SS_tight_Events = QCDHistList[0].GetBinContent(i+1)
SS_tight_Error = QCDHistList[0].GetBinError(i+1)
OS_relaxed_Events = QCDHistList[1].GetBinContent(i+1)
OS_relaxed_Error = QCDHistList[1].GetBinError(i+1)
SS_Events = QCDHistList[2].GetBinContent(i+1)
SS_Error = QCDHistList[2].GetBinError(i+1)
if predictPtBin == 'True':
ptValue1 = QCDHistList[0].GetBinCenter(i+1)
ptValue2 = QCDHistList[1].GetBinCenter(i+1)
if ptValue1 <= ptBins[1]:
scale_1 = scale_relaxed2Tight_l
elif ptValue1 <= ptBins[2]:
scale_1 = scale_relaxed2Tight_m
elif ptValue1 > ptBins[2]:
scale_1 = scale_relaxed2Tight_h
if ptValue2 <= ptBins[1]:
scale_2 = scale_SS2OS_l
elif ptValue2 <= ptBins[2]:
scale_2 = scale_SS2OS_m
elif ptValue2 > ptBins[2]:
scale_2 = scale_SS2OS_h
relaxed2Tight.SetBinContent(i+1, scale_1*SS_Events)
relaxed2Signal.SetBinContent(i+1, scale_1*OS_relaxed_Events)
SS2OS.SetBinContent(i+1, scale_2*SS_Events)
SS2OS_signal.SetBinContent(i+1, scale_2*SS_tight_Events)
if SS_Events > 0:
fullHistTranslate.SetBinContent(i+1, SS_tight_Events*OS_relaxed_Events/SS_Events)
# relaxed2Tight.SetBinError(i+1, getCombinedError(scale_relaxed2Tight, scale_er_relaxed2Tight, SS_Events, SS_Error))
# SS2OS.SetBinError(i+1, getCombinedError(scale_SS2OS, scale_er_SS2OS, SS_Events, SS_Error))
# relaxed2Tight.SetLineColor(r.kSpring+8)
relaxed2Tight.SetFillColor(r.kSpring+1)
# SS2OS.SetLineColor(r.kOrange)
SS2OS.SetFillColor(r.kOrange-4)
SS2OS_signal.SetLineColor(r.kSpring+1)
SS2OS_signal.SetLineWidth(2)
relaxed2Signal.SetLineStyle(2)
relaxed2Signal.SetLineColor(r.kOrange-4)
relaxed2Signal.SetLineWidth(2)
fullHistTranslate.SetLineColor(r.kPink+1)
fullHistTranslate.SetLineWidth(2)
fullHistTranslate.SetLineStyle(2)
legendHistos[0].append((SS2OS_signal, 'From SS/Tight (%.0f)' %SS2OS_signal.Integral()))
legendHistos[0].append((relaxed2Signal, 'From OS/Relax (%.0f)' %relaxed2Signal.Integral()))
#legendHistos[0].append((fullHistTranslate, 'full hist (%.0f)' %fullHistTranslate.Integral()))
var_background[1].Add(relaxed2Tight)
var_background[2].Add(SS2OS)
legendHistos[1].append((relaxed2Tight, 'From SS/Relax (%.0f)' %relaxed2Tight.Integral()))
legendHistos[2].append((SS2OS, 'From SS/Relax (%.0f)' %SS2OS.Integral()))
relaxed2Signal = tool.addFakeTHStack(relaxed2Signal,var_background[0])
SS2OS_signal = tool.addFakeTHStack(SS2OS_signal,var_background[0])
fullHistTranslate = tool.addFakeTHStack(fullHistTranslate,var_background[0])
legendPosition = (0.6, 0.7, 0.90, 0.88)
l = []
r.gROOT.SetBatch(True) # to suppress canvas pop-outs
if bTag == 'True':
titleName = '1 Medium 1 Loose b-tag'
fileName = 'bTag'
elif bTag == 'False':
titleName = ''
fileName = 'all'
elif bTag == 'Revert':
titleName = 'Revert b-tag'
fileName = 'revert_bTag'
elif bTag == 'Loose':
titleName = 'Loose b-tag'
fileName = 'loose_bTag'
psfile = '%s/%s_%s_%s_%i.pdf' %(location, varName, fileName, region, scale)
c = r.TCanvas("c","Test", 800, 600)
#ps = r.TPDF(psfile,112)
c.Divide(2,2)
drawOpt = ''
for k in range(4):
c.cd(k+1)
if logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k+1)
var_background[k].SetTitle('%s %s Events %s (%.1f fb^{-1}); %s; events / bin' %(signSelection, iso, titleName, Lumi,varName))
var_background[k].SetMaximum(max)
var_background[k].SetMinimum(0.01)
var_background[k].Draw()
if predict == 'True' and k == 0:
SS2OS_signal.Draw('same')
relaxed2Signal.Draw('same')
#fullHistTranslate.Draw('same')
if k != 0 and useData == 'True':
var_data[k-1].Draw('PE same')
legendPosition = (0.63, 0.93 - 0.035*len(legendHistos[k]), 0.93, 0.9)
l.append(tool.setMyLegend(lPosition=legendPosition, lHistList=legendHistos[k]))
l[k].Draw('same')
var_signal[k].Draw('same')
c.Update()
c.Print('%s(' %psfile)
c.cd(1)
r.gPad.SetLogy(0)
QCDDiff.SetTitle('OS/SS MultiJet Relaxed Events %s (%.1f fb^{-1}); %s; OS/SS' %(titleName, Lumi,varName))
QCDDiff.SetMarkerStyle(8)
QCDDiff.SetMarkerSize(0.9)
QCDDiff.SetMaximum(4)
QCDDiff.SetMinimum(0)
QCDDiff.Draw('PE')
flErr = r.TGraph(4)
flErr.SetPoint(0, ptBins[0],QCDDiff.GetBinContent(1)+QCDDiff.GetBinError(1))
flErr.SetPoint(3, ptBins[0],QCDDiff.GetBinContent(1)-QCDDiff.GetBinError(1))
flErr.SetPoint(1, ptBins[1],QCDDiff.GetBinContent(1)+QCDDiff.GetBinError(1))
flErr.SetPoint(2, ptBins[1],QCDDiff.GetBinContent(1)-QCDDiff.GetBinError(1))
flErr.SetFillStyle(3001)
flErr.SetFillColor(r.kRed)
fmErr = r.TGraph(4)
fmErr.SetPoint(0, ptBins[1],QCDDiff.GetBinContent(2)+QCDDiff.GetBinError(2))
fmErr.SetPoint(3, ptBins[1],QCDDiff.GetBinContent(2)-QCDDiff.GetBinError(2))
fmErr.SetPoint(1, ptBins[2],QCDDiff.GetBinContent(2)+QCDDiff.GetBinError(2))
fmErr.SetPoint(2, ptBins[2],QCDDiff.GetBinContent(2)-QCDDiff.GetBinError(2))
fmErr.SetFillStyle(3001)
fmErr.SetFillColor(r.kBlue)
fhErr = r.TGraph(4)
fhErr.SetPoint(0, ptBins[2],QCDDiff.GetBinContent(3)+QCDDiff.GetBinError(3))
fhErr.SetPoint(3, ptBins[2],QCDDiff.GetBinContent(3)-QCDDiff.GetBinError(3))
fhErr.SetPoint(1, ptBins[3],QCDDiff.GetBinContent(3)+QCDDiff.GetBinError(3))
fhErr.SetPoint(2, ptBins[3],QCDDiff.GetBinContent(3)-QCDDiff.GetBinError(3))
fhErr.SetFillStyle(3001)
fhErr.SetFillColor(r.kGreen)
flErr.Draw('fsame')
fmErr.Draw('fsame')
fhErr.Draw('fsame')
lFit1 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(flErr,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(QCDDiff.GetBinContent(1), QCDDiff.GetBinError(1))),
(fmErr,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(QCDDiff.GetBinContent(2), QCDDiff.GetBinError(2))),
(fhErr,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(QCDDiff.GetBinContent(3), QCDDiff.GetBinError(3)))])
lFit1.Draw('same')
for k in range(3):
c.cd(k+2)
if logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k+2)
QCDHistList[k].SetTitle('%s %s Data - MC Events %s (%.1f fb^{-1}); %s; events / bin' %(signSelection, iso, titleName, Lumi,varName))
QCDHistList[k].SetMarkerStyle(8)
QCDHistList[k].SetMarkerSize(0.9)
QCDHistList[k].SetMaximum(max)
QCDHistList[k].SetMinimum(1)
QCDHistList[k].Draw('PE')
c.Update()
c.Print('%s' %psfile)
c.cd(1)
r.gPad.SetLogy(0)
QCDDiff2.SetTitle('tight/relaxed MultiJet SS Events %s (%.1f fb^{-1}); %s; tight/relaxed' %(titleName, Lumi,varName))
QCDDiff2.SetMarkerStyle(8)
QCDDiff2.SetMarkerSize(0.9)
QCDDiff2.SetMinimum(0)
QCDDiff2.SetMaximum(0.2)
QCDDiff2.Draw('PE')
flErr2 = r.TGraph(4)
flErr2.SetPoint(0, ptBins[0],QCDDiff2.GetBinContent(1)+QCDDiff2.GetBinError(1))
flErr2.SetPoint(3, ptBins[0],QCDDiff2.GetBinContent(1)-QCDDiff2.GetBinError(1))
flErr2.SetPoint(1, ptBins[1],QCDDiff2.GetBinContent(1)+QCDDiff2.GetBinError(1))
flErr2.SetPoint(2, ptBins[1],QCDDiff2.GetBinContent(1)-QCDDiff2.GetBinError(1))
flErr2.SetFillStyle(3001)
flErr2.SetFillColor(r.kRed)
fmErr2 = r.TGraph(4)
fmErr2.SetPoint(0, ptBins[1],QCDDiff2.GetBinContent(2)+QCDDiff2.GetBinError(2))
fmErr2.SetPoint(3, ptBins[1],QCDDiff2.GetBinContent(2)-QCDDiff2.GetBinError(2))
fmErr2.SetPoint(1, ptBins[2],QCDDiff2.GetBinContent(2)+QCDDiff2.GetBinError(2))
fmErr2.SetPoint(2, ptBins[2],QCDDiff2.GetBinContent(2)-QCDDiff2.GetBinError(2))
fmErr2.SetFillStyle(3001)
fmErr2.SetFillColor(r.kBlue)
fhErr2 = r.TGraph(4)
fhErr2.SetPoint(0, ptBins[2],QCDDiff2.GetBinContent(3)+QCDDiff2.GetBinError(3))
fhErr2.SetPoint(3, ptBins[2],QCDDiff2.GetBinContent(3)-QCDDiff2.GetBinError(3))
fhErr2.SetPoint(1, ptBins[3],QCDDiff2.GetBinContent(3)+QCDDiff2.GetBinError(3))
fhErr2.SetPoint(2, ptBins[3],QCDDiff2.GetBinContent(3)-QCDDiff2.GetBinError(3))
fhErr2.SetFillStyle(3001)
fhErr2.SetFillColor(r.kGreen)
flErr2.Draw('fsame')
fmErr2.Draw('fsame')
fhErr2.Draw('fsame')
lFit2 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(flErr2,'Scale between relaxed/tight in SS region: %.3f \pm %.3f' %(QCDDiff2.GetBinContent(1), QCDDiff2.GetBinError(1))),
(fmErr2,'Scale between relaxed/tight in SS region: %.3f \pm %.3f' %(QCDDiff2.GetBinContent(2), QCDDiff2.GetBinError(2))),
(fhErr2,'Scale between relaxed/tight in SS region: %.3f \pm %.3f' %(QCDDiff2.GetBinContent(3), QCDDiff2.GetBinError(3)))])
lFit2.Draw('same')
c.Print('%s)' %psfile)
#ps.Close()
print "Plot saved at %s" %(psfile)
def go(sig, bkg):
bins_array = array('d', range(20,151,1))
sigHist = r.TH2F('sigHist', "", len(bins_array)-1, bins_array,len(bins_array)-1, bins_array)
bkgHist = r.TH2F('bkgHist', "", len(bins_array)-1, bins_array,len(bins_array)-1, bins_array)
sigHistJet1 = r.TH2F('sigHistJet1', "", len(bins_array)-1, bins_array,len(bins_array)-1, bins_array)
bkgHistJet1 = r.TH2F('bkgHistJet1', "", len(bins_array)-1, bins_array,len(bins_array)-1, bins_array)
sigHistJet2 = r.TH2F('sigHistJet2', "", len(bins_array)-1, bins_array,len(bins_array)-1, bins_array)
bkgHistJet2 = r.TH2F('bkgHistJet2', "", len(bins_array)-1, bins_array,len(bins_array)-1, bins_array)
sigHistJet3 = r.TH2F('sigHistJet3', "", len(bins_array)-1, bins_array,len(bins_array)-1, bins_array)
bkgHistJet3 = r.TH2F('bkgHistJet3', "", len(bins_array)-1, bins_array,len(bins_array)-1, bins_array)
bkgHist2 = r.TH2F('bkgHist2', "", 10000, 0, 1.0, 10000, 0, 1.0)
bkgHistJet = r.TH2F('bkgHistJet', "", 10000, 0, 1.0, 10000, 0, 1.0)
distHist3 = r.TH1F('distHist3', "", len(bins_array)-1, bins_array)
distHist = r.TGraph()
distHist_2 = r.TGraph()
distHist2 = r.TGraph()
distHist2_2 = r.TGraph()
distHist4 = r.TGraph()
distHist4_2 = r.TGraph()
distHist5 = r.TGraph()
distHist5_2 = r.TGraph()
distHist3.SetTitle('; PtHat; weight')
# distHist2 = r.TH2F('distHist2', "", len(bins_array)-1, bins_array, len(bins_array)-1, bins_array)
sigHist = getPtDistribution(sig, sigHist, None, None, 0.0, 'central', False, True)
sigHistJet1 = getPtDistribution(sig, sigHistJet1, None, None, 30.0, 'central', False, True)
sigHistJet2 = getPtDistribution(sig, sigHistJet2, None, None, 40.0)
sigHistJet3 = getPtDistribution(sig, sigHistJet3, None, None, 50.0)
sigHist.SetTitle('h2#tau#tau; Leading jet Pt; Leading #tau Pt')
bkgHist, bkgHist2, distHist3 = getPtDistribution(bkg, bkgHist, bkgHist2, distHist3, 0.0, 'central', False, True)
bkgHistJet1, bkgHistJet = getPtDistribution(bkg, bkgHistJet1, bkgHistJet, None, 30.0, 'central', False, True)
bkgHistJet2, bkgHistJet = getPtDistribution(bkg, bkgHistJet2, bkgHistJet, None, 40.0)
bkgHistJet3, bkgHistJet = getPtDistribution(bkg, bkgHistJet3, bkgHistJet, None, 50.0)
bkgHist.SetTitle('QCD; Leading jet Pt; Leading #tau Pt')
bkgHist2.SetTitle('QCD; 200000*PtHat^{-4.5}; weight')
getBothDistribution(sigHist, bkgHist, distHist, distHist_2)
getBothDistribution(sigHist, bkgHist, distHist5, distHist5_2, 5)
getBothDistribution(sigHist, bkgHist, distHist2, distHist2_2, 10)
getBothDistribution(sigHist, bkgHist, distHist4, distHist4_2, 15)
jet1 = r.TGraph()
jet1_2 = r.TGraph()
jet2 = r.TGraph()
jet2_2 = r.TGraph()
jet3 = r.TGraph()
jet3_2 = r.TGraph()
getBothDistribution(sigHistJet1, bkgHistJet1, jet1, jet1_2, 0, 30.5)
getBothDistribution(sigHistJet2, bkgHistJet2, jet2, jet2_2, 0, 30.5)
getBothDistribution(sigHistJet3, bkgHistJet3, jet3, jet3_2, 0, 30.5)
fakeHist1 = r.TH1F()
fakeHist1.SetMarkerStyle(5)
fakeHist2 = r.TH1F()
fakeHist2.SetMarkerStyle(2)
fakeHist2.SetMarkerColor(r.kBlue)
fakeHist3 = r.TH1F()
fakeHist3.SetMarkerStyle(25)
fakeHist3.SetMarkerColor(r.kRed)
fakeHist4 = r.TH1F()
fakeHist4.SetMarkerStyle(32)
fakeHist4.SetMarkerColor(r.kGreen)
fakeHist5 = r.TH1F()
fakeHist5.SetMarkerStyle(5)
fakeHist5.SetMarkerColor(r.kBlue)
fakeHist6 = r.TH1F()
fakeHist6.SetMarkerStyle(5)
fakeHist6.SetMarkerColor(r.kRed)
fakeHist7 = r.TH1F()
fakeHist7.SetMarkerStyle(5)
fakeHist7.SetMarkerColor(r.kGreen)
psfile = 'test2.pdf'
c = r.TCanvas("c","Test", 600, 600)
c.SetGrid()
c.SetFillStyle(4000)
# distHist.GetXaxis().SetLimits(0.998,1.00001)
distHist.GetXaxis().SetLimits(0.999,1.00001)
distHist.SetMinimum(0.001)
distHist.SetMaximum(0.8)
c.SetLogy()
distHist.GetXaxis().SetNdivisions(210)
distHist.Draw("AP")
distHist.SetMarkerStyle(7)
distHist.GetYaxis().SetTitleOffset(1.4)
distHist5.SetMarkerStyle(7)
distHist5_2.SetMarkerColor(r.kBlue)
distHist5.SetMarkerColor(r.kBlue)
# distHist5.Draw('PSame')
distHist2.SetMarkerStyle(7)
distHist2_2.SetMarkerColor(r.kRed)
distHist2.SetMarkerColor(r.kRed)
# distHist2.Draw('PSame')
distHist4.SetMarkerStyle(7)
distHist4_2.SetMarkerColor(r.kGreen)
distHist4.SetMarkerColor(r.kGreen)
# distHist4.Draw('PSame')
distHist_2.SetMarkerStyle(5)
distHist5_2.SetMarkerStyle(2)
distHist2_2.SetMarkerStyle(25)
distHist4_2.SetMarkerStyle(32)
# distHist2_2.Draw('PSame')
# distHist5_2.Draw('PSame')
distHist_2.Draw('PSame')
# distHist4_2.Draw('PSame')
jet1.SetMarkerStyle(7)
jet1.SetMarkerColor(r.kBlue)
jet1_2.SetMarkerStyle(5)
jet1_2.SetMarkerColor(r.kBlue)
jet2.SetMarkerStyle(7)
jet2.SetMarkerColor(r.kRed)
jet2_2.SetMarkerStyle(5)
jet2_2.SetMarkerColor(r.kRed)
jet3.SetMarkerStyle(7)
jet3.SetMarkerColor(r.kGreen)
jet3_2.SetMarkerStyle(5)
jet3_2.SetMarkerColor(r.kGreen)
jet1.Draw('PSame')
jet1_2.Draw('PSame')
jet2.Draw('PSame')
jet2_2.Draw('PSame')
jet3.Draw('PSame')
jet3_2.Draw('PSame')
l = tool.setMyLegend(lPosition = [0.2, 0.45, 0.5, 0.25], lHistList = [(fakeHist1, 'same pt cut (tau pt 40)'),
# (fakeHist2, 'leading + 5 (35, 40, 45)'),
# (fakeHist3, 'leading + 10 (35, 40, 45)'),
# (fakeHist4, 'leading + 15 (35, 40, 45)'),
(fakeHist5, 'same with jet pt 30 (tau pt 30)'),
(fakeHist6, 'same with jet pt 40 (tau pt 30)'),
(fakeHist7, 'same with jet pt 50 (tau pt 30)'),
])
l.Draw('same')
c.Print('%s(' %psfile)
c.SetLogy(0)
c.Clear()
distHist3.Draw()
c.Print('%s' %psfile)
c.Clear()
# p1 = r.TPad("p1","p1",0.,1,1.,0.0)
# p1.SetMargin(0.13, 0.13, 0.13, 0.13)
# p1.SetGrid()
p2 = r.TPad("p1","p1",0.,1,1.,0.0)
p2.SetMargin(0.13, 0.13, 0.13, 0.13)
p2.SetGrid()
#
# # p1.Draw()
# # p1.cd()
# # distHist2.GetXaxis().SetNdivisions(216)
# # distHist2.GetYaxis().SetNdivisions(216)
# # distHist2.GetYaxis().SetTitleOffset(1.2)
# # distHist2.Draw('COLZ')
# # c.Print('%s' %psfile)
# # c.Clear()
p2.Draw()
p2.cd()
sigHist.Draw('COLZ')
sigHist.GetYaxis().SetTitleOffset(1.2)
sigHist.GetXaxis().SetNdivisions(216)
sigHist.GetYaxis().SetNdivisions(216)
c.Print('%s' %psfile)
c.Clear()
p3 = r.TPad("p1","p1",0.,1,1.,0.0)
p3.SetMargin(0.13, 0.13, 0.13, 0.13)
p3.SetGrid()
p3.Draw()
p3.cd()
bkgHist.Draw('COLZ')
bkgHist.GetYaxis().SetTitleOffset(1.2)
bkgHist.GetXaxis().SetNdivisions(216)
bkgHist.GetYaxis().SetNdivisions(216)
c.Print('%s' %psfile)
c.Clear()
bkgHistJet2.Draw('COLZ')
bkgHistJet2.GetYaxis().SetTitleOffset(1.2)
bkgHistJet2.GetXaxis().SetNdivisions(216)
bkgHistJet2.GetYaxis().SetNdivisions(216)
c.Print('%s' %psfile)
c.Clear()
sigHistJet2.Draw('COLZ')
sigHistJet2.GetYaxis().SetTitleOffset(1.2)
sigHistJet2.GetXaxis().SetNdivisions(216)
sigHistJet2.GetYaxis().SetNdivisions(216)
c.Print('%s)' %psfile)
c.Close()
def yieldCalculator(dy_mc, tt_full_mc, dy_embed, tt_embed, massWindow, pairOption = 'iso', nBtag = '', doDraw = False):
yieldForMediumCat = {}
eventCounterForMediumCat = {}
yieldForLooseCat = {}
eventCounterForLooseCat = {}
inclusiveYields = {}
eventCounterForInclusive = {}
bins = [19, 225, 700]
dy_embed_1M = r.TH1F('dy_embed_1M', '', bins[0], bins[1], bins[2])
dy_embed_2M = r.TH1F('dy_embed_2M', '', bins[0], bins[1], bins[2])
tt_embed_1M = r.TH1F('tt_embed_1M', '', bins[0], bins[1], bins[2])
tt_embed_2M = r.TH1F('tt_embed_2M', '', bins[0], bins[1], bins[2])
fileList = [('DY_inclusive', dy_mc), ('tt_inclusive', tt_full_mc), ('DY_embed', dy_embed), ('tt_embed', tt_embed)]
for indexFile in range(len(fileList)):
name = fileList[indexFile][0]
iFile = r.TFile(fileList[indexFile][1])
iTree = iFile.Get('eventTree')
yieldForMediumCat[name+'_1M'] = 0.0
yieldForMediumCat[name+'_2M'] = 0.0
yieldForLooseCat[name+'_1L'] = 0.0
yieldForLooseCat[name+'_2L'] = 0.0
eventCounterForInclusive[name] = 0
eventCounterForMediumCat[name+'_1M'] = 0
eventCounterForMediumCat[name+'_2M'] = 0
eventCounterForLooseCat[name+'_1L'] = 0
eventCounterForLooseCat[name+'_2L'] = 0
total = iTree.GetEntries()
inclusiveYields[name] = 0.0
counter = 0
counter_0 = 0
isEmbed = False
isData = False
if 'emb' in name:
isEmbed = True
if name == 'DY_embed':
isData = True
for i in range(0, total):
tool.printProcessStatus(iCurrent=i+1, total=total, processName = 'Looping sample [%s]' %name, iPrevious=i)
#Fill Histograms
iTree.GetEntry(i)
#get the right pair of taus based on isoMin or ptMax
if (iTree.nElectrons > 0 or iTree.nMuons > 0):
continue
#get event category
signSelection, isoSelection, bTagSelection = makeWholeTools2.findCategory(tree = iTree,
iso = makeWholeSample_cfg.iso,
option = pairOption,
isData = isData,
relaxedRegionOption = makeWholeSample_cfg.Relax,
isEmbed = isEmbed,
usePassJetTrigger = makeWholeSample_cfg.usePassJetTrigger,
nBtag = nBtag)
if signSelection == None or isoSelection == None or bTagSelection == None:
continue
looseTag, mediumTag = getRightBTagCatName(bTagSelection)
if iTree.HLT_Any == 0:
continue
if iTree.ZTT == 0:
continue
if 'tt' not in name:
tmpEventYield = iTree.triggerEff*iTree.decayModeWeight
else:
tmpEventYield = iTree.triggerEff
if massWindow and not makeWholeTools2.passCut(iTree, pairOption):
continue
#calculate inclusiveYield
if (signSelection == "OS") and (isoSelection == "Tight"):
eventCounterForInclusive[name] += 1
if 'data' in iTree.sampleName:
inclusiveYields[name] += tmpEventYield*iTree.embeddedWeight
elif isEmbed:
inclusiveYields[name] += iTree.triggerEff*iTree.xs*0.983*(lumi/tt_semi_InitEvents)*iTree.embeddedWeight*iTree.PUWeight
else:
if not makeWholeTools2.passJetTrigger(iTree):
continue
inclusiveYields[name] += tmpEventYield*iTree.xs*lumi*iTree.PUWeight/(iTree.initEvents+0.0)
else:
continue
fillValue = iTree.fMassKinFit
#calculate category yield
if mediumTag != '0M':
if isEmbed:
if 'data' in iTree.sampleName:
yieldForMediumCat["%s_%s" %(name, mediumTag)] += tmpEventYield*iTree.embeddedWeight
if mediumTag == '1M':
dy_embed_1M.Fill(fillValue, tmpEventYield*iTree.embeddedWeight)
else:
dy_embed_2M.Fill(fillValue, tmpEventYield*iTree.embeddedWeight)
else:
yieldForMediumCat["%s_%s" %(name, mediumTag)] += tmpEventYield*iTree.xs*0.983*(lumi/tt_semi_InitEvents)*iTree.embeddedWeight*iTree.PUWeight
if mediumTag == '1M':
tt_embed_1M.Fill(fillValue, tmpEventYield*iTree.xs*0.983*(lumi/tt_semi_InitEvents)*iTree.embeddedWeight*iTree.PUWeight)
else:
