canvas.SetLogy(True)
canvas.legend.setPosition(0.6, 0.7, 0.95, 0.9)
canvas.addStacked(bmet,
title='Background',
color=r.TColor.GetColor(0x55, 0x44, 0xff),
idx=-1)
canvas.addStacked(mcmet,
title='#gamma + jet MC',
color=r.TColor.GetColor(0xff, 0xaa, 0xcc),
idx=-1)
canvas.addObs(dmet, title='Data')
canvas.xtitle = canvas.obsHistogram().GetXaxis().GetTitle()
canvas.ytitle = canvas.obsHistogram().GetYaxis().GetTitle()
canvas.Update(logy=True)
canvas.printWeb('monophoton/gjetsTFactor', 'distributions' + region)
###########################################
####### Transfer Factors ##################
###########################################
methods = [('Data', gmets), ('MC', mcmets)]
scanvas = SimpleCanvas(lumi=lumi)
else:
writeHist(obshist)
formatHist(obshist, vardef)
# Take care of masking
if vardef.blind is not None:
for i in range(1, obshist.GetNbinsX()+1):
binCenter = obshist.GetBinCenter(i)
if vardef.fullyBlinded() or (binCenter > vardef.blind[0] and (vardef.blind[1] == 'inf' or binCenter < vardef.blind[1])):
obshist.SetBinContent(i, 0.)
obshist.SetBinError(i, 0.)
if vardef.name == 'count' or vardef.name == args.bbb or vardef.name == args.chi2:
counters['data_obs'] = obshist
elif plotDir and not vardef.fullyBlinded():
canvas.addObs(obshist, title = plotConfig.obs.title)
if vardef.name == 'count':
printCounts(counters, plotConfig)
elif vardef.name == args.bbb:
print 'Bin-by-bin yield for variable', args.bbb
printBinByBin(counters, plotConfig)
elif vardef.name == args.chi2:
print 'Chi2 for variable', args.chi2
printChi2(counters, plotConfig)
if plotDir and vardef.name != 'count':
canvas.xtitle = obshist.GetXaxis().GetTitle()
canvas.ytitle = obshist.GetYaxis().GetTitle()
for probeEtaCut in probeEtaCuts:
finalCuts = allCuts + [probeEtaCut[0]]
cutString = ' && '.join(['(%s)' % c for c in finalCuts])
print cutString
label = recoilCut[1] + '_' + probeEtaCut[1]
canvas.Clear()
canvas.legend.Clear()
canvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
dataName = 'dataHist_' + label
dataHist = r.TH1D(dataName, '', 20, 0., 2.)
dataHist.Sumw2()
dataTree.Draw(varString + '>>' + dataName,
'weight * (' + cutString + ')', 'goff')
canvas.addObs(dataHist, title='Data')
for sample, color, mcTree in mcTrees:
mcName = sample + '_' + label
mcHist = r.TH1D(mcName, '', 20, 0., 2.)
mcHist.Sumw2()
mcTree.Draw(varString + '>>' + mcName,
str(lumi) + ' * weight * (' + cutString + ')', 'goff')
canvas.addStacked(mcHist, title=sample, color=color)
canvas.ylimits = (0.001, -1.)
canvas.printWeb('monophoton/phoMet', 'ratio_' + label, logy=True)
canvas.Clear()
canvas.legend.Clear()
canvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
dataName = 'dataHist'+skim
dataHist = varDef.makeHist(dataName)
if varDef.unit != '':
dataHist.GetXaxis().SetTitle(varDef.title + ' (' + varDef.unit +')')
else:
dataHist.GetXaxis().SetTitle(varDef.title)
dataCutString = cutString + ' && !(run > %s)' % config.runCutOff
dataTree.Draw(varDef.expr+'>>'+varDef.name+'-'+dataName, 'weight * ('+cutString+')', 'goff')
print dataHist.Integral()
canvas.addObs(dataHist, title = 'Data '+skimm)
for sample, color, mcTree in mcTrees:
mcName = sample+skimm
mcHist = varDef.makeHist(mcName)
mcTree.Draw(varDef.expr+'>>'+varDef.name+'-'+mcName, str(lumi)+' * weight * ('+cutString+')', 'goff')
canvas.addStacked(mcHist, title = sample, color = color)
