bmets.append(bmet)
gmets.append(gmet)
mcmets.append(mcmet)
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)
postscale = 1.
# make background histograms
# loop over groups with actual distributions
bkgTotal = vardef.makeHist('bkgtotal')
for group in [g for g in plotConfig.bkgGroups if len(g.samples) != 0]:
hist = groupHist(group, vardef, plotConfig, args.skimDir, lumi = lumi, postscale = postscale, outFile = outFile)
if not args.saveTrees:
bkgTotal.Add(hist)
if vardef.name == 'count' or vardef.name == args.bbb or vardef.name == args.chi2:
counters[group.name] = hist
elif plotDir:
canvas.addStacked(hist, title = group.title, color = group.color, drawOpt = drawOpt)
# formatted histograms added to bkgTotal
unformatHist(bkgTotal, vardef)
# then over groups without distributions (no samples but count set)
# probably doesn't work in trees
for group in [g for g in plotConfig.bkgGroups if len(g.samples) == 0]:
# cannot use lumi because cross section is not set
hist = groupHist(group, vardef, plotConfig, template = bkgTotal, postscale = postscale, outFile = outFile)
if vardef.name == 'count' or vardef.name == args.bbb or vardef.name == args.chi2:
counters[group.name] = hist
# plot signal distributions for sensitive variables
if isSensitive or outFile:
dmets.append(dmet)
bmets.append(bmet)
gmets.append(gmet)
mcmets.append(mcmet)
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 ##################
###########################################
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)
for group in plotConfig.bkgGroups:
ghist = inDir.Get(group.name + '_syst')
if group.scale == 'data':
# special case - scaling this group to data (for shape comparison)
ghist.Scale(fillToDataScale)
if graphic:
formatHist(ghist, plotdef)
title = group.title
if group.scale == 'data':
title += ' (norm. to obs)'
elif group.scale != 1.:
title += (' #times %.1f' % group.scale)
canvas.addStacked(ghist,
title=title,
color=group.color,
drawOpt=drawOpt)
else:
counters[group.name] = ghist
# background total used for uncertainty display
bkgTotal = inDir.Get('bkgtotal_syst')
if graphic:
formatHist(bkgTotal, plotdef)
# plot signal distributions for sensitive plots
if isSensitive:
for sspec in plotConfig.signalPoints:
shist = inDir.Get('samples/' + sspec.name + '_' +
plotConfig.name)
prescale = 1
postscale = 1.
# make background histograms
# loop over groups with actual distributions
bkgTotal = vardef.makeHist('bkgtotal')
for group in [g for g in plotConfig.bkgGroups if len(g.samples) != 0]:
hist = groupHist(group, vardef, plotConfig, args.skimDir, postscale = postscale, outFile = outFile)
bkgTotal.Add(hist)
if vardef.name == 'count' or vardef.name == args.bbb:
counters[group.name] = hist
elif plotDir:
canvas.addStacked(hist, title = group.title, color = group.color)
# formatted histograms added to bkgTotal
unformatHist(bkgTotal, vardef)
# then over groups without distributions (no samples but count set)
for group in [g for g in plotConfig.bkgGroups if len(g.samples) == 0]:
hist = groupHist(group, vardef, plotConfig, template = bkgTotal, postscale = postscale, outFile = outFile)
if vardef.name == 'count' or vardef.name == args.bbb:
counters[group.name] = hist
# plot signal distributions for sensitive variables
if isSensitive or outFile:
usedPoints = []
print dataHist.Integral()
canvas.addObs(dataHist, title='Data')
for region, cut, color, bkgTree in bkgTrees:
bkgHist = varDef.makeHist(varDef.name + '-' + region)
print bkgHist.GetName()
fullCut = cutString + ' && ' + cut
bkgTree.Draw(varDef.expr + '>>' + varDef.name + '-' + region,
'weight * (' + cutString + ')', 'goff')
if region == 'offtimeIso':
bkgHist.Scale(23.9 / bkgHist.Integral())
elif region == 'halo':
bkgHist.Scale(1. / 3000.)
print bkgHist.Integral()
canvas.addStacked(bkgHist, title=region, color=color)
for sample, color, mcTree in mcTrees:
mcHist = varDef.makeHist(varDef.name + '-' + sample)
print mcHist.GetName()
mcTree.Draw("1.02 * " + varDef.expr + '>>' + varDef.name + '-' + sample,
str(lumi) + ' * weight * (' + cutString + ')', 'goff')
print mcHist.Integral()
canvas.addStacked(mcHist, title=sample, color=color)
# canvas.ylimits = (0.001, -1.)
canvas.xtitle = varDef.title
canvas.ytitle = 'Events'
canvas.printWeb('monophoton/pes/', varDef.name + "02")
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)
bkg = source.Get('shapes_fit_s/gghg/total_background')
fake = source.Get('shapes_fit_s/gghg/fakemet')
sig = source.Get('shapes_fit_s/gghg/total_signal')
data = source.Get('shapes_fit_s/gghg/data')
bkg.Add(fake, -1.)
plotsSource = ROOT.TFile.Open(plots)
bkgTrue = plotsSource.Get(dist + '/bkgtotal')
fakeTrue = plotsSource.Get(dist + '/fakemet')
sigTrue = plotsSource.Get(dist + '/samples/' + signal + '_' + region)
canvas.legend.setPosition(0.4, 0.6, 0.9, 0.9)
canvas.addStacked(bkg, title='SM Bkgd.', color=ROOT.kGray, drawOpt='HIST')
canvas.addStacked(fake,
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,