tt_embed_2M.Fill(fillValue, tmpEventYield*iTree.xs*0.983*(lumi/tt_semi_InitEvents)*iTree.embeddedWeight*iTree.PUWeight)
else:
yieldForMediumCat["%s_%s" %(name, mediumTag)] += tmpEventYield*iTree.xs*lumi*iTree.PUWeight/iTree.initEvents
eventCounterForMediumCat["%s_%s" %(name, mediumTag)] += 1
#calculate loose cat yield for embed samples
if isEmbed and (looseTag != '0L'):
if 'data' in iTree.sampleName:
yieldForLooseCat["%s_%s" %(name, looseTag)] += tmpEventYield*iTree.embeddedWeight
else:
yieldForLooseCat["%s_%s" %(name, looseTag)] += tmpEventYield*iTree.embeddedWeight*iTree.xs*0.983*lumi/tt_semi_InitEvents
eventCounterForLooseCat["%s_%s" %(name, looseTag)] += 1
print ''
if isEmbed:
print name
print 'inclusive yield for %s: %.2f +/- %.2f' %(name, inclusiveYields[name], calcSysUncSingle(inclusiveYields[name], eventCounterForInclusive[name]))
print '1-Medium %s yield: %.2f +/- %.2f \t events: %i' %(name, yieldForMediumCat[name+'_1M'], calcSysUncSingle(yieldForMediumCat[name+'_1M'], eventCounterForMediumCat[name+'_1M']), eventCounterForMediumCat[name+'_1M'])
print '1-Loose %s yield: %.2f +/- %.2f \t events: %i' %(name, yieldForLooseCat[name+'_1L'], calcSysUncSingle(yieldForLooseCat[name+'_1L'], eventCounterForLooseCat[name+'_1L']), eventCounterForLooseCat[name+'_1L'])
print '2-Medium %s yield: %.2f +/- %.2f \t events: %i' %(name, yieldForMediumCat[name+'_2M'], calcSysUncSingle(yieldForMediumCat[name+'_2M'], eventCounterForMediumCat[name+'_2M']), eventCounterForMediumCat[name+'_2M'])
print '2-Loose %s yield: %.2f +/- %.2f \t events: %i' %(name, yieldForLooseCat[name+'_2L'], calcSysUncSingle(yieldForLooseCat[name+'_2L'], eventCounterForLooseCat[name+'_2L']), eventCounterForLooseCat[name+'_2L'])
else:
print 'inclusive yield for %s: %.2f' %(name, inclusiveYields[name])
delta_dy_inclusive = calcSysUncSingle(inclusiveYields['DY_embed'], eventCounterForInclusive['DY_embed'])
delta_tt_inclusive = calcSysUncSingle(inclusiveYields['tt_embed'], eventCounterForInclusive['tt_embed'])
delta_dy_1M = calcSysUncSingle(yieldForMediumCat['DY_embed_1M'], eventCounterForMediumCat['DY_embed_1M'])
delta_tt_1M = calcSysUncSingle(yieldForMediumCat['tt_embed_1M'], eventCounterForMediumCat['tt_embed_1M'])
delta_dy_2M = calcSysUncSingle(yieldForMediumCat['DY_embed_2M'], eventCounterForMediumCat['DY_embed_2M'])
delta_tt_2M = calcSysUncSingle(yieldForMediumCat['tt_embed_2M'], eventCounterForMediumCat['tt_embed_2M'])
sysUnc_1M = calcSysUnc(sf = (yieldForMediumCat['DY_embed_1M']-yieldForMediumCat['tt_embed_1M'])/(inclusiveYields['DY_embed'] - inclusiveYields['tt_embed']),
numA = yieldForMediumCat['DY_embed_1M'],
numB = yieldForMediumCat['tt_embed_1M'],
denomA = inclusiveYields['DY_embed'],
denomB = inclusiveYields['tt_embed'],
delta_numA = delta_dy_1M,
delta_numB = delta_tt_1M,
delta_denomA = delta_dy_inclusive,
delta_denomB = delta_tt_inclusive)
sysUnc_2M = calcSysUnc(sf = (yieldForMediumCat['DY_embed_2M']-yieldForMediumCat['tt_embed_2M'])/(inclusiveYields['DY_embed'] - inclusiveYields['tt_embed']),
numA = yieldForMediumCat['DY_embed_2M'],
numB = yieldForMediumCat['tt_embed_2M'],
denomA = inclusiveYields['DY_embed'],
denomB = inclusiveYields['tt_embed'],
delta_numA = delta_dy_2M,
delta_numB = delta_tt_2M,
delta_denomA = delta_dy_inclusive,
delta_denomB = delta_tt_inclusive)
sysUnc_xs_1M = calcSysUnc(sf = (yieldForMediumCat['DY_embed_1M']-yieldForMediumCat['tt_embed_1M'])/(inclusiveYields['DY_embed'] - inclusiveYields['tt_embed']),
numA = yieldForMediumCat['DY_embed_1M'],
numB = yieldForMediumCat['tt_embed_1M'],
denomA = inclusiveYields['DY_embed'],
denomB = inclusiveYields['tt_embed'],
delta_numA = 0,
delta_numB = yieldForMediumCat['tt_embed_1M']*0.1,
delta_denomA = 0,
delta_denomB = inclusiveYields['tt_embed']*0.1)
sysUnc_xs_2M = calcSysUnc(sf = (yieldForMediumCat['DY_embed_2M']-yieldForMediumCat['tt_embed_2M'])/(inclusiveYields['DY_embed'] - inclusiveYields['tt_embed']),
numA = yieldForMediumCat['DY_embed_2M'],
numB = yieldForMediumCat['tt_embed_2M'],
denomA = inclusiveYields['DY_embed'],
denomB = inclusiveYields['tt_embed'],
delta_numA = 0,
delta_numB = yieldForMediumCat['tt_embed_2M']*0.1,
delta_denomA = 0,
delta_denomB = inclusiveYields['tt_embed']*0.1)
preScaleFactor = inclusiveYields['DY_inclusive']/(inclusiveYields['DY_embed'] - inclusiveYields['tt_embed'])
scaleFactor_1M = preScaleFactor*(yieldForMediumCat['DY_embed_1M']-yieldForMediumCat['tt_embed_1M'])
scaleFactor_2M = preScaleFactor*(yieldForMediumCat['DY_embed_2M']-yieldForMediumCat['tt_embed_2M'])
preScaleFactor2 = (inclusiveYields['DY_inclusive']+inclusiveYields['tt_inclusive'])/(inclusiveYields['DY_embed'])
scaleFactor_1M2 = preScaleFactor2*(yieldForMediumCat['DY_embed_1M'])
scaleFactor_2M2 = preScaleFactor2*(yieldForMediumCat['DY_embed_2M'])
print ''
print 'predicted DY in 1M: %.2fx(%.2f +/- %.2f - %.2f +/- %.2f)/(%.2f +/- %.2f - %.2f +/- %.2f) = %.2f +/- %.2f%% (statistical)' %(inclusiveYields['DY_inclusive'],
yieldForMediumCat['DY_embed_1M'],
calcSysUncSingle(yieldForMediumCat['DY_embed_1M'], eventCounterForMediumCat['DY_embed_1M']),
yieldForMediumCat['tt_embed_1M'],
calcSysUncSingle(yieldForMediumCat['tt_embed_1M'], eventCounterForMediumCat['tt_embed_1M']),
inclusiveYields['DY_embed'],
calcSysUncSingle(inclusiveYields['DY_embed'], eventCounterForInclusive['DY_embed']),
inclusiveYields['tt_embed'],
calcSysUncSingle(inclusiveYields['tt_embed'], eventCounterForInclusive['tt_embed']),
scaleFactor_1M,
sysUnc_1M*inclusiveYields['DY_inclusive']/scaleFactor_1M*100
)
print 'predicted DY in 2M: %.2fx(%.2f +/- %.2f - %.2f +/- %.2f)/(%.2f +/- %.2f - %.2f +/- %.2f) = %.2f +/- %.2f%% (statistical)' %(inclusiveYields['DY_inclusive'],
yieldForMediumCat['DY_embed_2M'],
calcSysUncSingle(yieldForMediumCat['DY_embed_2M'], eventCounterForMediumCat['DY_embed_2M']),
yieldForMediumCat['tt_embed_2M'],
calcSysUncSingle(yieldForMediumCat['tt_embed_2M'], eventCounterForMediumCat['tt_embed_2M']),
inclusiveYields['DY_embed'],
calcSysUncSingle(inclusiveYields['DY_embed'], eventCounterForInclusive['DY_embed']),
inclusiveYields['tt_embed'],
calcSysUncSingle(inclusiveYields['tt_embed'], eventCounterForInclusive['tt_embed']),
scaleFactor_2M,
sysUnc_2M*inclusiveYields['DY_inclusive']/scaleFactor_2M*100
)
print ''
print 'predicted DY in 1M: %.2fx(%.2f - %.2f +/- %.2f)/(%.2f - %.2f +/- %.2f) = %.2f +/- %.2f%% (tt_xs)' %(inclusiveYields['DY_inclusive'],
yieldForMediumCat['DY_embed_1M'],
yieldForMediumCat['tt_embed_1M'],
yieldForMediumCat['tt_embed_1M']*0.1,
inclusiveYields['DY_embed'],
inclusiveYields['tt_embed'],
inclusiveYields['tt_embed']*0.1,
scaleFactor_1M,
sysUnc_xs_1M*inclusiveYields['DY_inclusive']/scaleFactor_1M*100
)
print 'predicted DY in 2M: %.2fx(%.2f - %.2f +/- %.2f)/(%.2f - %.2f +/- %.2f) = %.2f +/- %.2f%% (tt_xs)' %(inclusiveYields['DY_inclusive'],
yieldForMediumCat['DY_embed_2M'],
yieldForMediumCat['tt_embed_2M'],
yieldForMediumCat['tt_embed_2M']*0.1,
inclusiveYields['DY_embed'],
inclusiveYields['tt_embed'],
inclusiveYields['tt_embed']*0.1,
scaleFactor_2M,
sysUnc_xs_2M*inclusiveYields['DY_inclusive']/scaleFactor_2M*100
)
print ''
print 'DY MC in 1M: %.2f' %yieldForMediumCat['DY_inclusive_1M']
print 'DY MC in 2M: %.2f' %yieldForMediumCat['DY_inclusive_2M']
# print 'predicted DY+tt in 1M: %.2f' %scaleFactor_1M2
# print 'predicted DY+tt in 2M: %.2f' %scaleFactor_2M2
if doDraw:
position = (0.6, 0.9 - 0.06*2, 0.87, 0.9)
l1 = tool.setMyLegend(position, [(dy_embed_1M, 'dy_embed_1M'), (tt_embed_1M, 'tt_embed_1M')])
l2 = tool.setMyLegend(position, [(dy_embed_2M, 'dy_embed_2M'), (tt_embed_2M, 'tt_embed_2M')])
psfile = 'DY_embed_tauUp.pdf'
c = r.TCanvas("c","Test", 800, 600)
dy_embed_1M.Draw()
dy_embed_1M.SetTitle(';fMassKinFit;')
tt_embed_1M.SetLineStyle(2)
tt_embed_1M.SetLineColor(r.kRed)
tt_embed_1M.Draw('same')
l1.Draw('same')
c.Print('%s(' %psfile)
dy_embed_2M.Draw()
dy_embed_2M.SetTitle(';fMassKinFit;')
tt_embed_2M.SetLineStyle(2)
tt_embed_2M.SetLineColor(r.kRed)
tt_embed_2M.Draw('same')
l2.Draw('same')
c.Print('%s)' %psfile)
return scaleFactor_1M, scaleFactor_2M, scaleFactor_1M2, scaleFactor_2M2, preScaleFactor
HistMaxList[0][1].SetTitle("BDT training at %s; BDT; " %(trainMass))
#draw from the highest histogram
c = r.TCanvas("c","Test", 800, 600);
ps = r.TPostScript(psfile,112);
r.gPad.SetTickx()
r.gPad.SetTicky()
ps.NewPage()
HistMaxList[0][1].Draw()
HistMaxList[1][1].Draw("same")
HistMaxList[2][1].Draw("same")
HistMaxList[3][1].Draw("same")
l = tool.setMyLegend(lPosition=legendPosition, lHistList=histList)
l.Draw("same")
c.Update()
ps.NewPage()
c.cd()
r.gPad.SetGrid()
for i in range(49):
bkgRejection = 1 - BDT_bkg.Integral(98-i*2,99)
sigEff1 = BDT_1.Integral(98-i*2,99)
sigEff2 = BDT_2.Integral(98-i*2,99)
sigEff3 = BDT_3.Integral(98-i*2,99)
if bkgRejection == 1.0 or sigEff1 >= 1.0 or sigEff2 >= 1.0 or sigEff3 >= 1.0:
continue
eff_1.Fill(sigEff1, bkgRejection)
eff_2.Fill(sigEff2, bkgRejection)
def getHistos(varName, signalSelection, logY, sigBoost, nbins, useData, max,
rangeMin, rangeMax, location, bTag, predict, predictPtBin,
region, thirdLeptonVeto):
r.gStyle.SetOptStat(0)
fileList = draw_cfg.MCFileList
histList = []
histList_4QCD = []
QCDHistList = []
QCDHistList_4KS = []
QCDHistList_withScale = []
varRange = [nbins, rangeMin, rangeMax]
nBins = 10000
Lumi = 19.7
initNEventsList = []
legendHistos = []
var_background = []
scaleMCPt = 1.0
tmpFile = []
tmpTree = []
var_data_4KS = []
var_data = []
var_data_4QCD = []
histList_4KS = []
for i in range(5):
var_data.append(
r.TH1F('data_%i' % (i), "", varRange[0], varRange[1], varRange[2]))
var_data_4KS.append(
r.TH1F('data_4KS_%i' % (i), "", nBins, varRange[1], varRange[2]))
if i < 5:
var_data_4QCD.append(
r.TH1F('data_4QCD_%i' % (i), "", varRange[0], varRange[1],
varRange[2]))
dataName = draw_cfg.dataFile
fData = r.TFile(dataName)
treeData = fData.Get('eventTree')
print 'Adding events from: %s ...' % dataName
for iEntry in range(treeData.GetEntries()):
treeData.GetEntry(iEntry)
select = passCut(treeData, bTag, region, thirdLeptonVeto)
if (select == 0) or (select == 1) or (select > 6):
continue
var_data[select - 2].Fill(varsList.findVar(treeData, varName))
var_data_4KS[select - 2].Fill(varsList.findVar(treeData, varName))
if select == 2:
var_data_4QCD[0].Fill(
varsList.findVar(treeData, varName),
findPtScale(treeData.pt1.at(0), treeData.pt2.at(0), 'left',
'%s%s' % (region, bTag)))
elif select == 3:
var_data_4QCD[1].Fill(
varsList.findVar(treeData, varName),
findPtScale(treeData.pt1.at(0), treeData.pt2.at(0), 'up',
'%s%s' % (region, bTag)))
elif select == 4:
var_data_4QCD[2].Fill(
varsList.findVar(treeData, varName),
findPtScale(treeData.pt1.at(0), treeData.pt2.at(0), 'left',
'%s%s' % (region, bTag)))
var_data_4QCD[3].Fill(
varsList.findVar(treeData, varName),
findPtScale(treeData.pt1.at(0), treeData.pt2.at(0), 'up',
'%s%s' % (region, bTag)))
legendHistos.append([])
for j in range(5):
var_data[j].SetMarkerStyle(8)
var_data[j].SetMarkerSize(0.9)
legendHistos.append([])
legendHistos[j + 1].append(
(var_data[j], 'observed (%.0f)' % var_data[j].Integral()))
for i in range(len(fileList)):
for j in range(6):
histList_4KS.append(
r.TH1F('%s_%i_KS' % (fileList[i][0], j), fileList[i][0], nBins,
varRange[1], varRange[2]))
histList.append(
r.TH1F('%s_%i' % (fileList[i][0], j), fileList[i][0],
varRange[0], varRange[1], varRange[2]))
histList_4QCD.append(
r.TH1F('%s_%i_2' % (fileList[i][0], j), fileList[i][0],
varRange[0], varRange[1], varRange[2]))
print 'Adding events from: %s ...' % (fileList[i][1])
tmpFile.append(r.TFile(fileList[i][1]))
tmpTree.append(tmpFile[i].Get('eventTree'))
for iEntry in range(tmpTree[i].GetEntries()):
tmpTree[i].GetEntry(iEntry)
select = passCut(tmpTree[i], bTag, region, thirdLeptonVeto)
if (not select) or (select > 6):
continue
histList[6 * i + select - 1].Fill(
varsList.findVar(tmpTree[i], varName) * scaleMCPt,
tmpTree[i].triggerEff)
histList_4KS[6 * i + select - 1].Fill(
varsList.findVar(tmpTree[i], varName) * scaleMCPt,
tmpTree[i].triggerEff)
if select == 2:
histList_4QCD[6 * i].Fill(
varsList.findVar(tmpTree[i], varName) * scaleMCPt,
tmpTree[i].triggerEff *
findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0),
'left', '%s%s' % (region, bTag)))
elif select == 3:
histList_4QCD[6 * i + 1].Fill(
varsList.findVar(tmpTree[i], varName) * scaleMCPt,
tmpTree[i].triggerEff *
findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0),
'up', '%s%s' % (region, bTag)))
elif select == 4:
histList_4QCD[6 * i + 2].Fill(
varsList.findVar(tmpTree[i], varName) * scaleMCPt,
tmpTree[i].triggerEff *
findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0),
'left', '%s%s' % (region, bTag)))
histList_4QCD[6 * i + 3].Fill(
varsList.findVar(tmpTree[i], varName) * scaleMCPt,
tmpTree[i].triggerEff *
findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0),
'up', '%s%s' % (region, bTag)))
initNEventsList.append(tmpFile[i].Get('preselection'))
for j in range(6):
var_background.append(r.THStack())
histList[6 * i + j].SetFillColor(fileList[i][3])
histList[6 * i + j].Scale(fileList[i][2] * Lumi /
initNEventsList[i].GetBinContent(1))
histList_4QCD[6 * i + j].Scale(fileList[i][2] * Lumi /
initNEventsList[i].GetBinContent(1))
histList_4KS[6 * i + j].Scale(fileList[i][2] * Lumi /
initNEventsList[i].GetBinContent(1))
var_background[j].Add(histList[6 * i + j])
legendHistos[j].append(
(histList[6 * i + j], '%s (%.2f)' %
(fileList[i][0], histList[6 * i + j].Integral())))
data_i = []
MC_i = []
data_r = []
MC_r = []
e = []
MC_List = []
for i in range(3):
QCDHistList.append(
r.TH1F('QCD_%i' % (i), "", varRange[0], varRange[1], varRange[2]))
QCDHistList_4KS.append(
r.TH1F('QCD_%i_KS' % (i), "", nBins, varRange[1], varRange[2]))
MC_List.append(
r.TH1F('MC_total_%i' % (i), "", varRange[0], varRange[1],
varRange[2]))
for j in range(varRange[0]):
dataValue = var_data[i].GetBinContent(j + 1)
dataError = var_data[i].GetBinError(j + 1)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList[6 * k + 1 + i].GetBinContent(j + 1)
if i == 0:
data_i.append(dataValue)
e.append(dataError)
MC_i.append(MCValue)
if i == 2:
data_r.append(dataValue)
MC_r.append(MCValue)
MC_List[i].SetBinContent(j + 1, MCValue)
if dataValue - MCValue > 0:
QCDHistList[i].SetBinContent(j + 1, dataValue - MCValue)
QCDHistList[i].SetBinError(j + 1, dataError)
MC_List[i].Sumw2()
for j in range(nBins):
dataValue4KS = var_data_4KS[i].GetBinContent(j + 1)
MCValue4KS = 0
for k in range(len(fileList)):
MCValue4KS += histList_4KS[6 * k + 1 + i].GetBinContent(j + 1)
if dataValue4KS - MCValue4KS > 0:
QCDHistList_4KS[i].SetBinContent(j + 1,
dataValue4KS - MCValue4KS)
ss_t = QCDHistList[0].Integral()
ss_l = QCDHistList[2].Integral()
os_l = QCDHistList[1].Integral()
os_l_data = var_data[1].Integral()
print "QCD in SS_T: %.4f" % ss_t
print "QCD in SS_L: %.4f" % ss_l
print "QCD in OS_L: %.4f" % os_l
print "Data in OS_L:%.4f" % os_l_data
print "SF: %.4f" % (ss_t / ss_l)
print "SF qcd/data: %.4f" % (os_l / os_l_data)
for i in range(4):
QCDHistList_withScale.append(
r.TH1F('QCD_withScale_%i' % (i), "", varRange[0], varRange[1],
varRange[2]))
for j in range(varRange[0]):
dataValue = var_data_4QCD[i].GetBinContent(j + 1)
dataError = var_data_4QCD[i].GetBinError(j + 1)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList_4QCD[6 * k + i].GetBinContent(j + 1)
if dataValue - MCValue > 0:
QCDHistList_withScale[i].SetBinContent(j + 1,
dataValue - MCValue)
QCDDiff = r.TH1F('QCD_diff', "", varRange[0], varRange[1], varRange[2])
QCDDiff2 = r.TH1F('QCD_diff2', "", varRange[0], varRange[1], varRange[2])
QCDDiff_R2T = r.TH1F('QCDDiff_R2T', "", varRange[0], varRange[1],
varRange[2])
# QCDDiff2.Sumw2()
fit1 = r.TF1("fit1", "[0]", varRange[1], varRange[2])
fit1.SetParName(0, 'scale')
fit1.FixParameter(0, 1.0)
QCDDiff.Fit('fit1', '0EM')
fit1.SetLineStyle(2)
fit1.SetLineColor(r.kRed)
fit2 = r.TF1("fit2", "[0]", varRange[1], varRange[2])
fit2.SetParName(0, 'scale')
fit2.FixParameter(0, 1.0)
QCDDiff2.Fit('fit2', '0EM')
fit2.SetLineStyle(2)
fit2.SetLineColor(r.kRed)
DrawSignal = False
if signalSelection != '':
var_signal = []
var_signal_4KS = []
for i in range(6):
var_signal.append(
r.TH1F('%s_%i' % (signalSelection, i), "", varRange[0],
varRange[1], varRange[2]))
var_signal_4KS.append(
r.TH1F('%s_%i_4KS' % (signalSelection, i), "", nBins,
varRange[1], varRange[2]))
signalDict = draw_cfg.signalDict
if signalSelection in signalDict:
fSignal = r.TFile(signalDict[signalSelection][0])
treeSignal = fSignal.Get('eventTree')
print 'Adding events from: %s ...' % (
signalDict[signalSelection][0])
for iEntry in range(treeSignal.GetEntries()):
treeSignal.GetEntry(iEntry)
select = passCut(treeSignal, bTag, region, thirdLeptonVeto)
if (not select) or (select > 6):
continue
var_signal[select - 1].Fill(
varsList.findVar(treeSignal, varName),
treeSignal.triggerEff)
var_signal_4KS[select - 1].Fill(
varsList.findVar(treeSignal, varName),
treeSignal.triggerEff)
initNEventsSignal = fSignal.Get('preselection')
for i in range(6):
var_signal[i].SetLineStyle(7)
var_signal[i].SetLineWidth(4)
var_signal[i].Scale(signalDict[signalSelection][1] * sigBoost *
Lumi / initNEventsSignal.GetBinContent(1))
if sigBoost != 1:
sum = var_signal[i].Integral(0,
var_signal[i].GetNbinsX() + 1)
legendHistos[i].append((
var_signal[i], '%sx%0.f (%.2f)' %
(signalSelection, sigBoost, var_signal[i].Integral())))
else:
legendHistos[i].append(
(var_signal[i], '%s (%.2f)' %
(signalSelection, var_signal[i].Integral())))
DrawSignal = True
else:
print '%s not supported, please use H260, H300 or H350' % signalSelection
if predict == 'True':
scale_SS2OS = fit1.GetParameter(0)
scale_er_SS2OS = fit1.GetParError(0)
scale_relaxed2Tight = fit2.GetParameter(0)
scale_er_relaxed2Tight = fit2.GetParError(0)
QCDHistList_withScale[0].Scale(scale_SS2OS)
QCDHistList_withScale[1].Scale(scale_relaxed2Tight)
QCDHistList_withScale[2].Scale(scale_SS2OS)
QCDHistList_withScale[3].Scale(scale_relaxed2Tight)
QCDHistList_withScale[3].SetFillColor(r.kSpring + 1)
QCDHistList_withScale[2].SetFillColor(r.kOrange - 4)
QCDHistList_withScale[0].SetLineColor(r.kSpring + 1)
QCDHistList_withScale[0].SetLineWidth(2)
QCDHistList_withScale[1].SetLineStyle(2)