# canvas.ylimits = (0.001, -1.)
canvas.xtitle = varDef.title
canvas.ytitle = 'Events'
canvas.printWeb('monophoton/phoMet/'+skim, skimm[:3]+'_'+jetsCut[0]+'_'+sign+'_'+varDef.name, logy = False)
canvas.cd()
canvas.Clear()
canvas.legend.Clear()
canvas.ylimits = (0.002, 2500)
canvas.SetLogy(True)
canvas.legend.setPosition(0.6, 0.7, 0.95, 0.9)
canvas.addStacked(bmet, title = 'Background', color = r.TColor.GetColor(0x55, 0x44, 0xff), idx = -1)
canvas.addStacked(mcmet, title = '#gamma + jet MC', color = r.TColor.GetColor(0xff, 0xaa, 0xcc), idx = -1)
canvas.addObs(dmet, title = 'Data')
canvas.xtitle = canvas.obsHistogram().GetXaxis().GetTitle()
canvas.ytitle = canvas.obsHistogram().GetYaxis().GetTitle()
canvas.Update(logy = True)
canvas.printWeb('monophoton/gjetsTFactor', 'distributions'+region)
###########################################
####### Transfer Factors ##################
###########################################
methods = [ ('Data', gmets), ('MC', mcmets) ]
scanvas = SimpleCanvas(lumi = lumi)
if graphic:
formatHist(shist, plotdef)
title = sspec.title
if sspec.group.scale != 1. and group.scale != 'data':
title += (' #times %.1f' % sspec.group.scale)
canvas.addSignal(shist,
title=title,
color=sspec.color,
drawOpt=drawOpt)
else:
counters[sspec.name] = shist
if obshist:
if graphic:
formatHist(obshist, plotdef)
canvas.addObs(obshist, title=plotConfig.obs.title)
else:
counters['data_obs'] = obshist
if plotdef.name == 'count':
printCounts(counters, plotConfig)
elif plotdef.name == args.bbb:
printBinByBin(counters, plotdef, plotConfig)
elif plotdef.name == args.chi2:
printChi2(counters, plotdef, plotConfig)
else:
if args.asimov:
plotdef.name += args.asimov.capitalize()
printCanvas(canvas, plotdef, plotConfig, plotDir)
data.Add('/mnt/hadoop/scratch/yiiyama/monophoton/skim/sph-16*-m_tpeg.root')
outputFile.cd()
data.Draw('probes.sieie>>hdata', selection, 'goff')
hmc.Scale(hdata.GetSumOfWeights() / hmc.GetSumOfWeights())
ratio = hdata.Clone('ratio')
ratio.Divide(hmc)
line = ROOT.TF1('line', '[0] + [1] * x', 0.0085, 0.0105)
line.SetParameters(1.5, -100.)
ratio.Fit(line)
outputFile.cd()
hmc.Write()
hdata.Write()
ratio.Write()
line.Write('fit')
# visualize
canvas = DataMCCanvas(lumi=sum(s.lumi
for s in allsamples.getmany('sph-16*-m')))
canvas.legend.setPosition(0.7, 0.7, 0.9, 0.9)
canvas.addStacked(hmc, title='MC', color=ROOT.kBlue, style=3003)
canvas.addObs(hdata, title='Data')
canvas.printWeb('purity', 'sieie_ratio', logy=False)
for recoilCut in recoilCuts:
allCuts = cuts + [recoilCut[0]]
for probeEtaCut in probeEtaCuts:
finalCuts = allCuts + [probeEtaCut[0]]
cutString = " && ".join(["(%s)" % c for c in finalCuts])
print cutString
label = recoilCut[1] + "_" + probeEtaCut[1]
canvas.Clear()
canvas.legend.Clear()
canvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
dataName = "dataHist_" + label
dataHist = r.TH1D(dataName, "", 20, 0.0, 2.0)
dataHist.Sumw2()
dataTree.Draw(varString + ">>" + dataName, "weight * (" + cutString + ")", "goff")
canvas.addObs(dataHist, title="Data")
for sample, color, mcTree in mcTrees:
mcName = sample + "_" + label
mcHist = r.TH1D(mcName, "", 20, 0.0, 2.0)
mcHist.Sumw2()
mcTree.Draw(varString + ">>" + mcName, str(lumi) + " * weight * (" + cutString + ")", "goff")
canvas.addStacked(mcHist, title=sample, color=color)
canvas.ylimits = (0.001, -1.0)
canvas.printWeb("monophoton/phoMet", "ratio_" + label, logy=True)
title='Fake E_{T}^{miss}',
color=ROOT.kRed,
drawOpt='HIST')
canvas.addStacked(sig, title='H(125)', color=ROOT.kBlue, drawOpt='HIST')
bkgTrue.Scale(1., 'width')
fakeTrue.Scale(fakeNorm / fakeTrue.GetSumOfWeights(), 'width')
sigTrue.Scale(sigScale, 'width')
canvas.addSignal(bkgTrue,
title='True SM Bkgd.',
color=ROOT.kGreen + 2,
drawOpt='HIST')
canvas.addSignal(fakeTrue,
title='True Fake E_{T}^{miss}',
color=ROOT.kRed + 2,
drawOpt='HIST')
canvas.addSignal(sigTrue,
title='True H(125)',
color=ROOT.kBlue + 2,
drawOpt='HIST')
canvas.addObs(data, drawOpt='EP')
canvas.title = '#sigma#timesBR = %.2f, N_{fake} = %.0f' % (originalMu *
sigScale, fakeNorm)
canvas.xtitle = 'm_{T} (GeV)'
canvas.ytitle = 'Events / GeV'
canvas.printWeb('monophoton/fakemet', name, logy=False)