QCDHistList_withScale[1].SetLineColor(r.kOrange - 4)
QCDHistList_withScale[1].SetLineWidth(2)
legendHistos[0].append(
(QCDHistList_withScale[0],
'From SS/Tight (%.0f)' % QCDHistList_withScale[0].Integral()))
legendHistos[0].append(
(QCDHistList_withScale[1],
'From OS/Relax (%.0f)' % QCDHistList_withScale[1].Integral()))
var_background[1].Add(QCDHistList_withScale[3])
var_background[2].Add(QCDHistList_withScale[2])
legendHistos[1].append(
(QCDHistList_withScale[3],
'From SS/Relax (%.0f)' % QCDHistList_withScale[3].Integral()))
legendHistos[2].append(
(QCDHistList_withScale[2],
'From SS/Relax (%.0f)' % QCDHistList_withScale[2].Integral()))
QCDHistList_withScale[1] = tool.addFakeTHStack(
QCDHistList_withScale[1], var_background[0])
QCDHistList_withScale[0] = tool.addFakeTHStack(
QCDHistList_withScale[0], var_background[0])
# var_data[1].Sumw2()
# MC_List[1].Sumw2()
for i in range(varRange[0]):
oldValue = var_data[1].GetBinContent(i + 1)
mcValue = MC_List[1].GetBinContent(i + 1)
if oldValue - mcValue > 0:
QCDDiff2.SetBinContent(i + 1, (oldValue - mcValue) / oldValue)
QCDDiff2.SetBinError(i + 1,
MC_List[1].GetBinError(i + 1) / oldValue)
print QCDDiff2.Integral()
QCDDiff = var_data[1].Clone()
QCDDiff_sub = QCDHistList_withScale[2].Clone() + MC_List[1].Clone()
QCDDiff.Divide(QCDDiff_sub)
QCDDiff_R2T = var_data[0].Clone()
QCDDiff_R2T_sub = QCDHistList_withScale[3].Clone() + MC_List[0].Clone()
QCDDiff_R2T.Divide(QCDDiff_R2T_sub)
legendPosition = (0.6, 0.7, 0.90, 0.88)
l = []
r.gROOT.SetBatch(True) # to suppress canvas pop-outs
if bTag == 'True':
titleName = '1 Medium 1 Loose b-tag'
fileName = 'bTag'
elif bTag == 'False':
titleName = ''
fileName = 'all'
elif bTag == '2M':
titleName = '2 Medium b-tags'
fileName = '2MbTag'
elif bTag == '1M':
titleName = '1 Medium b-tag'
fileName = '1MbTag'
KS1 = QCDHistList_4KS[0].KolmogorovTest(QCDHistList_4KS[2])
KS2 = QCDHistList_4KS[1].KolmogorovTest(QCDHistList_4KS[2])
ks_values = []
tmpHists = []
ks_values2 = []
tmpHists2 = []
nTimes = 10000
# for i in range(nTimes):
# tool.printProcessStatus(i, nTimes, processName = 'Making Sample Histograms')
# tmpHists.append(r.TH1F('tmpHist_%i' %(i),"", nBins, varRange[1], varRange[2]))
# tmpHists[i].FillRandom(QCDHistList_4KS[2], 100)
# ks_values.append(QCDHistList_4KS[0].KolmogorovTest(tmpHists[i]))
# tmpHists2.append(r.TH1F('tmpHist2_%i' %(i),"", nBins, varRange[1], varRange[2]))
# tmpHists2[i].FillRandom(QCDHistList_4KS[2], 100)
# ks_values2.append(QCDHistList_4KS[2].KolmogorovTest(tmpHists[i]))
print ''
print 'KS Test 1: %.3f' % KS1
print 'KS Test 2: %.3f' % KS2
psfile = '%s_%s_%s.pdf' % (varName, fileName, signalSelection)
c = r.TCanvas("c", "Test", 800, 900)
#ps = r.TPDF(psfile,112)
c.Divide(2, 3)
drawOpt = ''
QCDDiff.SetTitle(
'Data/Background OS Relaxed Events %s (%.1f fb^{-1}); %s; Data/Background'
% (titleName, Lumi, varName))
QCDDiff.SetMarkerStyle(8)
QCDDiff.SetMarkerSize(0.9)
QCDDiff.SetMaximum(4)
QCDDiff.SetMinimum(0)
QCDDiff_R2T.SetTitle(
'Data/Background OS Relaxed Events %s (%.1f fb^{-1}); %s; Data/Background'
% (titleName, Lumi, varName))
QCDDiff_R2T.SetMarkerStyle(8)
QCDDiff_R2T.SetMarkerSize(0.9)
QCDDiff_R2T.SetMaximum(4)
QCDDiff_R2T.SetMinimum(0)
for k in range(6):
c.cd(k + 1)
r.gPad.SetTicky()
if k > 1 and logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k + 1)
var_background[k].SetTitle(
'%s %s Events %s (%.1f fb^{-1}); %s; events / bin' %
(signSelection, iso, titleName, Lumi, varName))
if k < 2:
var_background[k].SetMaximum(250)
else:
var_background[k].SetMaximum(max)
var_background[k].SetMinimum(0.01)
var_background[k].Draw()
if predict == 'True' and k == 0:
QCDHistList_withScale[0].Draw('same')
QCDHistList_withScale[1].Draw('same')
if k != 0 and useData == 'True':
var_data[k - 1].Draw('PE same')
legendPosition = (0.63, 0.93 - 0.03 * len(legendHistos[k]), 0.93, 0.9)
l.append(
tool.setMyLegend(lPosition=legendPosition,
lHistList=legendHistos[k]))
if k == 1:
ksLegend1 = tool.setMyLegend(
(0.3, 0.8, 0.6, 0.9),
[(QCDHistList_withScale[3], 'KS Test: %.3f' % KS1)])
ksLegend1.Draw('same')
if k == 2:
ksLegend2 = tool.setMyLegend(
(0.3, 0.8, 0.6, 0.9),
[(QCDHistList_withScale[2], 'KS Test: %.3f' % KS2)])
ksLegend2.Draw('same')
l[k].Draw('same')
var_signal[k].Draw('same')
c.Update()
c.cd(5)
r.gPad.SetLogy(0)
QCDDiff.Draw('PE')
fit1.Draw('same')
c.cd(6)
r.gPad.SetLogy(0)
QCDDiff_R2T.Draw('PE')
fit2.Draw('same')
c.Update()
c.Print('%s(' % psfile)
c.cd(1)
r.gPad.SetLogy(0)
QCDDiff.Draw('PE')
fit1.Draw('same')
lFit1 = tool.setMyLegend(
(0.15, 0.7, 0.9, 0.85),
[(fit1, 'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %
(fit1.GetParameter(0), fit1.GetParError(0)))])
lFit1.Draw('same')
for k in range(3):
c.cd(k + 2)
if logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k + 2)
QCDHistList_withScale[k + 1].SetTitle(
'%s %s Data - MC Events %s (%.1f fb^{-1}); %s; events / bin' %
(signSelection, iso, titleName, Lumi, varName))
QCDHistList_withScale[k + 1].SetMarkerStyle(8)
QCDHistList_withScale[k + 1].SetMarkerSize(0.9)
QCDHistList_withScale[k + 1].SetMaximum(max)
QCDHistList_withScale[k + 1].SetMinimum(1)
QCDHistList_withScale[k + 1].Draw('PE')
c.Update()
c.Print('%s' % psfile)
c.cd(1)
r.gPad.SetLogy(0)
QCDDiff2.SetTitle(
'QCD/Data OS Relaxed Events %s (%.1f fb^{-1}); %s; QCD/Data' %
(titleName, Lumi, varName))
QCDDiff2.SetMarkerStyle(8)
QCDDiff2.SetMarkerSize(0.9)
QCDDiff2.SetMinimum(0.0)
QCDDiff2.SetMaximum(1.0)
QCDDiff2.Draw('PE')
fit2.Draw('same')
lFit2 = tool.setMyLegend(
(0.15, 0.7, 0.9, 0.85),
[(fit2, 'Scale between relaxed/tight in SS region: %.3f \pm %.3f' %
(fit2.GetParameter(0), fit2.GetParError(0)))])
lFit2.Draw('same')
c.Update()
c.Print('%s' % psfile)
# c.Clear()
# c.Divide(1,2)
# c.cd(1)
# graph = r.TH1F('graph', '', 20, 0, 1.0)
# graph2 = r.TH1F('graph2', '', 20, 0, 1.0)
# for i in range(nTimes):
# tool.printProcessStatus(i, nTimes, processName = 'Plotting Sample Histograms')
# graph.Fill(ks_values[i])
# graph2.Fill(ks_values2[i])
# print ''
# graph.SetTitle('KS Tests Between SS Tight and Relaxed; KS; ')
# graph.SetMarkerStyle(8)
# graph.Draw('PE')
# c.cd(2)
# graph2.SetTitle('KS Self-test in SS Relaxed; KS; ')
# graph2.SetMarkerStyle(8)
# graph2.Draw('PE')
# c.Update()
# c.Print('%s' %psfile)
# c.Clear()
#
# a1 = getAccuDist(QCDHistList_4KS[0], varRange[1], varRange[2], 'SS Tight')
# a2 = getAccuDist(QCDHistList_4KS[2], varRange[1], varRange[2], 'SS Relax')
# a1.SetLineColor(r.kRed)
# l3 = tool.setMyLegend((0.15, 0.75, 0.3, 0.85),[(a1,'SS Tight'),
# (a2,'SS Relax')])
#
# a1.SetTitle('Cumulative Distribution; %s;' %varName)
# a1.Draw()
# a2.Draw('same')
# l3.Draw('same')
c.Print('%s)' % psfile)
#ps.Close()
print "Plot saved at %s" % (psfile)
c.Close()
def KSTest(ifile, ofile, name):
f = r.TFile(ifile)
testTree = f.Get("TestTree")
trainTree = f.Get("TrainTree")
nBins = 20
nBins2 = 100000
xMin = -1.0
xMax = 1.0
BDT_Sig_Train = r.TH1F('BDT_Sig_Train', 'BDT_Sig_Train', nBins, xMin, xMax)
BDT_Sig_Test = r.TH1F('BDT_Sig_Test',
'Overtraining Check (%s)' % name[:name.find('_')],
nBins, xMin, xMax)
BDT_Bkg_Train = r.TH1F('BDT_Bkg_Train', 'BDT_Bkg_Train', nBins, xMin, xMax)
BDT_Bkg_Test = r.TH1F('BDT_Bkg_Test', 'BDT_Bkg_Test', nBins, xMin, xMax)
BDT_Sig_Train_4KS = r.TH1F('BDT_Sig_Train_4KS', 'BDT_Sig_Train_4KS',
nBins2, xMin, xMax)
BDT_Sig_Test_4KS = r.TH1F('BDT_Sig_Test_4KS', 'BDT_Sig_Test_4KS', nBins2,
xMin, xMax)
BDT_Bkg_Train_4KS = r.TH1F('BDT_Bkg_Train_4KS', 'BDT_Bkg_Train_4KS',
nBins2, xMin, xMax)
BDT_Bkg_Test_4KS = r.TH1F('BDT_Bkg_Test_4KS', 'BDT_Bkg_Test_4KS', nBins2,
xMin, xMax)
totalTest = testTree.GetEntries()
for i in range(totalTest):
testTree.GetEntry(i)
if testTree.className == "Signal":
BDT_Sig_Test.Fill(testTree.BDT, testTree.weight)
BDT_Sig_Test_4KS.Fill(testTree.BDT, testTree.weight)
else:
BDT_Bkg_Test.Fill(testTree.BDT, testTree.weight)
BDT_Bkg_Test_4KS.Fill(testTree.BDT, testTree.weight)
totalTrain = trainTree.GetEntries()
for i in range(totalTrain):
trainTree.GetEntry(i)
if trainTree.className == "Signal":
BDT_Sig_Train.Fill(trainTree.BDT, trainTree.weight)
BDT_Sig_Train_4KS.Fill(trainTree.BDT, trainTree.weight)
else:
BDT_Bkg_Train.Fill(trainTree.BDT, trainTree.weight)
BDT_Bkg_Train_4KS.Fill(trainTree.BDT, trainTree.weight)
BDT_Bkg_Train.Sumw2()
BDT_Sig_Train.Sumw2()
sigKS = BDT_Sig_Test_4KS.KolmogorovTest(BDT_Sig_Train_4KS)
bkgKS = BDT_Bkg_Test_4KS.KolmogorovTest(BDT_Bkg_Train_4KS)
print 'signal: %.4f' % sigKS
print 'background: %.4f' % bkgKS
BDT_Bkg_Train.Scale(1 / BDT_Bkg_Train.Integral())
BDT_Bkg_Train.SetMarkerColor(r.kRed)
BDT_Bkg_Train.SetMarkerStyle(21)
BDT_Bkg_Test.SetLineColor(r.kRed)
BDT_Bkg_Test.SetFillColor(r.kRed)
BDT_Bkg_Test.SetFillStyle(3354)
BDT_Bkg_Test.Scale(1 / BDT_Bkg_Test.Integral())
BDT_Sig_Train.Scale(1 / BDT_Sig_Train.Integral())
BDT_Sig_Train.SetMarkerColor(r.kBlue)
BDT_Sig_Train.SetMarkerStyle(21)
BDT_Sig_Test.SetLineColor(r.kBlue)
BDT_Sig_Test.SetFillColor(r.kBlue)
BDT_Sig_Test.SetFillStyle(3001)
BDT_Sig_Test.Scale(1 / BDT_Sig_Test.Integral())
legendHistos1 = []
legendHistos1.append((BDT_Bkg_Test, 'bkg test'))
legendHistos1.append((BDT_Bkg_Train, 'bkg train'))
# legendHistos1.append((BDT_Bkg_Train, 'KS: %0.3f' %bkgKS))
legendHistos2 = []
legendHistos2.append((BDT_Sig_Test, 'sig test'))
legendHistos2.append((BDT_Sig_Train, 'sig train'))
# legendHistos2.append((BDT_Sig_Train, 'KS: %0.3f' %sigKS))
l1 = tool.setMyLegend(lPosition=(0.2, 0.67, 0.5, 0.82),
lHistList=legendHistos1)
l2 = tool.setMyLegend(lPosition=(0.6, 0.67, 0.9, 0.82),
lHistList=legendHistos2)
r.gStyle.SetOptStat(0)
c = r.TCanvas("c", "Test", 800, 600)
BDT_Sig_Test.Draw()
BDT_Sig_Test.GetXaxis().SetTitle("BDT")
BDT_Sig_Test.SetMaximum(0.5)
BDT_Sig_Train.Draw('sameE1P')
BDT_Bkg_Test.Draw('same')
BDT_Bkg_Train.Draw('sameE1P')
l1.Draw('same')
l2.Draw('same')
c.Print('%s.pdf' % ofile)
#
# for i in range(1,histRange2[0]+1):
# tmpHist5_eta.SetBinContent(i, frac1)
# tmpHist6_eta.SetBinContent(i, frac2)
c = r.TCanvas("c","Test", 800, 600)
ps = r.TPDF('%sPt_%s.pdf' %(psfile, title),112)
c.Divide(2,2)
c.cd(1)
r.gPad.SetLogy()
tmpHist3.SetTitle('%s tau1 pt; pt1; events' %(title))
tmpHist3.Draw()
tmpHist1.SetLineColor(r.kRed)
tmpHist1.Draw("same")
l1 = tool.setMyLegend(lPosition=legendPosition, lHistList=histList1)
l1.Draw("same")
c.Update()
c.cd(2)
r.gPad.SetLogy()
tmpHist4.SetTitle('%s tau2 pt; pt2; events' %(title))
tmpHist4.Draw("")
tmpHist2.SetLineColor(r.kRed)
tmpHist2.Draw("same")
l2 = tool.setMyLegend(lPosition=legendPosition, lHistList=histList2)
l2.Draw("same")
c.Update()
c.cd(3)
tmpHist5.SetTitle('tau1 same charge rate; pt1; SS/(SS+OS)')
tmpHist5.SetMaximum(1.0)
tmpHist5.SetMinimum(0.0)
def run(varName,
bins,
unit,
cat='em',
eID='MVANonTrig80_2',
fit=False,
region='barrel',
triggerLeg='e'):
dir = '2016_trigStudy'
dataFunc = setFunc('dataFunc')
append_name = ''
files_mc = [ # ("SUSY", '/nfs_scratch/zmao/13TeV_samples_25ns_newSplitting_noChargeMatch/SYNC_SUSY_HToTauTau_M-160_%s_%s.root' %(cat, append_name), r.kRed)
]
hist_mc_all = {}
hist_mc_pass = {}
file_data = '/user_data/zmao/%s/%s/data_Electron_all_SYNC_em_noIso.root' % (
dir, eID
) #'/nfs_scratch/zmao/supy-output//slice/ee//data_Electron_events.root'# %(cat, append_name)
hist_data_all = r.TH1F('hist_data_all', '', len(bins) - 1, bins)
hist_data_pass = r.TH1F('hist_data_pass', '', len(bins) - 1, bins)
for iName, iSample, iColor in files_mc:
if iName not in hist_mc_all.keys():
hist_mc_all[iName] = r.TH1F("hist_%s_all" % iName, "",
len(bins) - 1, bins)
hist_mc_pass[iName] = r.TH1F("hist_%s_pass" % iName, "",
len(bins) - 1, bins)
loop_one_sample(iSample, varName, hist_mc_all[iName],
hist_mc_pass[iName], cat, False, region, triggerLeg)
if file_data != '':
loop_one_sample(file_data, varName, hist_data_all, hist_data_pass, cat,
True, region, triggerLeg)
g_mc = []
histList = []
funcs = []
i = 0
for iKey in hist_mc_all.keys():
g_mc.append(r.TGraphAsymmErrors())
g_mc[i].BayesDivide(hist_mc_pass[iKey], hist_mc_all[iKey])
g_mc[i].SetMarkerStyle(8)
g_mc[i].SetMarkerSize(0.9)
color = getColor(iKey)
g_mc[i].SetMarkerColor(color)
g_mc[i].SetLineColor(color)
if iKey == 'ttbar' and fit:
funcs.append(setFunc('%sFunc' % iKey))
funcs[i].SetLineColor(color)
funcs[i].SetLineStyle(2)
g_mc[i].Fit('%sFunc' % iKey, "R")
histList.append(
(g_mc[i], "%s, plateau: %.3f +- %.3f" %
(iKey, funcs[i].GetParameter(0), funcs[i].GetParError(0)),
'ep'))
else:
funcs.append(None)
if len(bins) == 3:
x = r.Double(0.0)
y1 = r.Double(0.0)
y2 = r.Double(0.0)
g_mc[i].GetPoint(0, x, y1)
g_mc[i].GetPoint(1, x, y2)
histList.append((
g_mc[i], "%s (%.3f +/- %.3f, %.3f +/- %.3f)" %
(iKey, y1, g_mc[i].GetErrorY(0), y2, g_mc[i].GetErrorY(1)),
'ep'))
else:
histList.append((g_mc[i], iKey, 'ep'))
i += 1
g_data = r.TGraphAsymmErrors()
g_data.BayesDivide(hist_data_pass, hist_data_all)
dataFunc.SetLineColor(r.kBlack)
dataFunc.SetLineStyle(2)
fakeHist = r.TH1F("fakeHist", "", 1, 0, 1)
fakeHist.SetLineColor(r.kWhite)
if fit:
g_data.Fit("dataFunc", "R")
histList.append(
(g_data, 'Observed, plateau: %.3f +/- %.3f' %
(dataFunc.GetParameter(0), dataFunc.GetParError(0)), "ep"))
histList.append(
(fakeHist, ' halfpoint: %.1f +/- %.1f' %
(dataFunc.GetParameter(1), dataFunc.GetParError(1)), "l"))
histList.append(
(fakeHist, ' slope: %.3f +/- %.3f' %
(dataFunc.GetParameter(2), dataFunc.GetParError(2)), "l"))
else:
if len(bins) == 3:
x = r.Double(0.0)
data_y1 = r.Double(0.0)
data_y2 = r.Double(0.0)
g_data.GetPoint(0, x, data_y1)
g_data.GetPoint(1, x, data_y2)
histList.append(
(g_data, "Observed (%.3f +/- %.3f, %.3f +/- %.3f)" %
(data_y1, g_data.GetErrorY(0), data_y2, g_data.GetErrorY(1)),
'ep'))
else:
histList.append((g_data, 'Observed', 'ep'))
fakeHist = r.TH1D("fakeHist", "", 1, 0, 1)
fakeHist.SetLineColor(r.kWhite)
g_data.SetMarkerStyle(8)
g_data.SetMarkerSize(0.9)
g_data.SetMarkerColor(r.kBlack)
length = 800
# length = 1800
c = r.TCanvas("c", "Test", length, 800)
null = r.TH2F("null", "", len(bins) - 1, bins, 1, 0, 1.1)
null.SetMaximum(1.2)
if triggerLeg == 'e':
psfile = 'eleTrigTurnOnCurve_%s_%s_%s_%s.pdf' % (varName, cat, eID,
region)
null.SetTitle("electron trigger turn-on curve; %s; Effeciency" % unit)
else:
psfile = 'muonTrigTurnOnCurve_%s_%s_%s_%s.pdf' % (varName, cat, eID,
region)
null.SetTitle("muon trigger turn-on curve; %s; Effeciency" % unit)
null.SetStats(False)
if len(bins) == 3:
leg = tool.setMyLegend((0.1, 0.4 - 0.06 * 4, 0.95, 0.4), histList)
else:
leg = tool.setMyLegend((0.45, 0.4 - 0.06 * 4, 0.9, 0.4), histList)
r.gPad.SetTicky()
r.gPad.SetTickx()
r.gPad.SetTicky()
null.Draw()
for i in range(len(hist_mc_all.keys())):
g_mc[i].Draw('same PE')
g_data.Draw('same PE')
leg.Draw('same')
c.Print('%s' % psfile)
c.Close()
def main():
file_13TeV = '/nfs_scratch/zmao/test/DYJetsToLL_all.root'#'/nfs_scratch/zmao/test/TTJets_all.root'#
file_8TeV = '/nfs_scratch/zmao/samples_Iso/tauESOn/normal/dy.root'#'/nfs_scratch/zmao/samples_Iso/tauESOff/normal/TT.root'#
vars2compare = [("tau1Pt", [50, 30, 230]),
# ("tau2Pt", [50, 30, 230]),
# ("tau1Eta", [25, -2.5, 2.5]),
# ("tau2Eta", [25, -2.5, 2.5]),
# ("tau1Iso", [40, 0, 2.]),
# ("tau2Iso", [40, 0, 2.]),
# ("J1Pt", [50, 30, 230]),
# ("J2Pt", [50, 30, 230]),
# ("J1Eta", [40, -4, 4]),
# ("J2Eta", [40, -4, 4]),
# ("J1CSVbTag", [50, 0, 1]),
# ("J2CSVbTag", [50, 0, 1]),
#
# ("CSVJ1Pt", [50, 30, 230]),
# ("CSVJ2Pt", [50, 30, 230]),
# ("CSVJ1Eta", [40, -4, 4]),
# ("CSVJ2Eta", [40, -4, 4]),
# ("CSVJ1", [50, 0, 1]),
# ("CSVJ2", [50, 0, 1]),
#
# ("svMass", [35, 0, 350]),
# ("met", [25, 0, 200]),
# ("mJJ", [50, 0, 500])
]
histDicts = buildHistDict(vars2compare)
psfile = 'out.pdf'
c = r.TCanvas("c","Test", 800, 600)
counter = 0
for iVar, Range in vars2compare:
unc13TeV = readFile(file = file_13TeV, hist = histDicts[iVar]['13TeV'], varName = iVar, which = '13TeV')
unc8TeV = readFile(file = file_8TeV, hist = histDicts[iVar]['8TeV'], varName = iVar, which = '8TeV')
#normalization
# histDicts[iVar]['13TeV'].Scale(1.0/histDicts[iVar]['13TeV'].Integral(0, histDicts[iVar]['13TeV'].GetNbinsX()+1))
# histDicts[iVar]['8TeV'].Scale(1.0/histDicts[iVar]['8TeV'].Integral(0, histDicts[iVar]['8TeV'].GetNbinsX()+1))
counter += 1
c.Clear()
histDicts[iVar]['13TeV'].SetTitle("13 TeV vs 8 TeV; %s; a.u" %iVar)
histDicts[iVar]['8TeV'].SetTitle("13 TeV vs 8 TeV; %s; a.u" %iVar)
histDicts[iVar]['8TeV'].SetLineColor(r.kRed)
if histDicts[iVar]['13TeV'].GetMaximum() >= histDicts[iVar]['8TeV'].GetMaximum():
histDicts[iVar]['13TeV'].Draw()
histDicts[iVar]['8TeV'].Draw('same')
else:
histDicts[iVar]['8TeV'].Draw()
histDicts[iVar]['13TeV'].Draw('same')
if counter == 1:
l = tool.setMyLegend(lPosition = [0.7, 0.85, 0.9, 0.7], lHistList = [(histDicts[iVar]['13TeV'], '13TeV'), (histDicts[iVar]['8TeV'], '8TeV')])
l.Draw('same')
c.Print('%s(' %psfile)
if counter == len(vars2compare):
l.Draw('same')
c.Print('%s)' %psfile)
else:
l.Draw('same')
c.Print('%s' %psfile)
print "13TeV Integral: %.2f +- %.2f (fb)" %(histDicts[iVar]['13TeV'].Integral(0, histDicts[iVar]['13TeV'].GetNbinsX()+1), unc13TeV)
print "8TeV Integral : %.2f +- %.2f (fb)" %(histDicts[iVar]['8TeV'].Integral(0, histDicts[iVar]['8TeV'].GetNbinsX()+1), unc8TeV)
c.Close()
def loop_one_sample(iSample, iLocation, iFS):
print 'combining sample [%s] for LUT' % (iSample)
if 'data' in iSample:
return 0
if ('H2hh' in iSample) or ('ggH' in iSample) or ('Zprime' in iSample):
return 0
#pdfWeights
pdfHists = []
nWeights = 101
initSumPDF = []
for i in range(nWeights):
pdfHists.append(r.TH1F("pdf_%i" % i, "", len(bins) - 1, bins))
pdfHists[i].Sumw2()
iFile = r.TFile(iLocation)
iTree = iFile.Get("Ntuple")
nEntries = iTree.GetEntries()
iTree.SetBranchStatus("*", 1)
iTree.SetBranchStatus("sampleName", 0)
eventCount = iFile.Get('eventCount')
eventCountWeighted = iFile.Get('eventCountWeighted')
if options.signal:
for i in range(100):
initSumPDF.append(
int((iFile.Get('eventCountWeightedPDF_%i' %
i)).GetBinContent(1)))
hist_highPUWeight = r.TH1F("hist_highPUWeight", "", 50, 0.5, 1.5)
hist_lowPUWeight = r.TH1F("hist_lowPUWeight", "", 50, 0.5, 1.5)
yieldEstimator_OS = 0.0
yieldEstimator_SS = 0.0
fillcounter = 0
met = lvClass()
nBins = len(plots_cfg.list)
# nEntries = int(nEntries/3.)
for iEntry in range(nEntries):
iTree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'looping over file %s' % (iSample), iEntry - 1)
met.SetCoordinates(iTree.pfMetEt, 0.0, iTree.pfMetPhi, 0)
l1.SetCoordinates(iTree.pt_1, iTree.eta_1, iTree.phi_1, iTree.m_1)
l2.SetCoordinates(iTree.pt_2, iTree.eta_2, iTree.phi_2, iTree.m_2)
if not plots.passCut(iTree, iFS, False, l1, l2, met, ''):
continue
if options.method != 'SS' and iTree.q_1 == iTree.q_2:
continue
if plots.regionSelection(iTree, iFS, "control", options.method,
plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
sampleName = 'MC' + controlRegionName
elif plots.regionSelection(iTree, iFS, "signal", options.method,
plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
sampleName = iSample
else:
continue
triggerEff = iTree.trigweight_1 * iTree.trigweight_2
xs = iTree.xs
if 'WJets' in iSample:
xs = xs * plots_cfg.WJetsScanRange[0]
if "Zprime" in iSample:
xs = plots.getZPrimeXS(iSample[7:])
if eventCount:
initEvents = int(eventCount.GetBinContent(1))
else:
initEvents = int(iTree.initEvents)
if eventCountWeighted:
initSumWeights = int(eventCountWeighted.GetBinContent(1))
else:
initSumWeights = int(iTree.initWeightedEvents)
m_effective = (l1 + l2 + met).mass()
#overflow and underflow
if m_effective < plots_cfg.list[0]:
m_effective = (plots_cfg.list[0] + plots_cfg.list[1]) / 2.0
if m_effective > plots_cfg.list[nBins - 1]:
m_effective = (plots_cfg.list[nBins - 1] +
plots_cfg.list[nBins - 2]) / 2.0
if options.PUWeight:
PUWeight = cutSampleTools.getPUWeight(iTree.nTruePU)
else:
PUWeight = 1.0
genEventWeight = iTree.genEventWeight
if options.noNegGenEvts and genEventWeight < 0:
continue
if options.noGenEvtWeight:
genEventWeight = 1.0
if (sampleName == 'MC' + controlRegionName) and ("WJets" in iSample):
totalNormalWeights = triggerEff * xs * (
plots.lumi) * genEventWeight * PUWeight
pdfHists[0].Fill(m_effective, totalNormalWeights)
for i in range(1, nWeights):
pdfID = getPDFWeightID(i, iSample, iTree.pdfID)
pdfHists[i].Fill(
m_effective,
totalNormalWeights * iTree.pdfWeight.at(pdfID))
elif (controlRegionName not in sampleName) and ("WJets"
not in iSample):
totalNormalWeights = triggerEff * xs * (
plots.lumi) * genEventWeight * PUWeight
absMax = 0
for i in range(1, nWeights):
pdfID = getPDFWeightID(i, iSample, iTree.pdfID)
if abs(iTree.pdfWeight.at(pdfID)) > absMax:
absMax = iTree.pdfWeight.at(pdfID)
if absMax > 2.5:
continue
pdfHists[0].Fill(m_effective, totalNormalWeights)
for i in range(1, nWeights):
pdfID = getPDFWeightID(i, iSample, iTree.pdfID)
pdfHists[i].Fill(
m_effective,
totalNormalWeights * iTree.pdfWeight.at(pdfID))
#hist_highPUWeight.Fill(PUWeight)
if PUWeight > 1.0:
hist_highPUWeight.Fill(iTree.pdfWeight.at(pdfID),
genEventWeight * PUWeight)
else:
hist_lowPUWeight.Fill(iTree.pdfWeight.at(pdfID),
genEventWeight * PUWeight)
c = r.TCanvas("c", "Test", 600, 600)
c.Range(0, 0, 1, 1)
c.SetFillColor(0)
# r.gPad.SetLogy()
hist_highPUWeight.SetLineColor(r.kBlue)
hist_lowPUWeight.SetLineColor(r.kRed)
# hist_lowPUWeight.Scale(1./hist_lowPUWeight.Integral(0, hist_lowPUWeight.GetNbinsX()+1))
hist_highPUWeight.Draw()
hist_lowPUWeight.Draw("same")
position = (0.15, 0.85 - 0.05 * 2, 0.47, 0.85)
histList = []
histList.append((hist_highPUWeight, "high PU-weight events", 'l'))
histList.append((hist_lowPUWeight, "low PU-weight events", 'l'))
legends = tool.setMyLegend(position, histList)
legends.Draw("same")
c.Print('pdfSys.pdf')
return getVariations(pdfHists, initSumPDF, initSumWeights)
def getHistos(varName, signalSelection, logY, sigBoost, nbins, useData, max, rangeMin, rangeMax, location, bTag, predict, predictPtBin):
r.gStyle.SetOptStat(0)
preFix = 'ClassApp_both_ClassApp_QCD_ClassApp_EWK_TMVARegApp_'
fileList = [('ZZ', preFix + 'ZZ_eff_all.root', 2500, 5),
('tt_full_lep',preFix + 'tt_eff_all.root', 26197.5, r.kRed-7),
('tt_semi_lep',preFix + 'tt_semi_eff_all.root', 109281, r.kAzure+7),
#('DYJetsToLL', 'TMVARegApp_DYJetsToLL_eff_all.root', 3504000, r.kGreen-7),
('DY2JetsToLL', preFix + 'DY2JetsToLL_eff_all.root', 181000, r.kGreen-7),
('DY3JetsToLL', preFix + 'DY3JetsToLL_eff_all.root', 51100, r.kGreen-7),
('W1JetsToLNu', preFix + 'W1JetsToLNu_eff_all.root', 5400000, r.kMagenta-9),
('W2JetsToLNu', preFix + 'W2JetsToLNu_eff_all.root', 1750000, r.kMagenta-9),
('W3JetsToLNu', preFix + 'W3JetsToLNu_eff_all.root', 519000, r.kMagenta-9)]
histList = []
QCDHistList = []
varRange = [nbins, rangeMin, rangeMax]
Lumi = 19.0
initNEventsList = []
legendHistos = []
var_background = []
fitList = []
for i in range(6):
fitList.append(r.TF1("Fit%i" %i,"[0]+[1]*x", rangeMin, rangeMax))
fitList[i].SetLineColor(r.kRed)
fitList[i].SetLineStyle(2)
fitList[i].SetParameter(0,0)
fitList[i].SetParameter(1,0)
tmpFile = []
tmpTree = []
var_data = []
for i in range(5):
var_data.append(r.TH1F('data_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
dataName = preFix + 'dataTotal_all.root'
fData = r.TFile(dataName)
treeData = fData.Get('eventTree')
print 'Adding events from: %s ...' %dataName
for iEntry in range(treeData.GetEntries()):
treeData.GetEntry(iEntry)
select = passCut(treeData, bTag)
if (select == 0) or (select == 1):
continue
var_data[select-2].Fill(varsList.findVar(treeData, varName))
legendHistos.append([])
for j in range(5):
var_data[j].SetMarkerStyle(8)
var_data[j].SetMarkerSize(0.9)
legendHistos.append([])
legendHistos[j+1].append((var_data[j], 'observed (%.0f)' %var_data[j].Integral()))
for i in range(len(fileList)):
for j in range(6):
histList.append(r.TH1F('%s_%i' %(fileList[i][0],j),fileList[i][0], varRange[0], varRange[1], varRange[2]))
print 'Adding events from: %s ...' %(fileList[i][1])
tmpFile.append(r.TFile(fileList[i][1]))
tmpTree.append(tmpFile[i].Get('eventTree'))
for iEntry in range(tmpTree[i].GetEntries()):
tmpTree[i].GetEntry(iEntry)
select = passCut(tmpTree[i], bTag)
if not select:
continue
histList[6*i+select-1].Fill(varsList.findVar(tmpTree[i], varName), tmpTree[i].triggerEff)
initNEventsList.append(tmpFile[i].Get('preselection'))
for j in range(6):
var_background.append(r.THStack())
histList[6*i+j].SetFillColor(fileList[i][3])
histList[6*i+j].Scale(fileList[i][2]*Lumi/initNEventsList[i].GetBinContent(1))
var_background[j].Add(histList[6*i+j])
legendHistos[j].append((histList[6*i+j], '%s (%.2f)' %(fileList[i][0], histList[6*i+j].Integral())))
data_i = []
MC_i = []
data_r = []
MC_r = []
e = []
for i in range(5):
QCDHistList.append(r.TH1F('QCD_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
for j in range(varRange[0]):
dataValue = var_data[i].GetBinContent(j+1)
dataError = var_data[i].GetBinError(j+1)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList[6*k+1+i].GetBinContent(j+1)
if i == 0:
data_i.append(dataValue)
e.append(dataError)
MC_i.append(MCValue)
if i == 2:
data_r.append(dataValue)
MC_r.append(MCValue)
if dataValue - MCValue > 0:
QCDHistList[i].SetBinContent(j+1, dataValue - MCValue)
QCDHistList[i].SetBinError(j+1, dataError)
QCDDiff = r.TH1F('QCD_diff',"", varRange[0], varRange[1], varRange[2])
QCDDiff2 = r.TH1F('QCD_diff2',"", varRange[0], varRange[1], varRange[2])
for j in range(varRange[0]):
SS_tight_Events = QCDHistList[0].GetBinContent(j+1)
OS_Events = QCDHistList[1].GetBinContent(j+1)
SS_Events = QCDHistList[2].GetBinContent(j+1)
SS_tight_Error = QCDHistList[0].GetBinError(j+1)
OS_Error = QCDHistList[1].GetBinError(j+1)
SS_Error = QCDHistList[2].GetBinError(j+1)
if SS_Events != 0:
QCDDiff.SetBinContent(j+1, OS_Events/SS_Events)
QCDDiff.SetBinError(j+1, OS_Events/SS_Events*getCombinedError(OS_Events, OS_Error, SS_Events, SS_Error))
QCDDiff2.SetBinContent(j+1, SS_tight_Events/SS_Events)
QCDDiff2.SetBinError(j+1, SS_tight_Events/SS_Events*getCombinedError(SS_tight_Events, SS_tight_Error, SS_Events, SS_Error))
fit1 = r.TF1("fit1","[0]", varRange[1],varRange[2])
fit1.SetParName(0,'scale')
QCDDiff.Fit('fit1', '0E')
fit1.SetLineStyle(2)
fit1.SetLineColor(r.kRed)
fit2 = r.TF1("fit2","[0]", varRange[1],varRange[2])
fit2.SetParName(0,'scale')
QCDDiff2.Fit('fit2', '0E')
fit2.SetLineStyle(2)
fit2.SetLineColor(r.kRed)
ptBins = [varRange[1], 100, 200, varRange[2]]
if predictPtBin == 'True':
fit_l = r.TF1("fit_l","[0]", ptBins[0], ptBins[1])
fit_l.SetLineColor(r.kRed)
fit_m = r.TF1("fit_m","[0]", ptBins[1], ptBins[2])
fit_m.SetLineColor(r.kBlue)
fit_h = r.TF1("fit_h","[0]", ptBins[2], ptBins[3])
fit_h.SetLineColor(r.kGreen)
QCDDiff.Fit('fit_l', '0ER')
QCDDiff.Fit('fit_m', '0ER')
QCDDiff.Fit('fit_h', '0ER')
fit2_l = r.TF1("fit2_l","[0]", ptBins[0], ptBins[1])
fit2_m = r.TF1("fit2_m","[0]", ptBins[1], ptBins[2])
fit2_h = r.TF1("fit2_h","[0]", ptBins[2], ptBins[3])
fit2_l.SetLineColor(r.kRed)
fit2_m.SetLineColor(r.kBlue)
fit2_h.SetLineColor(r.kGreen)
fit2_l.SetLineStyle(2)
fit2_m.SetLineStyle(2)
fit2_h.SetLineStyle(2)
fit_l.SetLineStyle(2)
fit_m.SetLineStyle(2)
fit_h.SetLineStyle(2)
QCDDiff2.Fit('fit2_l', '0ER')
QCDDiff2.Fit('fit2_m', '0ER')
QCDDiff2.Fit('fit2_h', '0ER')
DrawSignal = False
if signalSelection != '':
var_signal = []
for i in range(6):
var_signal.append(r.TH1F('%s_%i' %(signalSelection,i),"", varRange[0], varRange[1], varRange[2]))
signalDict = {'H260': (preFix + 'H2hh260_all.root', 14.76),
'H300': (preFix + 'H2hh300_all.root', 15.9),
'H350': (preFix + 'H2hh350_all.root', 8.57)}
if signalSelection in signalDict:
fSignal = r.TFile(signalDict[signalSelection][0])
treeSignal = fSignal.Get('eventTree')
print 'Adding events from: %s ...' %(signalDict[signalSelection][0])
for iEntry in range(treeSignal.GetEntries()):
treeSignal.GetEntry(iEntry)
select = passCut(treeSignal, bTag)
if not select:
continue
var_signal[select-1].Fill(varsList.findVar(treeSignal, varName), treeSignal.triggerEff)
initNEventsSignal = fSignal.Get('preselection')
for i in range(6):
var_signal[i].SetLineStyle(7)
var_signal[i].SetLineWidth(4)
var_signal[i].Scale(signalDict[signalSelection][1]*sigBoost*Lumi/initNEventsSignal.GetBinContent(1))
if sigBoost != 1:
legendHistos[i].append((var_signal[i], '%sx%0.f (%.2f)' %(signalSelection, sigBoost, var_signal[i].Integral())))
else:
legendHistos[i].append((var_signal[i], '%s (%.2f)' %(signalSelection, var_signal[i].Integral())))
DrawSignal = True
else:
print '%s not supported, please use H260, H300 or H350' %signalSelection
if predict == 'True':
relaxed2Tight = r.TH1F('relaxed2Tight','', varRange[0], varRange[1], varRange[2])
SS2OS = r.TH1F('SS2OS','', varRange[0], varRange[1], varRange[2])
relaxed2Signal = r.TH1F('relaxed2Signal','', varRange[0], varRange[1], varRange[2])
SS2OS_signal = r.TH1F('SS2OS_signal','', varRange[0], varRange[1], varRange[2])
fullHistTranslate = r.TH1F('fullHistTranslate','', varRange[0], varRange[1], varRange[2])
scale_SS2OS = fit1.GetParameter(0)
scale_er_SS2OS = fit1.GetParError(0)
scale_relaxed2Tight = fit2.GetParameter(0)
scale_er_relaxed2Tight = fit2.GetParError(0)
if predictPtBin == 'True':
scale_SS2OS_l = fit_l.GetParameter(0)
scale_er_SS2OS_l = fit_l.GetParError(0)
scale_relaxed2Tight_l = fit2_l.GetParameter(0)
scale_er_relaxed2Tight_l = fit2_l.GetParError(0)
scale_SS2OS_m = fit_m.GetParameter(0)
scale_er_SS2OS_m = fit_m.GetParError(0)
scale_relaxed2Tight_m = fit2_m.GetParameter(0)
scale_er_relaxed2Tight_m = fit2_m.GetParError(0)
scale_SS2OS_h = fit_h.GetParameter(0)
scale_er_SS2OS_h = fit_h.GetParError(0)
scale_relaxed2Tight_h = fit2_h.GetParameter(0)
scale_er_relaxed2Tight_h = fit2_h.GetParError(0)
for i in range(varRange[0]):
SS_tight_Events = QCDHistList[0].GetBinContent(i+1)
SS_tight_Error = QCDHistList[0].GetBinError(i+1)
OS_relaxed_Events = QCDHistList[1].GetBinContent(i+1)
OS_relaxed_Error = QCDHistList[1].GetBinError(i+1)
SS_Events = QCDHistList[2].GetBinContent(i+1)
SS_Error = QCDHistList[2].GetBinError(i+1)
scale_1 = scale_relaxed2Tight
scale_2 = scale_SS2OS
if predictPtBin == 'True':
ptValue = QCDHistList[0].GetBinCenter(i+1)
if ptValue <= ptBins[1]:
scale_1 = scale_relaxed2Tight_l
scale_2 = scale_SS2OS_l
elif ptValue <= ptBins[2]:
scale_1 = scale_relaxed2Tight_m
scale_2 = scale_SS2OS_m
else:
scale_1 = scale_relaxed2Tight_h
scale_2 = scale_SS2OS_h
relaxed2Tight.SetBinContent(i+1, scale_1*SS_Events)
relaxed2Signal.SetBinContent(i+1, scale_1*OS_relaxed_Events)
SS2OS.SetBinContent(i+1, scale_2*SS_Events)
SS2OS_signal.SetBinContent(i+1, scale_2*SS_tight_Events)
if SS_Events > 0:
fullHistTranslate.SetBinContent(i+1, SS_tight_Events*OS_relaxed_Events/SS_Events)
# relaxed2Tight.SetBinError(i+1, getCombinedError(scale_relaxed2Tight, scale_er_relaxed2Tight, SS_Events, SS_Error))
# SS2OS.SetBinError(i+1, getCombinedError(scale_SS2OS, scale_er_SS2OS, SS_Events, SS_Error))
# relaxed2Tight.SetLineColor(r.kSpring+8)
relaxed2Tight.SetFillColor(r.kSpring+1)
# SS2OS.SetLineColor(r.kOrange)
SS2OS.SetFillColor(r.kOrange-4)
SS2OS_signal.SetLineColor(r.kSpring+1)
SS2OS_signal.SetLineWidth(2)
relaxed2Signal.SetLineStyle(2)
relaxed2Signal.SetLineColor(r.kOrange-4)
relaxed2Signal.SetLineWidth(2)
fullHistTranslate.SetLineColor(r.kPink+1)
fullHistTranslate.SetLineWidth(2)
fullHistTranslate.SetLineStyle(2)
legendHistos[0].append((SS2OS_signal, 'From SS/Tight (%.0f)' %SS2OS_signal.Integral()))
legendHistos[0].append((relaxed2Signal, 'From OS/Relax (%.0f)' %relaxed2Signal.Integral()))
#legendHistos[0].append((fullHistTranslate, 'full hist (%.0f)' %fullHistTranslate.Integral()))
var_background[1].Add(relaxed2Tight)
var_background[2].Add(SS2OS)
legendHistos[1].append((relaxed2Tight, 'From SS/Relax (%.0f)' %relaxed2Tight.Integral()))
legendHistos[2].append((SS2OS, 'From SS/Relax (%.0f)' %SS2OS.Integral()))
relaxed2Signal = tool.addFakeTHStack(relaxed2Signal,var_background[0])
SS2OS_signal = tool.addFakeTHStack(SS2OS_signal,var_background[0])
fullHistTranslate = tool.addFakeTHStack(fullHistTranslate,var_background[0])
legendPosition = (0.6, 0.7, 0.90, 0.88)
l = []
r.gROOT.SetBatch(True) # to suppress canvas pop-outs
if bTag == 'True':
titleName = '1 Medium 1 Loose b-tag'
fileName = 'bTag'
elif bTag == 'False':
titleName = ''
fileName = 'all'
elif bTag == 'Revert':
titleName = 'Revert b-tag'
fileName = 'revert_bTag'
psfile = '%s/%s_%s_IsoRatio.pdf' %(location, varName, fileName)
c = r.TCanvas("c","Test", 800, 1000)
#ps = r.TPDF(psfile,112)
c.Divide(2,3)
drawOpt = ''
for k in range(6):
c.cd(k+1)
if logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k+1)
var_background[k].SetTitle('%s %s Events %s (%.1f fb^{-1}); %s; events / bin' %(signSelection, iso, titleName, Lumi,varName))
var_background[k].SetMaximum(max)
var_background[k].SetMinimum(0.01)
var_background[k].Draw()
if predict == 'True' and k == 0:
SS2OS_signal.Draw('same')
relaxed2Signal.Draw('same')
#fullHistTranslate.Draw('same')
if k != 0 and useData == 'True':
var_data[k-1].Draw('PE same')
legendPosition = (0.63, 0.93 - 0.035*len(legendHistos[k]), 0.93, 0.9)
l.append(tool.setMyLegend(lPosition=legendPosition, lHistList=legendHistos[k]))
l[k].Draw('same')
var_signal[k].Draw('same')
c.Update()
c.Print('%s(' %psfile)
c.Clear()
c.Divide(2,3)
c.cd(1)
r.gPad.SetLogy(0)
QCDDiff.SetTitle('OS/SS MultiJet Relaxed Events %s (%.1f fb^{-1}); %s; OS/SS' %(titleName, Lumi,varName))
QCDDiff.SetMarkerStyle(8)
QCDDiff.SetMarkerSize(0.9)
QCDDiff.SetMaximum(2)
QCDDiff.SetMinimum(0)
QCDDiff.Draw('PE')
if predictPtBin == 'True':
lFit1 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fit_l,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(fit_l.GetParameter(0), fit_l.GetParError(0))),
(fit_m,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(fit_m.GetParameter(0), fit_m.GetParError(0))),
(fit_h,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(fit_h.GetParameter(0), fit_h.GetParError(0)))])
fit_l.Draw('same')
fit_m.Draw('same')
fit_h.Draw('same')
else:
fit1.Draw('same')
lFit1 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fit1,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(fit1.GetParameter(0), fit1.GetParError(0)))])
lFit1.Draw('same')
for k in range(5):
c.cd(k+2)
if logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k+2)
QCDHistList[k].SetTitle('%s %s Data - MC Events %s (%.1f fb^{-1}); %s; events / bin' %(signSelection, iso, titleName, Lumi,varName))
QCDHistList[k].SetMarkerStyle(8)
QCDHistList[k].SetMarkerSize(0.9)
QCDHistList[k].SetMaximum(max)
QCDHistList[k].SetMinimum(1)
QCDHistList[k].Draw('PE')
c.Update()
c.Print('%s' %psfile)
c.Clear()
c.Divide(2,3)
c.cd(1)
r.gPad.SetLogy(0)
QCDDiff2.SetTitle('tight/relaxed MultiJet SS Events %s (%.1f fb^{-1}); %s; tight/relaxed' %(titleName, Lumi,varName))
QCDDiff2.SetMarkerStyle(8)
QCDDiff2.SetMarkerSize(0.9)
QCDDiff2.SetMinimum(0)
QCDDiff2.SetMaximum(1)
QCDDiff2.Draw('PE')
if predictPtBin == 'True':
lFit2 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fit2_l,'Scale between relaxed/tight in SS region: %.3f \pm %.3f' %(fit2_l.GetParameter(0), fit2_l.GetParError(0))),
(fit2_m,'Scale between relaxed/tight in SS region: %.3f \pm %.3f' %(fit2_m.GetParameter(0), fit2_m.GetParError(0))),
(fit2_h,'Scale between relaxed/tight in SS region: %.3f \pm %.3f' %(fit2_h.GetParameter(0), fit2_h.GetParError(0)))])
fit2_l.Draw('same')
fit2_m.Draw('same')
fit2_h.Draw('same')
else:
fit2.Draw('same')
lFit2 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fit2,'Scale between relaxed/tight in SS region: %.3f \pm %.3f' %(fit2.GetParameter(0), fit2.GetParError(0)))])
lFit2.Draw('same')
c.Print('%s' %psfile)
c.Clear()
c.Divide(2,3)
lineInfoList = []
i = 0
tmpQCDHist2 = QCDHistList[2].Clone()
tmpQCDHist2.Divide(QCDHistList[4])
tmpQCDHist1 = QCDHistList[1].Clone()
tmpQCDHist1.Divide(QCDHistList[3])
c.cd(1)
tmpQCDHist1.SetTitle('1.5<iso<4/4<iso<10 OS QCD Events %s (%.1f fb^{-1}); %s; relaxed1/relaxed2' %(titleName, Lumi,varName))
tmpQCDHist1.GetXaxis().SetTitleOffset(1.2)
tmpQCDHist1.SetMaximum(0.3)
tmpQCDHist1.SetMinimum(0)
tmpQCDHist1.Draw()
tmpQCDHist1.Fit('Fit0', '0ER')
l1 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fitList[0],'Ratio between relaxed1/relaxed2: y = %.2f\pm%.2f + (%.4f\pm%.4f)x' %(fitList[0].GetParameter(0), fitList[0].GetParError(0), fitList[0].GetParameter(1), fitList[0].GetParError(1)))])
l1.Draw('same')
fitList[0].Draw('same')
c.cd(2)
tmpQCDHist2.SetTitle('1.5<iso<4/4<iso<10 SS QCD Events %s (%.1f fb^{-1}); %s; relaxed1/relaxed2' %(titleName, Lumi,varName))
tmpQCDHist2.SetMaximum(0.3)
tmpQCDHist2.GetXaxis().SetTitleOffset(1.2)
tmpQCDHist2.SetMinimum(0)
tmpQCDHist2.Draw()
tmpQCDHist2.Fit('Fit1', '0ER')
l2 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fitList[1],'Ratio between relaxed1/relaxed2: y = %.2f\pm%.2f + (%.4f\pm%.4f)x' %(fitList[1].GetParameter(0), fitList[1].GetParError(0), fitList[1].GetParameter(1), fitList[1].GetParError(1)))])
l2.Draw('same')
fitList[1].Draw('same')
QCDHistList[1].SetTitle('OS Relaxed1 / SS Relaxed1 QCD Events %s (%.1f fb^{-1}); %s; OS/SS' %(titleName, Lumi,varName))
QCDHistList[3].SetTitle('OS Relaxed2 / SS Relaxed2 QCD Events %s (%.1f fb^{-1}); %s; OS/SS' %(titleName, Lumi,varName))
QCDHistList[2].SetTitle('SS Relaxed1 / SS Tight QCD Events %s (%.1f fb^{-1}); %s; Relaxed1/SS' %(titleName, Lumi,varName))
QCDHistList[4].SetTitle('SS Relaxed2 / SS Tight QCD Events %s (%.1f fb^{-1}); %s; Relaxed2/SS' %(titleName, Lumi,varName))
for k in [1,3]:
c.cd(k+2)
QCDHistList[k].Divide(QCDHistList[k+1])
QCDHistList[k].SetMarkerStyle(8)
QCDHistList[k].SetMaximum(3)
QCDHistList[k].SetMinimum(0)
QCDHistList[k].SetMarkerSize(0.9)
QCDHistList[k].Draw('PE')
QCDHistList[k].Fit('Fit%i' %(k+1), '0ER')
lineInfoList.append(tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fitList[k+1],'Ratio between OS/SS: y = %.3f\pm%.3f + (%.4f\pm%.4f)x' %(fitList[k+1].GetParameter(0), fitList[k+1].GetParError(0), fitList[k+1].GetParameter(1), fitList[k+1].GetParError(1)))]))
lineInfoList[i].Draw('same')
fitList[k+1].Draw('same')
i += 1
QCDHistList[2].SetMaximum(9)
QCDHistList[4].SetMaximum(80)
for k in [2,4]:
c.cd(k+2)
QCDHistList[k].Divide(QCDHistList[0])
QCDHistList[k].SetMarkerStyle(8)
QCDHistList[k].SetMinimum(0)
QCDHistList[k].SetMarkerSize(0.9)
QCDHistList[k].Draw('PE')
QCDHistList[k].Fit('Fit%i' %(k+1), '0ER')
lineInfoList.append(tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fitList[k+1],'Ratio between relaxed%i/iso: y = %.3f\pm%.3f + (%.4f\pm%.4f)x' %(k/2,fitList[k+1].GetParameter(0), fitList[k+1].GetParError(0), fitList[k+1].GetParameter(1), fitList[k+1].GetParError(1)))]))
lineInfoList[i].Draw('same')
fitList[k+1].Draw('same')
i += 1
c.Print('%s)' %psfile)
#ps.Close()
print "Plot saved at %s" %(psfile)
def getHistos(signalSelection, sigBoost, location, bTag):
r.gStyle.SetOptStat(0)
preFix = 'ClassApp_both_ClassApp_QCD_ClassApp_EWK_TMVARegApp_'
fileList = [('ZZ', preFix + 'ZZ_eff_all.root', 2500, 5, r.kYellow),
('tt_full_lep',preFix+'tt_eff_all.root', 26197.5, r.kRed-7),
('tt_semi_lep',preFix+'tt_semi_eff_all.root', 109281, r.kAzure+7),
# ('DYJetsToLL', 'DYJetsToLL_eff_all.root', 3504000, r.kGreen-7),
('DY2JetsToLL', preFix+'DY2JetsToLL_eff_all.root', 181000, r.kGreen-7),
('DY3JetsToLL', preFix+'DY3JetsToLL_eff_all.root', 51100, r.kGreen-7),
('W1JetsToLNu', preFix+'W1JetsToLNu_eff_all.root', 5400000, r.kMagenta-9),
('W2JetsToLNu', preFix+'W2JetsToLNu_eff_all.root', 1750000, r.kMagenta-9),
('W3JetsToLNu', preFix+'W3JetsToLNu_eff_all.root', 519000, r.kMagenta-9)]
histList = []
QCDHistList = []
varRange = [20, -1, 1, 20, -1, 1]
Lumi = 19.0
initNEventsList = []
legendHistos = []
tmpFile = []
tmpTree = []
var_data = []
signal = r.TH2F('signal',"", varRange[0], varRange[1], varRange[2], varRange[0], varRange[1], varRange[2])
EWK = r.TH2F('EWK',"", varRange[0], varRange[1], varRange[2], varRange[0], varRange[1], varRange[2])
QCD = r.TH2F('QCD',"", varRange[0], varRange[1], varRange[2], varRange[0], varRange[1], varRange[2])
data = r.TH2F('data',"", varRange[0], varRange[1], varRange[2], varRange[0], varRange[1], varRange[2])
MC = r.TH2F('MC',"", varRange[0], varRange[1], varRange[2], varRange[0], varRange[1], varRange[2])
background = r.TH2F('background',"", varRange[0], varRange[1], varRange[2], varRange[0], varRange[1], varRange[2])
dataName = preFix + 'dataTotal_all.root'
fData = r.TFile(dataName)
treeData = fData.Get('eventTree')
print 'Adding events from: %s ...' %dataName
for iEntry in range(treeData.GetEntries()):
treeData.GetEntry(iEntry)
select = passCut(treeData, bTag)
if select == 3:
data.Fill(varsList.findVar(treeData, 'BDT_EWK'), varsList.findVar(treeData, 'BDT_QCD'))
for i in range(len(fileList)):
print 'Adding events from: %s ...' %(fileList[i][1])
tmpFile.append(r.TFile(fileList[i][1]))
tmpTree.append(tmpFile[i].Get('eventTree'))
initNEventsList.append(tmpFile[i].Get('preselection'))
tmpScale = fileList[i][2]*Lumi/initNEventsList[i].GetBinContent(1)
for iEntry in range(tmpTree[i].GetEntries()):
tmpTree[i].GetEntry(iEntry)
select = passCut(tmpTree[i], bTag)
if select == 1:
background.Fill(varsList.findVar(tmpTree[i], 'BDT_EWK'), varsList.findVar(tmpTree[i], 'BDT_QCD'), tmpTree[i].triggerEff*tmpScale)
EWK.Fill(varsList.findVar(tmpTree[i], 'BDT_EWK'), varsList.findVar(tmpTree[i], 'BDT_QCD'), tmpTree[i].triggerEff*tmpScale)
if select == 3:
MC.Fill(varsList.findVar(tmpTree[i], 'BDT_EWK'), varsList.findVar(tmpTree[i], 'BDT_QCD'), tmpTree[i].triggerEff*tmpScale)
if signalSelection != '':
var_signal = []
for i in range(4):
var_signal.append(r.TH1F('%s_%i' %(signalSelection,i),"", varRange[0], varRange[1], varRange[2]))
signalDict = {'H260': (preFix+'H2hh260_all.root', 14.76),
'H300': (preFix+'H2hh300_all.root', 15.9),
'H350': (preFix+'H2hh350_all.root', 8.57)}
if signalSelection in signalDict:
fSignal = r.TFile(signalDict[signalSelection][0])
treeSignal = fSignal.Get('eventTree')
print 'Adding events from: %s ...' %(signalDict[signalSelection][0])
initNEventsSignal = fSignal.Get('preselection')
scaleSignal = signalDict[signalSelection][1]*sigBoost*Lumi/initNEventsSignal.GetBinContent(1)
for iEntry in range(treeSignal.GetEntries()):
treeSignal.GetEntry(iEntry)
select = passCut(treeSignal, bTag)
if select == 1:
signal.Fill(varsList.findVar(treeSignal, 'BDT_EWK'), varsList.findVar(treeSignal, 'BDT_QCD'), treeSignal.triggerEff)
background.Fill(varsList.findVar(treeSignal, 'BDT_EWK'), varsList.findVar(treeSignal, 'BDT_QCD'), treeSignal.triggerEff*scaleSignal)
signal.Scale(scaleSignal)
else:
print '%s not supported, please use H260, H300 or H350' %signalSelection
background = estBkg2(MC, data, background, 0.025)
QCD = estBkg2(MC, data, QCD, 0.025)
legendHistos.append((background, 'background (%.2f)' %background.Integral()))
r.gROOT.SetBatch(True) # to suppress canvas pop-outs
if bTag == 'True':
titleName = '1 Medium 1 Loose b-tag'
fileName = 'bTag'
elif bTag == 'False':
titleName = ''
fileName = 'all'
elif bTag == 'Revert':
titleName = 'Revert b-tag'
fileName = 'revert_bTag'
psfile = '%s/BDT2D_%s.pdf' %(location, fileName)
c = r.TCanvas("c","Test", 800, 600)
#ps = r.TPDF(psfile,112)
drawOpt = ''
c.Divide(2,2)
c.cd(1)
signSelection, iso = conditions(1)
EWK.SetTitle('%s %s Events %s (%.1f fb^{-1}); BDT_EWK;BDT_QCD; events / bin' %(signSelection, iso, titleName, Lumi))
EWK.Draw("CONTZ")
legendPosition = (0.5, 0.75, 0.75, 0.9)
l = tool.setMyLegend(lPosition=legendPosition, lHistList=[(EWK, "EWK (%.2f)" %EWK.Integral())])
l.Draw('same')
c.cd(2)
QCD.SetTitle('%s %s Events %s (%.1f fb^{-1}); BDT_EWK;BDT_QCD; events / bin' %(signSelection, iso, titleName, Lumi))
QCD.Draw("CONTZ")
l2 = tool.setMyLegend(lPosition=legendPosition, lHistList=[(QCD, "QCD (%.2f)" %QCD.Integral())])
l2.Draw('same')
c.cd(3)
signal.SetTitle('%s %s Events %s (%.1f fb^{-1}); BDT_EWK;BDT_QCD; events / bin' %(signSelection, iso, titleName, Lumi))
signal.Draw("CONTZ")
l3 = tool.setMyLegend(lPosition=legendPosition, lHistList=[(signal, "signal x%.0f (%.2f)" %(sigBoost,signal.Integral()))])
l3.Draw('same')
c.cd(4)
background.SetTitle('%s %s Events %s (%.1f fb^{-1}); BDT_EWK;BDT_QCD; events / bin' %(signSelection, iso, titleName, Lumi))
background.Draw("CONTZ")
l4 = tool.setMyLegend(lPosition=legendPosition, lHistList=[(background, "total (%.2f)" %background.Integral())])
l4.Draw('total')
c.Update()
c.Print('%s' %psfile)
#ps.Close()
print "Plot saved at %s" %(psfile)
if jetsList[0][1].pt() < 20 or abs(jetsList[0][1].eta()) >= 2.4:
continue
if jetsList[1][1].pt() < 20 or abs(jetsList[1][1].eta()) >= 2.4:
continue
if jetsList[0][0] > CSVCut1 and jetsList[1][0] > CSVCut2:
iHist.Fill((jetsList[0][1]+jetsList[1][1]).mass())
print ''
intList = tool.unitNormHists(HistNameList)
legendPosition = (0.58, 0.7, 0.88, 0.80)
legendHistos = [(h_mjj_h,"H -> hh -> #tau^{+}#tau^{-} b#bar{b}"),#(%.2f events/20 fb^{-1})" %(intList[0]*xsList[0]*20/sigPreSelection)),
(h_mjj_tt,"t#bar{t} -> b#bar{b} ll"), #(%.1f events/20 fb^{-1})" %(intList[1]*xsList[1]*20/ttPreSelection)),
(h_mjj_zz,"ZZ + jets -> 2q 2l")]#(%.1f events/20 fb^{-1})" %(intList[2]*xsList[2]*20/ZZPreSelection))]
h_mjj_zz.SetTitle("%s; mJJCSVSort; Unit Normalized" % (Title))
h_mjj_zz.GetYaxis().SetTitleOffset(1.2)
c = r.TCanvas("c","Test", 800, 600)
r.gPad.SetTickx()
r.gPad.SetTicky()
psfile = os.environ['PYPATH']+'/Plots/'+psfile
ps = r.TPostScript(psfile,112)
tool.setDrawHists(sigHist=h_mjj_h, ttHist=h_mjj_tt, ZZHist=h_mjj_zz)
l = tool.setMyLegend(lPosition=legendPosition, lHistList=legendHistos)
l.Draw("same")
ps.Close()
print "Plot saved at %s" %(psfile)
tHist1.SetLineColor(r.kAzure+9)
tHist1.SetFillColor(r.kAzure+9)
tHist1.SetFillStyle(3003)
tHist2 = r.TH1F()
tHist2.SetLineColor(2)
tHist2.SetLineWidth(2)
tHist2.SetLineStyle(2)
legendPosition = (0.5, 0.75, 0.9, 0.85)
legendHistos = [(tHist1,"%s with %d events" %(options.file1, tree1.GetEntries())),
(tHist2,"%s with %d events" %(options.file2, tree2.GetEntries()))]
listBranch = tree1.GetListOfBranches()
oFileName = "diff.root"
ofile = r.TFile(oFileName, "RECREATE")
nBranches = listBranch.GetEntries()
for i in range(nBranches):
iBranchName = listBranch.At(i).GetName()
tmpCanvas = r.TCanvas(iBranchName,"c",800,600)
tree1.Draw("%s" %(iBranchName), "")
tree2.Draw("%s" %(iBranchName), "", "same")
l = tool.setMyLegend(lPosition=legendPosition, lHistList=legendHistos)
l.Draw("same")
tmpCanvas.Write()
tool.printProcessStatus(i+1, nBranches, 'Saving to file %s.root' %(oFileName))
print ' -- saved %d branches' %(nBranches)
tmpHist4 = iFile_1.Get("InputVariables_Id/%s__Background_Id" %(varsList[i]))
tool.unitNormHists([tmpHist1, tmpHist2, tmpHist3, tmpHist4])
tool.setDrawHists4(tmpHist3, tmpHist4, tmpHist1, tmpHist2)
tmpHist1.SetTitle('%s; %s; Unit Normalized' %(varsList[i], varsList[i]))
tmpHist2.SetTitle('%s; %s; Unit Normalized' %(varsList[i], varsList[i]))
tmpHist3.SetTitle('%s; %s; Unit Normalized' %(varsList[i], varsList[i]))
tmpHist4.SetTitle('%s; %s; Unit Normalized' %(varsList[i], varsList[i]))
tmpHist1.GetXaxis().SetRangeUser(varRanges[varsList[i]][0],varRanges[varsList[i]][1])
tmpHist2.GetXaxis().SetRangeUser(varRanges[varsList[i]][0],varRanges[varsList[i]][1])
tmpHist3.GetXaxis().SetRangeUser(varRanges[varsList[i]][0],varRanges[varsList[i]][1])
tmpHist4.GetXaxis().SetRangeUser(varRanges[varsList[i]][0],varRanges[varsList[i]][1])
legendList.append(tool.setMyLegend(lPosition=legendPosition, lHistList = [(tmpHist1, '%s_pt45: Sig' %(trainMass)),
(tmpHist2, '%s_pt45: Bkg' %(trainMass)),
(tmpHist3, '%s_old: Sig' %(trainMass)),
(tmpHist4, '%s_old: Bkg' %(trainMass))]))
legendList[i].Draw("same")
c.Update()
ps.Close()
c2 = r.TCanvas("c2","Test", 1000, 600)
ps = r.TPDF('%s_2.pdf' %(psfile[0: psfile.rfind('.')]),112)
c2.Divide(3,2)
for i in range(6, len(varsList)):
c2.cd(i%6 +1)
tmpHist1 = iFile_2.Get("InputVariables_Id/%s__Signal_Id" %(varsList[i]))
tmpHist2 = iFile_2.Get("InputVariables_Id/%s__Background_Id" %(varsList[i]))
tmpHist3 = iFile_1.Get("InputVariables_Id/%s__Signal_Id" %(varsList[i]))
tmpHist4 = iFile_1.Get("InputVariables_Id/%s__Background_Id" %(varsList[i]))
def getHistos(varName, signalSelection, logY, sigBoost, nbins, useData, max, rangeMin, rangeMax, location, bTag, predict, predictPtBin, region):
r.gStyle.SetOptStat(0)
preFix = 'ClassApp_both_ClassApp_QCD_ClassApp_EWK_TMVARegApp_'
fileList = [('ZZ', preFix + 'ZZ_eff_all.root', 2500, 5),
('WZJetsTo2L2Q', preFix + 'WZJetsTo2L2Q_eff_all.root', 2207, 5),
('W1JetsToLNu', preFix + 'W1JetsToLNu_eff2_all.root', 5400000, r.kMagenta-9),
('W2JetsToLNu', preFix + 'W2JetsToLNu_eff2_all.root', 1750000, r.kMagenta-9),
('W3JetsToLNu', preFix + 'W3JetsToLNu_eff2_all.root', 519000, r.kMagenta-9),
#('DYJetsToLL', 'TMVARegApp_DYJetsToLL_eff_all.root', 3504000, r.kGreen-7),
('DY1JetsToLL', preFix + 'DY1JetsToLL_eff2_all.root', 561000, r.kGreen-7),
('DY2JetsToLL', preFix + 'DY2JetsToLL_eff2_all.root', 181000, r.kGreen-7),
('DY3JetsToLL', preFix + 'DY3JetsToLL_eff2_all.root', 51100, r.kGreen-7),
('tt_full_lep',preFix + 'tt_eff_all.root', 26197.5, r.kRed-7),
('tt_semi_lep',preFix + 'tt_semi_eff_all.root', 109281, r.kAzure+7)]
histList = []
histList_4QCD = []
QCDHistList = []
QCDHistList_4KS = []
QCDHistList_withScale = []
varRange = [nbins, rangeMin, rangeMax]
nBins = 10000
Lumi = 19.0
initNEventsList = []
legendHistos = []
var_background = []
scaleMCPt = 1.0
tmpFile = []
tmpTree = []
var_data_4KS = []
var_data = []
var_data_4QCD = []
histList_4KS = []
for i in range(5):
var_data.append(r.TH1F('data_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
var_data_4KS.append(r.TH1F('data_4KS_%i' %(i),"", nBins, varRange[1], varRange[2]))
if i < 5:
var_data_4QCD.append(r.TH1F('data_4QCD_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
dataName = preFix + 'dataTotal_all.root'
fData = r.TFile(dataName)
treeData = fData.Get('eventTree')
print 'Adding events from: %s ...' %dataName
for iEntry in range(treeData.GetEntries()):
treeData.GetEntry(iEntry)
select = passCut(treeData, bTag, region)
if (select == 0) or (select == 1) or (select > 6):
continue
var_data[select-2].Fill(varsList.findVar(treeData, varName))
var_data_4KS[select-2].Fill(varsList.findVar(treeData, varName))
if select == 2:
var_data_4QCD[0].Fill(varsList.findVar(treeData, varName), findPtScale(treeData.pt1.at(0),treeData.pt2.at(0), 'left', region))
elif select == 3:
var_data_4QCD[1].Fill(varsList.findVar(treeData, varName), findPtScale(treeData.pt1.at(0),treeData.pt2.at(0), 'up', region))
elif select == 4:
var_data_4QCD[2].Fill(varsList.findVar(treeData, varName), findPtScale(treeData.pt1.at(0),treeData.pt2.at(0), 'left', region))
var_data_4QCD[3].Fill(varsList.findVar(treeData, varName), findPtScale(treeData.pt1.at(0),treeData.pt2.at(0), 'up', region))
legendHistos.append([])
for j in range(5):
var_data[j].SetMarkerStyle(8)
var_data[j].SetMarkerSize(0.9)
legendHistos.append([])
legendHistos[j+1].append((var_data[j], 'observed (%.0f)' %var_data[j].Integral()))
for i in range(len(fileList)):
for j in range(6):
histList_4KS.append(r.TH1F('%s_%i_KS' %(fileList[i][0],j),fileList[i][0], nBins, varRange[1], varRange[2]))
histList.append(r.TH1F('%s_%i' %(fileList[i][0],j),fileList[i][0], varRange[0], varRange[1], varRange[2]))
histList_4QCD.append(r.TH1F('%s_%i_2' %(fileList[i][0],j),fileList[i][0], varRange[0], varRange[1], varRange[2]))
print 'Adding events from: %s ...' %(fileList[i][1])
tmpFile.append(r.TFile(fileList[i][1]))
tmpTree.append(tmpFile[i].Get('eventTree'))
for iEntry in range(tmpTree[i].GetEntries()):
tmpTree[i].GetEntry(iEntry)
select = passCut(tmpTree[i], bTag, region)
if (not select) or (select > 6):
continue
histList[6*i+select-1].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff)
histList_4KS[6*i+select-1].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff)
if select == 2:
histList_4QCD[6*i].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff*findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0), 'left', region))
elif select == 3:
histList_4QCD[6*i+1].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff*findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0), 'up', region))
elif select == 4:
histList_4QCD[6*i+2].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff*findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0), 'left', region))
histList_4QCD[6*i+3].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff*findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0), 'up', region))
initNEventsList.append(tmpFile[i].Get('preselection'))
for j in range(6):
var_background.append(r.THStack())
histList[6*i+j].SetFillColor(fileList[i][3])
histList[6*i+j].Scale(fileList[i][2]*Lumi/initNEventsList[i].GetBinContent(1))
histList_4QCD[6*i+j].Scale(fileList[i][2]*Lumi/initNEventsList[i].GetBinContent(1))
histList_4KS[6*i+j].Scale(fileList[i][2]*Lumi/initNEventsList[i].GetBinContent(1))
var_background[j].Add(histList[6*i+j])
legendHistos[j].append((histList[6*i+j], '%s (%.2f)' %(fileList[i][0], histList[6*i+j].Integral())))
data_i = []
MC_i = []
data_r = []
MC_r = []
e = []
for i in range(3):
QCDHistList.append(r.TH1F('QCD_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
QCDHistList_4KS.append(r.TH1F('QCD_%i_KS' %(i),"", nBins, varRange[1], varRange[2]))
for j in range(varRange[0]):
dataValue = var_data[i].GetBinContent(j+1)
dataError = var_data[i].GetBinError(j+1)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList[6*k+1+i].GetBinContent(j+1)
if i == 0:
data_i.append(dataValue)
e.append(dataError)
MC_i.append(MCValue)
if i == 2:
data_r.append(dataValue)
MC_r.append(MCValue)
if dataValue - MCValue > 0:
QCDHistList[i].SetBinContent(j+1, dataValue - MCValue)
QCDHistList[i].SetBinError(j+1, dataError)
for j in range(nBins):
dataValue4KS = var_data_4KS[i].GetBinContent(j+1)
MCValue4KS = 0
for k in range(len(fileList)):
MCValue4KS += histList_4KS[6*k+1+i].GetBinContent(j+1)
if dataValue4KS - MCValue4KS > 0:
QCDHistList_4KS[i].SetBinContent(j+1, dataValue4KS - MCValue4KS)
for i in range(4):
QCDHistList_withScale.append(r.TH1F('QCD_withScale_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
for j in range(varRange[0]):
dataValue = var_data_4QCD[i].GetBinContent(j+1)
dataError = var_data_4QCD[i].GetBinError(j+1)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList_4QCD[6*k+i].GetBinContent(j+1)
if dataValue - MCValue > 0:
QCDHistList_withScale[i].SetBinContent(j+1, dataValue - MCValue)
QCDDiff = r.TH1F('QCD_diff',"", varRange[0], varRange[1], varRange[2])
QCDDiff2 = r.TH1F('QCD_diff2',"", varRange[0], varRange[1], varRange[2])
QCDDiff = QCDHistList[1].Clone()
QCDDiff.Divide(QCDHistList_withScale[2])
QCDDiff.Sumw2()
QCDDiff2 = QCDHistList[0].Clone()
QCDDiff2.Divide(QCDHistList_withScale[3])
QCDDiff2.Sumw2()
fit1 = r.TF1("fit1","[0]", varRange[1],varRange[2])
fit1.SetParName(0,'scale')
fit1.FixParameter(0,1.0)
QCDDiff.Fit('fit1', '0EM')
fit1.SetLineStyle(2)
fit1.SetLineColor(r.kRed)
fit2 = r.TF1("fit2","[0]", varRange[1],varRange[2])
fit2.SetParName(0,'scale')
fit2.FixParameter(0,1.0)
QCDDiff2.Fit('fit2', '0EM')
fit2.SetLineStyle(2)
fit2.SetLineColor(r.kRed)
DrawSignal = False
if signalSelection != '':
var_signal = []
var_signal_4KS = []
for i in range(6):
var_signal.append(r.TH1F('%s_%i' %(signalSelection,i),"", varRange[0], varRange[1], varRange[2]))
var_signal_4KS.append(r.TH1F('%s_%i_4KS' %(signalSelection,i),"", nBins, varRange[1], varRange[2]))
signalDict = {'H260': (preFix + 'H2hh260_all.root', 14.76),
'H300': (preFix + 'H2hh300_all.root', 15.9),
'H350': (preFix + 'H2hh350_all.root', 8.57)}
if signalSelection in signalDict:
fSignal = r.TFile(signalDict[signalSelection][0])
treeSignal = fSignal.Get('eventTree')
print 'Adding events from: %s ...' %(signalDict[signalSelection][0])
for iEntry in range(treeSignal.GetEntries()):
treeSignal.GetEntry(iEntry)
select = passCut(treeSignal, bTag, region)
if (not select) or (select > 6):
continue
var_signal[select-1].Fill(varsList.findVar(treeSignal, varName), treeSignal.triggerEff)
var_signal_4KS[select-1].Fill(varsList.findVar(treeSignal, varName), treeSignal.triggerEff)
initNEventsSignal = fSignal.Get('preselection')
for i in range(6):
var_signal[i].SetLineStyle(7)
var_signal[i].SetLineWidth(4)
var_signal[i].Scale(signalDict[signalSelection][1]*sigBoost*Lumi/initNEventsSignal.GetBinContent(1))
if sigBoost != 1:
legendHistos[i].append((var_signal[i], '%sx%0.f (%.2f)' %(signalSelection, sigBoost, var_signal[i].Integral())))
else:
legendHistos[i].append((var_signal[i], '%s (%.2f)' %(signalSelection, var_signal[i].Integral())))
DrawSignal = True
else:
print '%s not supported, please use H260, H300 or H350' %signalSelection
if predict == 'True':
scale_SS2OS = fit1.GetParameter(0)
scale_er_SS2OS = fit1.GetParError(0)
scale_relaxed2Tight = fit2.GetParameter(0)
scale_er_relaxed2Tight = fit2.GetParError(0)
QCDHistList_withScale[0].Scale(scale_SS2OS)
QCDHistList_withScale[1].Scale(scale_relaxed2Tight)
QCDHistList_withScale[2].Scale(scale_SS2OS)
QCDHistList_withScale[3].Scale(scale_relaxed2Tight)
QCDHistList_withScale[3].SetFillColor(r.kSpring+1)
QCDHistList_withScale[2].SetFillColor(r.kOrange-4)
QCDHistList_withScale[0].SetLineColor(r.kSpring+1)
QCDHistList_withScale[0].SetLineWidth(2)
QCDHistList_withScale[1].SetLineStyle(2)
QCDHistList_withScale[1].SetLineColor(r.kOrange-4)
QCDHistList_withScale[1].SetLineWidth(2)
legendHistos[0].append((QCDHistList_withScale[0], 'From SS/Tight (%.0f)' %QCDHistList_withScale[0].Integral()))
legendHistos[0].append((QCDHistList_withScale[1], 'From OS/Relax (%.0f)' %QCDHistList_withScale[1].Integral()))
var_background[1].Add(QCDHistList_withScale[3])
var_background[2].Add(QCDHistList_withScale[2])
legendHistos[1].append((QCDHistList_withScale[3], 'From SS/Relax (%.0f)' %QCDHistList_withScale[3].Integral()))
legendHistos[2].append((QCDHistList_withScale[2], 'From SS/Relax (%.0f)' %QCDHistList_withScale[2].Integral()))
QCDHistList_withScale[1] = tool.addFakeTHStack(QCDHistList_withScale[1],var_background[0])
QCDHistList_withScale[0] = tool.addFakeTHStack(QCDHistList_withScale[0],var_background[0])
legendPosition = (0.6, 0.7, 0.90, 0.88)
l = []
r.gROOT.SetBatch(True) # to suppress canvas pop-outs
if bTag == 'True':
titleName = '1 Medium 1 Loose b-tag'
fileName = 'bTag'
elif bTag == 'False':
titleName = ''
fileName = 'all'
elif bTag == 'Revert':
titleName = 'Revert b-tag'
fileName = 'revert_bTag'
elif bTag == 'Loose':
titleName = 'Loose b-tag'
fileName = 'loose_bTag'
KS1 = QCDHistList_4KS[0].KolmogorovTest(QCDHistList_4KS[2])
KS2 = QCDHistList_4KS[1].KolmogorovTest(QCDHistList_4KS[2])
print 'KS Test 1: %.3f' %KS1
print 'KS Test 2: %.3f' %KS2
psfile = '%s/%s_%s_all.pdf' %(location, varName, fileName)
c = r.TCanvas("c","Test", 800, 900)
#ps = r.TPDF(psfile,112)
c.Divide(2,3)
drawOpt = ''
for k in range(6):
c.cd(k+1)
r.gPad.SetTicky()
if logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k+1)
var_background[k].SetTitle('%s %s Events %s (%.1f fb^{-1}); %s; events / bin' %(signSelection, iso, titleName, Lumi,varName))
var_background[k].SetMaximum(max)
var_background[k].SetMinimum(0.01)
var_background[k].Draw()
if predict == 'True' and k == 0:
QCDHistList_withScale[0].Draw('same')
QCDHistList_withScale[1].Draw('same')
if k != 0 and useData == 'True':
var_data[k-1].Draw('PE same')
legendPosition = (0.63, 0.93 - 0.03*len(legendHistos[k]), 0.93, 0.9)
l.append(tool.setMyLegend(lPosition=legendPosition, lHistList=legendHistos[k]))
if k == 1:
ksLegend1 = tool.setMyLegend((0.3, 0.8, 0.6, 0.9), [(QCDHistList_withScale[3], 'KS Test: %.3f' %KS1)])
ksLegend1.Draw('same')
if k == 2:
ksLegend2 = tool.setMyLegend((0.3, 0.8, 0.6, 0.9), [(QCDHistList_withScale[2], 'KS Test: %.3f' %KS2)])
ksLegend2.Draw('same')
l[k].Draw('same')
var_signal[k].Draw('same')
c.Update()
c.Print('%s(' %psfile)
c.cd(1)
r.gPad.SetLogy(0)
QCDDiff.SetTitle('OS/SS MultiJet Relaxed Events %s (%.1f fb^{-1}); %s; OS/SS' %(titleName, Lumi,varName))
QCDDiff.SetMarkerStyle(8)
QCDDiff.SetMarkerSize(0.9)
QCDDiff.SetMaximum(4)
QCDDiff.SetMinimum(0)
QCDDiff.Draw('PE')
fit1.Draw('same')
lFit1 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fit1,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(fit1.GetParameter(0), fit1.GetParError(0)))])
lFit1.Draw('same')
for k in range(3):
c.cd(k+2)
if logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k+2)
QCDHistList_withScale[k+1].SetTitle('%s %s Data - MC Events %s (%.1f fb^{-1}); %s; events / bin' %(signSelection, iso, titleName, Lumi,varName))
QCDHistList_withScale[k+1].SetMarkerStyle(8)
QCDHistList_withScale[k+1].SetMarkerSize(0.9)
QCDHistList_withScale[k+1].SetMaximum(max)
QCDHistList_withScale[k+1].SetMinimum(1)
QCDHistList_withScale[k+1].Draw('PE')
c.Update()
c.Print('%s' %psfile)
c.cd(1)
r.gPad.SetLogy(0)
QCDDiff2.SetTitle('tight/relaxed MultiJet SS Events %s (%.1f fb^{-1}); %s; tight/relaxed' %(titleName, Lumi,varName))
QCDDiff2.SetMarkerStyle(8)
QCDDiff2.SetMarkerSize(0.9)
QCDDiff2.SetMinimum(3)
QCDDiff2.SetMaximum(0)
QCDDiff2.Draw('PE')
fit2.Draw('same')
lFit2 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fit2,'Scale between relaxed/tight in SS region: %.3f \pm %.3f' %(fit2.GetParameter(0), fit2.GetParError(0)))])
lFit2.Draw('same')
c.Print('%s)' %psfile)
#ps.Close()
print "Plot saved at %s" %(psfile)
def go(sig, bkg):
bins_array = array('d', range(20, 151, 1))
sigHist = r.TH2F('sigHist', "",
len(bins_array) - 1, bins_array,
len(bins_array) - 1, bins_array)
bkgHist = r.TH2F('bkgHist', "",
len(bins_array) - 1, bins_array,
len(bins_array) - 1, bins_array)
sigHistJet1 = r.TH2F('sigHistJet1', "",
len(bins_array) - 1, bins_array,
len(bins_array) - 1, bins_array)
bkgHistJet1 = r.TH2F('bkgHistJet1', "",
len(bins_array) - 1, bins_array,
len(bins_array) - 1, bins_array)
sigHistJet2 = r.TH2F('sigHistJet2', "",
len(bins_array) - 1, bins_array,
len(bins_array) - 1, bins_array)
bkgHistJet2 = r.TH2F('bkgHistJet2', "",
len(bins_array) - 1, bins_array,
len(bins_array) - 1, bins_array)
sigHistJet3 = r.TH2F('sigHistJet3', "",
len(bins_array) - 1, bins_array,
len(bins_array) - 1, bins_array)
bkgHistJet3 = r.TH2F('bkgHistJet3', "",
len(bins_array) - 1, bins_array,
len(bins_array) - 1, bins_array)
bkgHist2 = r.TH2F('bkgHist2', "", 10000, 0, 1.0, 10000, 0, 1.0)
bkgHistJet = r.TH2F('bkgHistJet', "", 10000, 0, 1.0, 10000, 0, 1.0)
distHist3 = r.TH1F('distHist3', "", len(bins_array) - 1, bins_array)
distHist = r.TGraph()
distHist_2 = r.TGraph()
distHist2 = r.TGraph()
distHist2_2 = r.TGraph()
distHist4 = r.TGraph()
distHist4_2 = r.TGraph()
distHist5 = r.TGraph()
distHist5_2 = r.TGraph()
distHist3.SetTitle('; PtHat; weight')
# distHist2 = r.TH2F('distHist2', "", len(bins_array)-1, bins_array, len(bins_array)-1, bins_array)
sigHist = getPtDistribution(sig, sigHist, None, None, 0.0, 'central',
False, True)
sigHistJet1 = getPtDistribution(sig, sigHistJet1, None, None, 30.0,
'central', False, True)
sigHistJet2 = getPtDistribution(sig, sigHistJet2, None, None, 40.0)
sigHistJet3 = getPtDistribution(sig, sigHistJet3, None, None, 50.0)
sigHist.SetTitle('h2#tau#tau; Leading jet Pt; Leading #tau Pt')
bkgHist, bkgHist2, distHist3 = getPtDistribution(bkg, bkgHist, bkgHist2,
distHist3, 0.0, 'central',
False, True)
bkgHistJet1, bkgHistJet = getPtDistribution(bkg, bkgHistJet1, bkgHistJet,
None, 30.0, 'central', False,
True)
bkgHistJet2, bkgHistJet = getPtDistribution(bkg, bkgHistJet2, bkgHistJet,
None, 40.0)
bkgHistJet3, bkgHistJet = getPtDistribution(bkg, bkgHistJet3, bkgHistJet,
None, 50.0)
bkgHist.SetTitle('QCD; Leading jet Pt; Leading #tau Pt')
bkgHist2.SetTitle('QCD; 200000*PtHat^{-4.5}; weight')
getBothDistribution(sigHist, bkgHist, distHist, distHist_2)
getBothDistribution(sigHist, bkgHist, distHist5, distHist5_2, 5)
getBothDistribution(sigHist, bkgHist, distHist2, distHist2_2, 10)
getBothDistribution(sigHist, bkgHist, distHist4, distHist4_2, 15)
jet1 = r.TGraph()
jet1_2 = r.TGraph()
jet2 = r.TGraph()
jet2_2 = r.TGraph()
jet3 = r.TGraph()
jet3_2 = r.TGraph()
getBothDistribution(sigHistJet1, bkgHistJet1, jet1, jet1_2, 0, 30.5)
getBothDistribution(sigHistJet2, bkgHistJet2, jet2, jet2_2, 0, 30.5)
getBothDistribution(sigHistJet3, bkgHistJet3, jet3, jet3_2, 0, 30.5)
fakeHist1 = r.TH1F()
fakeHist1.SetMarkerStyle(5)
fakeHist2 = r.TH1F()
fakeHist2.SetMarkerStyle(2)
fakeHist2.SetMarkerColor(r.kBlue)
fakeHist3 = r.TH1F()
fakeHist3.SetMarkerStyle(25)
fakeHist3.SetMarkerColor(r.kRed)
fakeHist4 = r.TH1F()
fakeHist4.SetMarkerStyle(32)
fakeHist4.SetMarkerColor(r.kGreen)
fakeHist5 = r.TH1F()
fakeHist5.SetMarkerStyle(5)
fakeHist5.SetMarkerColor(r.kBlue)
fakeHist6 = r.TH1F()
fakeHist6.SetMarkerStyle(5)
fakeHist6.SetMarkerColor(r.kRed)
fakeHist7 = r.TH1F()
fakeHist7.SetMarkerStyle(5)
fakeHist7.SetMarkerColor(r.kGreen)
psfile = 'test2.pdf'
c = r.TCanvas("c", "Test", 600, 600)
c.SetGrid()
c.SetFillStyle(4000)
# distHist.GetXaxis().SetLimits(0.998,1.00001)
distHist.GetXaxis().SetLimits(0.999, 1.00001)
distHist.SetMinimum(0.001)
distHist.SetMaximum(0.8)
c.SetLogy()
distHist.GetXaxis().SetNdivisions(210)
distHist.Draw("AP")
distHist.SetMarkerStyle(7)
distHist.GetYaxis().SetTitleOffset(1.4)
distHist5.SetMarkerStyle(7)
distHist5_2.SetMarkerColor(r.kBlue)
distHist5.SetMarkerColor(r.kBlue)
# distHist5.Draw('PSame')
distHist2.SetMarkerStyle(7)
distHist2_2.SetMarkerColor(r.kRed)
distHist2.SetMarkerColor(r.kRed)
# distHist2.Draw('PSame')
distHist4.SetMarkerStyle(7)
distHist4_2.SetMarkerColor(r.kGreen)
distHist4.SetMarkerColor(r.kGreen)
# distHist4.Draw('PSame')
distHist_2.SetMarkerStyle(5)
distHist5_2.SetMarkerStyle(2)
distHist2_2.SetMarkerStyle(25)
distHist4_2.SetMarkerStyle(32)
# distHist2_2.Draw('PSame')
# distHist5_2.Draw('PSame')
distHist_2.Draw('PSame')
# distHist4_2.Draw('PSame')
jet1.SetMarkerStyle(7)
jet1.SetMarkerColor(r.kBlue)
jet1_2.SetMarkerStyle(5)
jet1_2.SetMarkerColor(r.kBlue)
jet2.SetMarkerStyle(7)
jet2.SetMarkerColor(r.kRed)
jet2_2.SetMarkerStyle(5)
jet2_2.SetMarkerColor(r.kRed)
jet3.SetMarkerStyle(7)
jet3.SetMarkerColor(r.kGreen)
jet3_2.SetMarkerStyle(5)
jet3_2.SetMarkerColor(r.kGreen)
jet1.Draw('PSame')
jet1_2.Draw('PSame')
jet2.Draw('PSame')
jet2_2.Draw('PSame')
jet3.Draw('PSame')
jet3_2.Draw('PSame')
l = tool.setMyLegend(
lPosition=[0.2, 0.45, 0.5, 0.25],
lHistList=[
(fakeHist1, 'same pt cut (tau pt 40)'),
# (fakeHist2, 'leading + 5 (35, 40, 45)'),
# (fakeHist3, 'leading + 10 (35, 40, 45)'),
# (fakeHist4, 'leading + 15 (35, 40, 45)'),
(fakeHist5, 'same with jet pt 30 (tau pt 30)'),
(fakeHist6, 'same with jet pt 40 (tau pt 30)'),
(fakeHist7, 'same with jet pt 50 (tau pt 30)'),
])
l.Draw('same')
c.Print('%s(' % psfile)
c.SetLogy(0)
c.Clear()
distHist3.Draw()
c.Print('%s' % psfile)
c.Clear()
# p1 = r.TPad("p1","p1",0.,1,1.,0.0)
# p1.SetMargin(0.13, 0.13, 0.13, 0.13)
# p1.SetGrid()
p2 = r.TPad("p1", "p1", 0., 1, 1., 0.0)
p2.SetMargin(0.13, 0.13, 0.13, 0.13)
p2.SetGrid()
#
# # p1.Draw()
# # p1.cd()
# # distHist2.GetXaxis().SetNdivisions(216)
# # distHist2.GetYaxis().SetNdivisions(216)
# # distHist2.GetYaxis().SetTitleOffset(1.2)
# # distHist2.Draw('COLZ')
# # c.Print('%s' %psfile)
# # c.Clear()
p2.Draw()
p2.cd()
sigHist.Draw('COLZ')
sigHist.GetYaxis().SetTitleOffset(1.2)
sigHist.GetXaxis().SetNdivisions(216)
sigHist.GetYaxis().SetNdivisions(216)
c.Print('%s' % psfile)
c.Clear()
p3 = r.TPad("p1", "p1", 0., 1, 1., 0.0)
p3.SetMargin(0.13, 0.13, 0.13, 0.13)
p3.SetGrid()
p3.Draw()
p3.cd()
bkgHist.Draw('COLZ')
bkgHist.GetYaxis().SetTitleOffset(1.2)
bkgHist.GetXaxis().SetNdivisions(216)
bkgHist.GetYaxis().SetNdivisions(216)
c.Print('%s' % psfile)
c.Clear()
bkgHistJet2.Draw('COLZ')
bkgHistJet2.GetYaxis().SetTitleOffset(1.2)
bkgHistJet2.GetXaxis().SetNdivisions(216)
bkgHistJet2.GetYaxis().SetNdivisions(216)
c.Print('%s' % psfile)
c.Clear()
sigHistJet2.Draw('COLZ')
sigHistJet2.GetYaxis().SetTitleOffset(1.2)
sigHistJet2.GetXaxis().SetNdivisions(216)
sigHistJet2.GetYaxis().SetNdivisions(216)
c.Print('%s)' % psfile)
c.Close()
def getHistos(varName, signalSelection, logY, sigBoost, nbins, useData, max, rangeMin, rangeMax, location, bTag, predict, predictPtBin, region, thirdLeptonVeto):
r.gStyle.SetOptStat(0)
fileList = draw_cfg.MCFileList
histList = []
histList_4QCD = []
QCDHistList = []
QCDHistList_4KS = []
QCDHistList_withScale = []
varRange = [nbins, rangeMin, rangeMax]
nBins = 10000
Lumi = 19.7
initNEventsList = []
legendHistos = []
var_background = []
scaleMCPt = 1.0
tmpFile = []
tmpTree = []
var_data_4KS = []
var_data = []
var_data_4QCD = []
histList_4KS = []
for i in range(5):
var_data.append(r.TH1F('data_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
var_data_4KS.append(r.TH1F('data_4KS_%i' %(i),"", nBins, varRange[1], varRange[2]))
if i < 5:
var_data_4QCD.append(r.TH1F('data_4QCD_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
dataName = draw_cfg.dataFile
fData = r.TFile(dataName)
treeData = fData.Get('eventTree')
print 'Adding events from: %s ...' %dataName
for iEntry in range(treeData.GetEntries()):
treeData.GetEntry(iEntry)
select = passCut(treeData, bTag, region, thirdLeptonVeto)
if (select == 0) or (select == 1) or (select > 6):
continue
var_data[select-2].Fill(varsList.findVar(treeData, varName))
var_data_4KS[select-2].Fill(varsList.findVar(treeData, varName))
if select == 2:
var_data_4QCD[0].Fill(varsList.findVar(treeData, varName), findPtScale(treeData.pt1.at(0),treeData.pt2.at(0), 'left', '%s%s' %(region, bTag)))
elif select == 3:
var_data_4QCD[1].Fill(varsList.findVar(treeData, varName), findPtScale(treeData.pt1.at(0),treeData.pt2.at(0), 'up', '%s%s' %(region, bTag)))
elif select == 4:
var_data_4QCD[2].Fill(varsList.findVar(treeData, varName), findPtScale(treeData.pt1.at(0),treeData.pt2.at(0), 'left', '%s%s' %(region, bTag)))
var_data_4QCD[3].Fill(varsList.findVar(treeData, varName), findPtScale(treeData.pt1.at(0),treeData.pt2.at(0), 'up', '%s%s' %(region, bTag)))
legendHistos.append([])
for j in range(5):
var_data[j].SetMarkerStyle(8)
var_data[j].SetMarkerSize(0.9)
legendHistos.append([])
legendHistos[j+1].append((var_data[j], 'observed (%.0f)' %var_data[j].Integral()))
for i in range(len(fileList)):
for j in range(6):
histList_4KS.append(r.TH1F('%s_%i_KS' %(fileList[i][0],j),fileList[i][0], nBins, varRange[1], varRange[2]))
histList.append(r.TH1F('%s_%i' %(fileList[i][0],j),fileList[i][0], varRange[0], varRange[1], varRange[2]))
histList_4QCD.append(r.TH1F('%s_%i_2' %(fileList[i][0],j),fileList[i][0], varRange[0], varRange[1], varRange[2]))
print 'Adding events from: %s ...' %(fileList[i][1])
tmpFile.append(r.TFile(fileList[i][1]))
tmpTree.append(tmpFile[i].Get('eventTree'))
for iEntry in range(tmpTree[i].GetEntries()):
tmpTree[i].GetEntry(iEntry)
select = passCut(tmpTree[i], bTag, region, thirdLeptonVeto)
if (not select) or (select > 6):
continue
histList[6*i+select-1].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff)
histList_4KS[6*i+select-1].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff)
if select == 2:
histList_4QCD[6*i].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff*findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0), 'left', '%s%s' %(region, bTag)))
elif select == 3:
histList_4QCD[6*i+1].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff*findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0), 'up', '%s%s' %(region, bTag)))
elif select == 4:
histList_4QCD[6*i+2].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff*findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0), 'left', '%s%s' %(region, bTag)))
histList_4QCD[6*i+3].Fill(varsList.findVar(tmpTree[i], varName)*scaleMCPt, tmpTree[i].triggerEff*findPtScale(tmpTree[i].pt1.at(0), tmpTree[i].pt2.at(0), 'up', '%s%s' %(region, bTag)))
initNEventsList.append(tmpFile[i].Get('preselection'))
for j in range(6):
var_background.append(r.THStack())
histList[6*i+j].SetFillColor(fileList[i][3])
histList[6*i+j].Scale(fileList[i][2]*Lumi/initNEventsList[i].GetBinContent(1))
histList_4QCD[6*i+j].Scale(fileList[i][2]*Lumi/initNEventsList[i].GetBinContent(1))
histList_4KS[6*i+j].Scale(fileList[i][2]*Lumi/initNEventsList[i].GetBinContent(1))
var_background[j].Add(histList[6*i+j])
legendHistos[j].append((histList[6*i+j], '%s (%.2f)' %(fileList[i][0], histList[6*i+j].Integral())))
data_i = []
MC_i = []
data_r = []
MC_r = []
e = []
MC_List = []
for i in range(3):
QCDHistList.append(r.TH1F('QCD_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
QCDHistList_4KS.append(r.TH1F('QCD_%i_KS' %(i),"", nBins, varRange[1], varRange[2]))
MC_List.append(r.TH1F('MC_total_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
for j in range(varRange[0]):
dataValue = var_data[i].GetBinContent(j+1)
dataError = var_data[i].GetBinError(j+1)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList[6*k+1+i].GetBinContent(j+1)
if i == 0:
data_i.append(dataValue)
e.append(dataError)
MC_i.append(MCValue)
if i == 2:
data_r.append(dataValue)
MC_r.append(MCValue)
MC_List[i].SetBinContent(j+1, MCValue)
if dataValue - MCValue > 0:
QCDHistList[i].SetBinContent(j+1, dataValue - MCValue)
QCDHistList[i].SetBinError(j+1, dataError)
MC_List[i].Sumw2()
for j in range(nBins):
dataValue4KS = var_data_4KS[i].GetBinContent(j+1)
MCValue4KS = 0
for k in range(len(fileList)):
MCValue4KS += histList_4KS[6*k+1+i].GetBinContent(j+1)
if dataValue4KS - MCValue4KS > 0:
QCDHistList_4KS[i].SetBinContent(j+1, dataValue4KS - MCValue4KS)
ss_t = QCDHistList[0].Integral()
ss_l = QCDHistList[2].Integral()
os_l = QCDHistList[1].Integral()
os_l_data = var_data[1].Integral()
print "QCD in SS_T: %.4f" %ss_t
print "QCD in SS_L: %.4f" %ss_l
print "QCD in OS_L: %.4f" %os_l
print "Data in OS_L:%.4f" %os_l_data
print "SF: %.4f" %(ss_t/ss_l)
print "SF qcd/data: %.4f" %(os_l/os_l_data)
for i in range(4):
QCDHistList_withScale.append(r.TH1F('QCD_withScale_%i' %(i),"", varRange[0], varRange[1], varRange[2]))
for j in range(varRange[0]):
dataValue = var_data_4QCD[i].GetBinContent(j+1)
dataError = var_data_4QCD[i].GetBinError(j+1)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList_4QCD[6*k+i].GetBinContent(j+1)
if dataValue - MCValue > 0:
QCDHistList_withScale[i].SetBinContent(j+1, dataValue - MCValue)
QCDDiff = r.TH1F('QCD_diff',"", varRange[0], varRange[1], varRange[2])
QCDDiff2 = r.TH1F('QCD_diff2',"", varRange[0], varRange[1], varRange[2])
QCDDiff_R2T = r.TH1F('QCDDiff_R2T',"", varRange[0], varRange[1], varRange[2])
# QCDDiff2.Sumw2()
fit1 = r.TF1("fit1","[0]", varRange[1],varRange[2])
fit1.SetParName(0,'scale')
fit1.FixParameter(0,1.0)
QCDDiff.Fit('fit1', '0EM')
fit1.SetLineStyle(2)
fit1.SetLineColor(r.kRed)
fit2 = r.TF1("fit2","[0]", varRange[1],varRange[2])
fit2.SetParName(0,'scale')
fit2.FixParameter(0,1.0)
QCDDiff2.Fit('fit2', '0EM')
fit2.SetLineStyle(2)
fit2.SetLineColor(r.kRed)
DrawSignal = False
if signalSelection != '':
var_signal = []
var_signal_4KS = []
for i in range(6):
var_signal.append(r.TH1F('%s_%i' %(signalSelection,i),"", varRange[0], varRange[1], varRange[2]))
var_signal_4KS.append(r.TH1F('%s_%i_4KS' %(signalSelection,i),"", nBins, varRange[1], varRange[2]))
signalDict = draw_cfg.signalDict
if signalSelection in signalDict:
fSignal = r.TFile(signalDict[signalSelection][0])
treeSignal = fSignal.Get('eventTree')
print 'Adding events from: %s ...' %(signalDict[signalSelection][0])
for iEntry in range(treeSignal.GetEntries()):
treeSignal.GetEntry(iEntry)
select = passCut(treeSignal, bTag, region, thirdLeptonVeto)
if (not select) or (select > 6):
continue
var_signal[select-1].Fill(varsList.findVar(treeSignal, varName), treeSignal.triggerEff)
var_signal_4KS[select-1].Fill(varsList.findVar(treeSignal, varName), treeSignal.triggerEff)
initNEventsSignal = fSignal.Get('preselection')
for i in range(6):
var_signal[i].SetLineStyle(7)
var_signal[i].SetLineWidth(4)
var_signal[i].Scale(signalDict[signalSelection][1]*sigBoost*Lumi/initNEventsSignal.GetBinContent(1))
if sigBoost != 1:
sum = var_signal[i].Integral(0, var_signal[i].GetNbinsX()+1)
legendHistos[i].append((var_signal[i], '%sx%0.f (%.2f)' %(signalSelection, sigBoost, var_signal[i].Integral())))
else:
legendHistos[i].append((var_signal[i], '%s (%.2f)' %(signalSelection, var_signal[i].Integral())))
DrawSignal = True
else:
print '%s not supported, please use H260, H300 or H350' %signalSelection
if predict == 'True':
scale_SS2OS = fit1.GetParameter(0)
scale_er_SS2OS = fit1.GetParError(0)
scale_relaxed2Tight = fit2.GetParameter(0)
scale_er_relaxed2Tight = fit2.GetParError(0)
QCDHistList_withScale[0].Scale(scale_SS2OS)
QCDHistList_withScale[1].Scale(scale_relaxed2Tight)
QCDHistList_withScale[2].Scale(scale_SS2OS)
QCDHistList_withScale[3].Scale(scale_relaxed2Tight)
QCDHistList_withScale[3].SetFillColor(r.kSpring+1)
QCDHistList_withScale[2].SetFillColor(r.kOrange-4)
QCDHistList_withScale[0].SetLineColor(r.kSpring+1)
QCDHistList_withScale[0].SetLineWidth(2)
QCDHistList_withScale[1].SetLineStyle(2)
QCDHistList_withScale[1].SetLineColor(r.kOrange-4)
QCDHistList_withScale[1].SetLineWidth(2)
legendHistos[0].append((QCDHistList_withScale[0], 'From SS/Tight (%.0f)' %QCDHistList_withScale[0].Integral()))
legendHistos[0].append((QCDHistList_withScale[1], 'From OS/Relax (%.0f)' %QCDHistList_withScale[1].Integral()))
var_background[1].Add(QCDHistList_withScale[3])
var_background[2].Add(QCDHistList_withScale[2])
legendHistos[1].append((QCDHistList_withScale[3], 'From SS/Relax (%.0f)' %QCDHistList_withScale[3].Integral()))
legendHistos[2].append((QCDHistList_withScale[2], 'From SS/Relax (%.0f)' %QCDHistList_withScale[2].Integral()))
QCDHistList_withScale[1] = tool.addFakeTHStack(QCDHistList_withScale[1],var_background[0])
QCDHistList_withScale[0] = tool.addFakeTHStack(QCDHistList_withScale[0],var_background[0])
# var_data[1].Sumw2()
# MC_List[1].Sumw2()
for i in range(varRange[0]):
oldValue = var_data[1].GetBinContent(i+1)
mcValue = MC_List[1].GetBinContent(i+1)
if oldValue - mcValue > 0:
QCDDiff2.SetBinContent(i+1, (oldValue - mcValue)/oldValue)
QCDDiff2.SetBinError(i+1, MC_List[1].GetBinError(i+1)/oldValue)
print QCDDiff2.Integral()
QCDDiff = var_data[1].Clone()
QCDDiff_sub = QCDHistList_withScale[2].Clone() + MC_List[1].Clone()
QCDDiff.Divide(QCDDiff_sub)
QCDDiff_R2T = var_data[0].Clone()
QCDDiff_R2T_sub = QCDHistList_withScale[3].Clone() + MC_List[0].Clone()
QCDDiff_R2T.Divide(QCDDiff_R2T_sub)
legendPosition = (0.6, 0.7, 0.90, 0.88)
l = []
r.gROOT.SetBatch(True) # to suppress canvas pop-outs
if bTag == 'True':
titleName = '1 Medium 1 Loose b-tag'
fileName = 'bTag'
elif bTag == 'False':
titleName = ''
fileName = 'all'
elif bTag == '2M':
titleName = '2 Medium b-tags'
fileName = '2MbTag'
elif bTag == '1M':
titleName = '1 Medium b-tag'
fileName = '1MbTag'
KS1 = QCDHistList_4KS[0].KolmogorovTest(QCDHistList_4KS[2])
KS2 = QCDHistList_4KS[1].KolmogorovTest(QCDHistList_4KS[2])
ks_values = []
tmpHists = []
ks_values2 = []
tmpHists2 = []
nTimes = 10000
# for i in range(nTimes):
# tool.printProcessStatus(i, nTimes, processName = 'Making Sample Histograms')
# tmpHists.append(r.TH1F('tmpHist_%i' %(i),"", nBins, varRange[1], varRange[2]))
# tmpHists[i].FillRandom(QCDHistList_4KS[2], 100)
# ks_values.append(QCDHistList_4KS[0].KolmogorovTest(tmpHists[i]))
# tmpHists2.append(r.TH1F('tmpHist2_%i' %(i),"", nBins, varRange[1], varRange[2]))
# tmpHists2[i].FillRandom(QCDHistList_4KS[2], 100)
# ks_values2.append(QCDHistList_4KS[2].KolmogorovTest(tmpHists[i]))
print ''
print 'KS Test 1: %.3f' %KS1
print 'KS Test 2: %.3f' %KS2
psfile = '%s_%s_%s.pdf' %(varName, fileName, signalSelection)
c = r.TCanvas("c","Test", 800, 900)
#ps = r.TPDF(psfile,112)
c.Divide(2,3)
drawOpt = ''
QCDDiff.SetTitle('Data/Background OS Relaxed Events %s (%.1f fb^{-1}); %s; Data/Background' %(titleName, Lumi,varName))
QCDDiff.SetMarkerStyle(8)
QCDDiff.SetMarkerSize(0.9)
QCDDiff.SetMaximum(4)
QCDDiff.SetMinimum(0)
QCDDiff_R2T.SetTitle('Data/Background OS Relaxed Events %s (%.1f fb^{-1}); %s; Data/Background' %(titleName, Lumi,varName))
QCDDiff_R2T.SetMarkerStyle(8)
QCDDiff_R2T.SetMarkerSize(0.9)
QCDDiff_R2T.SetMaximum(4)
QCDDiff_R2T.SetMinimum(0)
for k in range(6):
c.cd(k+1)
r.gPad.SetTicky()
if k > 1 and logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k+1)
var_background[k].SetTitle('%s %s Events %s (%.1f fb^{-1}); %s; events / bin' %(signSelection, iso, titleName, Lumi,varName))
if k < 2:
var_background[k].SetMaximum(250)
else:
var_background[k].SetMaximum(max)
var_background[k].SetMinimum(0.01)
var_background[k].Draw()
if predict == 'True' and k == 0:
QCDHistList_withScale[0].Draw('same')
QCDHistList_withScale[1].Draw('same')
if k != 0 and useData == 'True':
var_data[k-1].Draw('PE same')
legendPosition = (0.63, 0.93 - 0.03*len(legendHistos[k]), 0.93, 0.9)
l.append(tool.setMyLegend(lPosition=legendPosition, lHistList=legendHistos[k]))
if k == 1:
ksLegend1 = tool.setMyLegend((0.3, 0.8, 0.6, 0.9), [(QCDHistList_withScale[3], 'KS Test: %.3f' %KS1)])
ksLegend1.Draw('same')
if k == 2:
ksLegend2 = tool.setMyLegend((0.3, 0.8, 0.6, 0.9), [(QCDHistList_withScale[2], 'KS Test: %.3f' %KS2)])
ksLegend2.Draw('same')
l[k].Draw('same')
var_signal[k].Draw('same')
c.Update()
c.cd(5)
r.gPad.SetLogy(0)
QCDDiff.Draw('PE')
fit1.Draw('same')
c.cd(6)
r.gPad.SetLogy(0)
QCDDiff_R2T.Draw('PE')
fit2.Draw('same')
c.Update()
c.Print('%s(' %psfile)
c.cd(1)
r.gPad.SetLogy(0)
QCDDiff.Draw('PE')
fit1.Draw('same')
lFit1 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fit1,'Scale between OS/SS in relaxed region: %.2f \pm %.2f' %(fit1.GetParameter(0), fit1.GetParError(0)))])
lFit1.Draw('same')
for k in range(3):
c.cd(k+2)
if logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k+2)
QCDHistList_withScale[k+1].SetTitle('%s %s Data - MC Events %s (%.1f fb^{-1}); %s; events / bin' %(signSelection, iso, titleName, Lumi,varName))
QCDHistList_withScale[k+1].SetMarkerStyle(8)
QCDHistList_withScale[k+1].SetMarkerSize(0.9)
QCDHistList_withScale[k+1].SetMaximum(max)
QCDHistList_withScale[k+1].SetMinimum(1)
QCDHistList_withScale[k+1].Draw('PE')
c.Update()
c.Print('%s' %psfile)
c.cd(1)
r.gPad.SetLogy(0)
QCDDiff2.SetTitle('QCD/Data OS Relaxed Events %s (%.1f fb^{-1}); %s; QCD/Data' %(titleName, Lumi,varName))
QCDDiff2.SetMarkerStyle(8)
QCDDiff2.SetMarkerSize(0.9)
QCDDiff2.SetMinimum(0.0)
QCDDiff2.SetMaximum(1.0)
QCDDiff2.Draw('PE')
fit2.Draw('same')
lFit2 = tool.setMyLegend((0.15, 0.7, 0.9, 0.85),[(fit2,'Scale between relaxed/tight in SS region: %.3f \pm %.3f' %(fit2.GetParameter(0), fit2.GetParError(0)))])
lFit2.Draw('same')
c.Update()
c.Print('%s' %psfile)
# c.Clear()
# c.Divide(1,2)
# c.cd(1)
# graph = r.TH1F('graph', '', 20, 0, 1.0)
# graph2 = r.TH1F('graph2', '', 20, 0, 1.0)
# for i in range(nTimes):
# tool.printProcessStatus(i, nTimes, processName = 'Plotting Sample Histograms')
# graph.Fill(ks_values[i])
# graph2.Fill(ks_values2[i])
# print ''
# graph.SetTitle('KS Tests Between SS Tight and Relaxed; KS; ')
# graph.SetMarkerStyle(8)
# graph.Draw('PE')
# c.cd(2)
# graph2.SetTitle('KS Self-test in SS Relaxed; KS; ')
# graph2.SetMarkerStyle(8)
# graph2.Draw('PE')
# c.Update()
# c.Print('%s' %psfile)
# c.Clear()
#
# a1 = getAccuDist(QCDHistList_4KS[0], varRange[1], varRange[2], 'SS Tight')
# a2 = getAccuDist(QCDHistList_4KS[2], varRange[1], varRange[2], 'SS Relax')
# a1.SetLineColor(r.kRed)
# l3 = tool.setMyLegend((0.15, 0.75, 0.3, 0.85),[(a1,'SS Tight'),
# (a2,'SS Relax')])
#
# a1.SetTitle('Cumulative Distribution; %s;' %varName)
# a1.Draw()
# a2.Draw('same')
# l3.Draw('same')
c.Print('%s)' %psfile)
#ps.Close()
print "Plot saved at %s" %(psfile)
c.Close()
legendPosition = (0.1, 0.2, 0.85, 0.5)
f.close()
effList_both[0].SetTitle('Background Rejection VS Signal Efficiency (%s); Signal Efficiency; Background Rejection' %massPoint)
effList_both[0].SetMinimum(0.5)
effList_both[0].GetXaxis().SetRangeUser(0.5, 1)
effList_both[0].Draw('L')
for j in range(1, len(iFileList)):
effList_both[j].Draw('sameL')
line = r.TLine(0.8, 0.5, 0.8, 1)
line.SetLineColor(r.kRed)
line.SetLineStyle(2)
line.Draw('same')
l.append(tool.setMyLegend(legendPosition, lHistList2))
l[iMassPoint].Draw('same')
for j in range(0, len(ROCCurves)):
ROCCurves[j].Draw('PLsame')
c.Update()
if massPoint == massPoints[0]:
c.Print('%s(' %psfile)
elif massPoint == massPoints[len(massPoints)-1]:
c.Print('%s' %psfile)
c.Clear()
bkgEffs[0].SetTitle('Bkg Efficiency with Sig Efficency at %.2f; H Mass; Background Efficiency' %sigEff)
bkgEffs[0].Draw('APL')
bkgEffs[0].SetMinimum(0.00)
bkgEffs[0].SetMaximum(0.2)
for j in range(1, len(iFileList)):
def getHistos(varName, signalSelection, logY, sigBoost, nbins, useData, max,
rangeMin, rangeMax, location, bTag, predict, predictPtBin,
region, thirdLeptonVeto, SF, yMax, relaxedRegionOption, usePU):
r.gStyle.SetOptStat(0)
SF = float(SF)
fileList = draw_cfg.MCFileList
histList = []
histList_4QCD = []
QCDHistList = []
QCDHistList_4KS = []
QCDHistList_withScale = []
varRange = [nbins, rangeMin, rangeMax]
nBins = 10000
Lumi = 19.7
legendHistos = []
var_background = []
scaleMCPt = 1.0
tmpFile = []
tmpTree = []
var_data_4KS = []
var_data = []
var_data_4QCD = []
histList_4KS = []
MC_Counts_0 = 0.0
MC_Counts_1 = 0.0
MC_Counts_2 = 0.0
for i in range(6):
var_data.append(
r.TH1F('data_%i' % (i), "", varRange[0], varRange[1], varRange[2]))
var_data_4KS.append(
r.TH1F('data_4KS_%i' % (i), "", nBins, varRange[1], varRange[2]))
if i < 5:
var_data_4QCD.append(
r.TH1F('data_4QCD_%i' % (i), "", varRange[0], varRange[1],
varRange[2]))
dataName = draw_cfg.dataFile
fData = r.TFile(dataName)
treeData = fData.Get('eventTree')
print 'Adding events from: %s ...' % dataName
for iEntry in range(treeData.GetEntries()):
treeData.GetEntry(iEntry)
select = passCut(treeData, bTag, region, thirdLeptonVeto,
relaxedRegionOption)
if (select == 0) or (select > 6):
continue
if (select == 1) and dontUnblind(treeData):
continue
var_data[select - 1].Fill(varsList.findVar(treeData, varName))
if (select != 1):
var_data_4KS[select - 2].Fill(varsList.findVar(treeData, varName))
if select == 2:
var_data_4QCD[0].Fill(varsList.findVar(treeData, varName), 1.0)
elif select == 3:
var_data_4QCD[1].Fill(varsList.findVar(treeData, varName), SF)
elif select == 4:
var_data_4QCD[2].Fill(varsList.findVar(treeData, varName), 1.0)
var_data_4QCD[3].Fill(varsList.findVar(treeData, varName), SF)
legendHistos.append([])
for j in range(6):
var_data[j].SetMarkerStyle(8)
var_data[j].SetMarkerSize(0.9)
legendHistos.append([])
integral = 'observed'
if j != 0 or (region != 'tight') or (bTag == 'None'):
integral = 'observed (%.0f)' % var_data[j].Integral(
0, varRange[0] + 1)
legendHistos[j].append((var_data[j], integral))
for i in range(len(fileList)):
for j in range(6):
histList_4KS.append(
r.TH1F('%s_%i_KS' % (fileList[i][0], j), fileList[i][0], nBins,
varRange[1], varRange[2]))
histList.append(
r.TH1F('%s_%i' % (fileList[i][0], j), fileList[i][0],
varRange[0], varRange[1], varRange[2]))
histList_4QCD.append(
r.TH1F('%s_%i_2' % (fileList[i][0], j), fileList[i][0],
varRange[0], varRange[1], varRange[2]))
print 'Adding events from: %s ...' % (fileList[i][1])
tmpFile.append(r.TFile(fileList[i][1]))
tmpTree.append(tmpFile[i].Get('eventTree'))
for iEntry in range(tmpTree[i].GetEntries()):
tmpTree[i].GetEntry(iEntry)
select = passCut(tmpTree[i], bTag, region, thirdLeptonVeto,
relaxedRegionOption)
if (not select) or (select > 6):
continue
if usePU:
allWeights = tmpTree[i].triggerEff * tmpTree[i].PUWeight
else:
allWeights = tmpTree[i].triggerEff
histList[6 * i + select - 1].Fill(
varsList.findVar(tmpTree[i], varName),
allWeights * tmpTree[i].xs / (tmpTree[i].initEvents))
histList_4KS[6 * i + select - 1].Fill(
varsList.findVar(tmpTree[i], varName),
allWeights * tmpTree[i].xs / (tmpTree[i].initEvents))
if select == 2:
histList_4QCD[6 * i].Fill(
varsList.findVar(tmpTree[i], varName),
allWeights * 1.0 * tmpTree[i].xs / (tmpTree[i].initEvents))
elif select == 3:
histList_4QCD[6 * i + 1].Fill(
varsList.findVar(tmpTree[i], varName),
allWeights * SF * tmpTree[i].xs / (tmpTree[i].initEvents))
elif select == 4:
histList_4QCD[6 * i + 2].Fill(
varsList.findVar(tmpTree[i], varName),
allWeights * 1.0 * tmpTree[i].xs / (tmpTree[i].initEvents))
histList_4QCD[6 * i + 3].Fill(
varsList.findVar(tmpTree[i], varName),
allWeights * SF * tmpTree[i].xs / (tmpTree[i].initEvents))
for j in range(6):
var_background.append(r.THStack())
histList[6 * i + j].SetFillColor(fileList[i][2])
histList[6 * i + j].Scale(Lumi)
histList_4QCD[6 * i + j].Scale(Lumi)
histList_4KS[6 * i + j].Scale(Lumi)
var_background[j].Add(histList[6 * i + j])
legendHistos[j].append(
(histList[6 * i + j],
'%s (%.2f)' % (fileList[i][0], histList[6 * i + j].Integral(
0, varRange[0] + 1))))
data_i = []
MC_i = []
data_r = []
MC_r = []
e = []
MC_List = []
for i in range(3):
QCDHistList.append(
r.TH1F('QCD_%i' % (i), "", varRange[0], varRange[1], varRange[2]))
QCDHistList_4KS.append(
r.TH1F('QCD_%i_KS' % (i), "", nBins, varRange[1], varRange[2]))
MC_List.append(
r.TH1F('MC_total_%i' % (i), "", varRange[0], varRange[1],
varRange[2]))
for j in range(varRange[0] + 2):
dataValue = var_data[i + 1].GetBinContent(j)
dataError = var_data[i + 1].GetBinError(j)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList[6 * k + 1 + i].GetBinContent(j)
if i == 0:
data_i.append(dataValue)
e.append(dataError)
MC_i.append(MCValue)
if i == 2:
data_r.append(dataValue)
MC_r.append(MCValue)
MC_List[i].SetBinContent(j, MCValue)
if dataValue - MCValue > 0:
QCDHistList[i].SetBinContent(j, dataValue - MCValue)
QCDHistList[i].SetBinError(j, dataError)
MC_List[i].Sumw2()
for j in range(nBins + 2):
dataValue4KS = var_data_4KS[i].GetBinContent(j)
MCValue4KS = 0
for k in range(len(fileList)):
MCValue4KS += histList_4KS[6 * k + 1 + i].GetBinContent(j)
if dataValue4KS - MCValue4KS > 0:
QCDHistList_4KS[i].SetBinContent(j, dataValue4KS - MCValue4KS)
ss_t = QCDHistList[0].Integral(0, varRange[0] + 1)
ss_l = QCDHistList[2].Integral(0, varRange[0] + 1)
os_l = QCDHistList[1].Integral(0, varRange[0] + 1)
os_l_data = var_data[2].Integral(0, varRange[0] + 1)
print "QCD in SS_T: %.4f" % ss_t
print "QCD in SS_L: %.4f" % ss_l
print "QCD in OS_L: %.4f" % os_l
print "Data in OS_L:%.4f" % os_l_data
print "SF: %.4f" % (ss_t / ss_l)
print "SF qcd/data: %.4f" % (os_l / os_l_data)
for i in range(4):
QCDHistList_withScale.append(
r.TH1F('QCD_withScale_%i' % (i), "", varRange[0], varRange[1],
varRange[2]))
for j in range(varRange[0] + 2):
dataValue = var_data_4QCD[i].GetBinContent(j)
dataError = var_data_4QCD[i].GetBinError(j)
MCValue = 0
for k in range(len(fileList)):
MCValue += histList_4QCD[6 * k + i].GetBinContent(j)
if dataValue - MCValue > 0:
QCDHistList_withScale[i].SetBinContent(j, dataValue - MCValue)
QCDDiff = r.TH1F('QCD_diff', "", varRange[0], varRange[1], varRange[2])
QCDDiff2 = r.TH1F('QCD_diff2', "", varRange[0], varRange[1], varRange[2])
QCDDiff_R2T = r.TH1F('QCDDiff_R2T', "", varRange[0], varRange[1],
varRange[2])
# QCDDiff2.Sumw2()
fit1 = r.TF1("fit1", "[0]", varRange[1], varRange[2])
fit1.SetParName(0, 'scale')
fit1.FixParameter(0, 1.0)
QCDDiff.Fit('fit1', '0EM')
fit1.SetLineStyle(2)
fit1.SetLineColor(r.kRed)
fit2 = r.TF1("fit2", "[0]", varRange[1], varRange[2])
fit2.SetParName(0, 'scale')
fit2.FixParameter(0, 1.0)
QCDDiff2.Fit('fit2', '0EM')
fit2.SetLineStyle(2)
fit2.SetLineColor(r.kRed)
DrawSignal = False
if signalSelection != '':
var_signal = []
var_signal_4KS = []
for i in range(6):
var_signal.append(
r.TH1F('%s_%i' % (signalSelection, i), "", varRange[0],
varRange[1], varRange[2]))
var_signal_4KS.append(
r.TH1F('%s_%i_4KS' % (signalSelection, i), "", nBins,
varRange[1], varRange[2]))
signalDict = draw_cfg.signalDict
if signalSelection in signalDict:
fSignal = r.TFile(signalDict[signalSelection])
treeSignal = fSignal.Get('eventTree')
print 'Adding events from: %s ...' % (signalDict[signalSelection])
for iEntry in range(treeSignal.GetEntries()):
treeSignal.GetEntry(iEntry)
select = passCut(treeSignal, bTag, region, thirdLeptonVeto,
relaxedRegionOption)
if (not select) or (select > 6):
continue
if usePU:
allWeights = treeSignal.triggerEff * treeSignal.PUWeight
else:
allWeights = treeSignal.triggerEff
var_signal[select - 1].Fill(
varsList.findVar(treeSignal, varName),
allWeights * treeSignal.xs / (treeSignal.initEvents))
var_signal_4KS[select - 1].Fill(
varsList.findVar(treeSignal, varName),
allWeights * treeSignal.xs / (treeSignal.initEvents))
initNEventsSignal = fSignal.Get('preselection')
for i in range(6):
var_signal[i].SetLineStyle(7)
var_signal[i].SetLineWidth(4)
var_signal[i].Scale(sigBoost * Lumi)
if sigBoost != 1:
sum = var_signal[i].Integral(0,
var_signal[i].GetNbinsX() + 1)
legendHistos[i].append(
(var_signal[i], '%sx%0.f (%.2f)' %
(signalSelection, sigBoost, var_signal[i].Integral(
0, varRange[0] + 1))))
else:
legendHistos[i].append(
(var_signal[i], '%s (%.2f)' %
(signalSelection, var_signal[i].Integral(
0, varRange[0] + 1))))
DrawSignal = True
else:
print '%s not supported, please use H260, H300 or H350' % signalSelection
scale_SS2OS = fit1.GetParameter(0)
scale_er_SS2OS = fit1.GetParError(0)
scale_relaxed2Tight = fit2.GetParameter(0)
scale_er_relaxed2Tight = fit2.GetParError(0)
QCDHistList_withScale[0].Scale(scale_SS2OS)
QCDHistList_withScale[1].Scale(scale_relaxed2Tight)
QCDHistList_withScale[2].Scale(scale_SS2OS)
QCDHistList_withScale[3].Scale(scale_relaxed2Tight)
QCDHistList_withScale[3].SetFillColor(r.TColor.GetColor(250, 202, 255))
QCDHistList_withScale[2].SetFillColor(r.TColor.GetColor(250, 202, 255))
QCDHistList_withScale[0].SetLineColor(r.TColor.GetColor(250, 202, 255))
QCDHistList_withScale[0].SetLineWidth(2)
QCDHistList_withScale[1].SetLineStyle(2)
QCDHistList_withScale[1].SetLineColor(r.TColor.GetColor(250, 202, 255))
QCDHistList_withScale[1].SetLineWidth(2)
var_background[1].Add(QCDHistList_withScale[3])
var_background[2].Add(QCDHistList_withScale[2])
legendHistos[1].append(
(QCDHistList_withScale[3],
'QCD (%.2f)' % QCDHistList_withScale[3].Integral(0, varRange[0] + 1)))
legendHistos[2].append(
(QCDHistList_withScale[2],
'QCD (%.2f)' % QCDHistList_withScale[2].Integral(0, varRange[0] + 1)))
allStacked = var_background[0].Clone()
QCDPredict = QCDHistList_withScale[1].Clone()
QCDPredict.SetLineStyle(1)
QCDPredict.SetLineWidth(1)
QCDPredict.SetLineColor(r.kBlack)
legendHistos[0].append((QCDPredict, 'QCD (%.0f, SF = %.3f)' %
(QCDPredict.Integral(0, varRange[0] + 1), SF)))
QCDPredict.SetFillColor(r.TColor.GetColor(250, 202, 255))
allStacked.Add(QCDPredict)
QCDHistList_withScale[1] = tool.addFakeTHStack(QCDHistList_withScale[1],
var_background[0])
QCDHistList_withScale[0] = tool.addFakeTHStack(QCDHistList_withScale[0],
var_background[0])
# var_data[1].Sumw2()
# MC_List[1].Sumw2()
for i in range(varRange[0]):
oldValue = var_data[2].GetBinContent(i + 1)
mcValue = MC_List[1].GetBinContent(i + 1)
if oldValue - mcValue > 0:
QCDDiff2.SetBinContent(i + 1, (oldValue - mcValue) / oldValue)
QCDDiff2.SetBinError(i + 1,
MC_List[1].GetBinError(i + 1) / oldValue)
print QCDDiff2.Integral(0, varRange[0] + 1)
QCDDiff = var_data[2].Clone()
QCDDiff_sub = QCDHistList_withScale[2].Clone() + MC_List[1].Clone()
QCDDiff.Divide(QCDDiff_sub)
QCDDiff_R2T = var_data[1].Clone()
QCDDiff_R2T_sub = QCDHistList_withScale[3].Clone() + MC_List[0].Clone()
QCDDiff_R2T.Divide(QCDDiff_R2T_sub)
legendPosition = (0.6, 0.7, 0.90, 0.88)
l = []
r.gROOT.SetBatch(True) # to suppress canvas pop-outs
if bTag == 'True':
titleName = '1 Medium 1 Loose b-tag'
fileName = 'bTag'
elif bTag == 'False':
titleName = ''
fileName = 'all'
elif bTag == '2M':
titleName = '2 Medium b-tags'
fileName = '2MbTag'
elif bTag == '1M':
titleName = '1 Medium b-tag'
fileName = '1MbTag'
elif bTag == '1M1NonM':
titleName = '1 Medium 1 Anti-Medium b-tag'
fileName = '1M1NonMbTag'
elif bTag == 'None':
titleName = '0 b-tag'
fileName = '0bTag'
KS1 = QCDHistList_4KS[0].KolmogorovTest(QCDHistList_4KS[2])
KS2 = QCDHistList_4KS[1].KolmogorovTest(QCDHistList_4KS[2])
ks_values = []
tmpHists = []
ks_values2 = []
tmpHists2 = []
nTimes = 10000
# for i in range(nTimes):
# tool.printProcessStatus(i, nTimes, processName = 'Making Sample Histograms')
# tmpHists.append(r.TH1F('tmpHist_%i' %(i),"", nBins, varRange[1], varRange[2]))
# tmpHists[i].FillRandom(QCDHistList_4KS[2], 100)
# ks_values.append(QCDHistList_4KS[0].KolmogorovTest(tmpHists[i]))
# tmpHists2.append(r.TH1F('tmpHist2_%i' %(i),"", nBins, varRange[1], varRange[2]))
# tmpHists2[i].FillRandom(QCDHistList_4KS[2], 100)
# ks_values2.append(QCDHistList_4KS[2].KolmogorovTest(tmpHists[i]))
print ''
print 'KS Test 1: %.3f' % KS1
print 'KS Test 2: %.3f' % KS2
fakeHist = r.TH1F()
fakeHist.SetLineColor(0)
usePUWeightName = ''
if usePU:
usePUWeightName = '_usePU'
psfile = '%s_%s_%s_%s_%s%s.pdf' % (varName, fileName, signalSelection,
region, relaxedRegionOption,
usePUWeightName)
c = r.TCanvas("c", "Test", 800, 900)
#ps = r.TPDF(psfile,112)
c.Divide(2, 3)
drawOpt = ''
QCDDiff.SetTitle(
'Data/Background OS Relaxed Events %s (%.1f fb^{-1}); %s; Data/Background'
% (titleName, Lumi, varName))
QCDDiff.SetMarkerStyle(8)
QCDDiff.SetMarkerSize(0.9)
QCDDiff.SetMaximum(4)
QCDDiff.SetMinimum(0)
QCDDiff_R2T.SetTitle(
'Data/Background OS Relaxed Events %s (%.1f fb^{-1}); %s; Data/Background'
% (titleName, Lumi, varName))
QCDDiff_R2T.SetMarkerStyle(8)
QCDDiff_R2T.SetMarkerSize(0.9)
QCDDiff_R2T.SetMaximum(4)
QCDDiff_R2T.SetMinimum(0)
pl_1 = r.TPad("pl_1", "pl_1", 0., 1, 0.5, 0.65)
pl_1_delta = r.TPad("pl_1_delta", "pl_1_delta", 0., 0.65, 0.5, 0.45)
pl_2 = r.TPad("pl_2", "pl_2", 0., 0.45, 0.5, 0.0)
pl_1.SetMargin(1, 1, 0, 1)
pl_1_delta.SetMargin(1, 1, 0.2, 0.05)
pr_1 = r.TPad("pr_1", "pr_1", 0.5, 1, 1., 0.65)
pr_1_delta = r.TPad("pr_1_delta", "pr_1_delta", 0.5, 0.65, 1.0, 0.45)
pr_2 = r.TPad("pr_2", "pr_2", 0.5, 0.45, 1.0, 0.0)
pr_1.SetMargin(1, 1, 0, 1)
pr_1_delta.SetMargin(1, 1, 0.2, 0.05)
pl_1.Draw()
pl_1_delta.Draw()
pl_2.Draw()
pr_1.Draw()
pr_1_delta.Draw()
pr_2.Draw()
pl_1.cd()
r.gPad.SetTicky()
signSelection, iso = conditions(1, region)
allStacked.SetMaximum(int(yMax))
allStacked.SetTitle(
'CMS Preliminary %.1f fb^{-1} at 8 TeV; %s; events / bin' %
(Lumi, varName))
allStacked.Draw()
var_data[0].Draw('PE same')
legendPosition = (0.43, 0.9 - 0.06 * len(legendHistos[0]), 0.87, 0.9)
l.append(
tool.setMyLegend(lPosition=legendPosition,
lHistList=getNewLegend(legendHistos[0], fakeHist)))
l[0].Draw('same')
var_signal[0].Draw('same')
pl_1_delta.cd()
r.gPad.SetTicky()
r.gPad.SetTickx()
pl_1_delta.SetGridy(1)
bkgEst = QCDHistList_withScale[1].Clone()
delta = var_data[0].Clone()
delta.Sumw2()
bkgEst.Sumw2()
delta.Divide(bkgEst)
delta.SetMinimum(0.5)
delta.SetMaximum(1.5)
delta.GetXaxis().SetTitle(varName)
delta.GetXaxis().SetLabelSize(0.07)
delta.GetXaxis().SetTitleSize(0.07)
delta.GetYaxis().SetLabelSize(0.07)
delta.GetYaxis().SetNdivisions(5, 5, 0)
delta.Draw()
pList = [pr_1, pl_2, pr_2]
for k in range(1, len(pList) + 1):
pList[k - 1].cd()
r.gPad.SetTicky()
if k > 1 and logY == 'True':
r.gPad.SetLogy()
signSelection, iso = conditions(k + 1, region)
var_background[k].SetTitle(
'%s %s Events %s (%.1f fb^{-1}); %s; events / bin' %
(signSelection, iso, titleName, Lumi, varName))
if k < 2:
var_background[k].SetMaximum(int(yMax))
else:
var_background[k].SetMaximum(max)
var_background[k].SetMinimum(0.01)
var_background[k].Draw()
if useData == 'True':
var_data[k].Draw('PE same')
legendPosition = (0.47, 0.9 - 0.06 * len(legendHistos[k]), 0.87, 0.9)
l.append(
tool.setMyLegend(lPosition=legendPosition,
lHistList=getNewLegend(legendHistos[k],
fakeHist)))
if k == 1:
ksLegend1 = tool.setMyLegend(
(0.2, 0.8, 0.5, 0.9),
[(QCDHistList_withScale[3], 'KS Test: %.3f' % KS1)])
ksLegend1.Draw('same')
if k == 2:
ksLegend2 = tool.setMyLegend(
(0.2, 0.8, 0.5, 0.9),
[(QCDHistList_withScale[2], 'KS Test: %.3f' % KS2)])
ksLegend2.Draw('same')
l[k].Draw('same')
var_signal[k].Draw('same')
pr_1_delta.cd()
r.gPad.SetTicky()
r.gPad.SetTickx()
pr_1_delta.SetGridy(1)
bkgEst2 = QCDHistList_withScale[3].Clone()
delta2 = var_data[1].Clone()
delta2.Sumw2()
bkgEst2.Sumw2()
delta2.Divide(bkgEst2)
delta2.SetMinimum(0.5)
delta2.SetMaximum(1.5)
delta2.GetXaxis().SetTitle(varName)
delta2.GetXaxis().SetLabelSize(0.07)
delta2.GetXaxis().SetTitleSize(0.07)
delta2.GetYaxis().SetLabelSize(0.07)
delta2.GetYaxis().SetNdivisions(5, 5, 0)
delta2.Draw()
c.Update()
c.Print('%s(' % psfile)
c.Clear()
p1 = r.TPad("p1", "p1", 0., 1, 1., 0.4)
p1_r = r.TPad("p1_r", "p1_r", 0., 0.39, 1., 0.06)
p1.SetMargin(1, 1, 0, 1)
p1_r.SetMargin(1, 1, 0.2, 1)
p1.Draw()
p1_r.Draw()
p1.cd()
r.gPad.SetTicky()
allStacked.Draw()
var_data[0].Draw('PE same')
l[0].Draw('same')
var_signal[0].Draw('same')
p1_r.cd()
r.gPad.SetTicky()
r.gPad.SetTickx()
p1_r.SetGridy(1)
delta.Draw()
c.Print('%s)' % psfile)
print "Plot saved at %s" % (psfile)
c.Close()