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:
usedPoints = []
for sspec in plotConfig.signalPoints:
usedPoints.append(sspec.name)
hist = groupHist(sspec.group, vardef, plotConfig, args.skimDir, samples = [sspec.name], name = sspec.name, lumi = lumi, outFile = outFile)
if vardef.name == 'count' or vardef.name == args.bbb or vardef.name == args.chi2:
counters[sspec.name] = hist
elif plotDir:
canvas.addSignal(hist, title = sspec.title, color = sspec.color, drawOpt = drawOpt)
# write out all signal distributions if asked for
if args.allSignal:
for group in plotConfig.sigGroups:
for sample in allsamples.getmany(group.samples):
if sample.name not in usedPoints:
usedPoints.append(sample.name)
groupHist(group, vardef, plotConfig, args.skimDir, samples = [sample.name], name = sample.name, lumi = lumi, outFile = outFile)
if args.asimov == '':
print 'data_obs'
if args.saveTrees:
obshist = []
else:
obshist = vardef.makeHist('data_obs', outDir = outFile)
scanvas.legend.apply(tname, tfact)
scanvas.addHistogram(tfact, drawOpt='EP')
scanvas.printWeb('monophoton/gjetsTFactor', 'tfactor' + method)
canvas.Clear()
canvas.legend.Clear()
canvas.SetLogy(True)
canvas.ylimits = (0.01, 2.5)
canvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
canvas.addObs(tfacts[0], 'Data')
canvas.addSignal(tfacts[1], title='MC', color=r.kRed, idx=-1)
canvas.addStacked(tfacts[1], title='MC', idx=-1)
canvas.printWeb('monophoton/gjetsTFactor', 'tfactorRatio', ymax=2.5)
###########################################
####### Plain Root Attempt ################
###########################################
expo = r.TF1("SingleExpo", "[0] * TMath::Exp([1] * x) + [2]", 0., 600.)
expo.SetParameters(1., -0.1, 0.)
expo.SetParLimits(1, -10., 0.)
expo.SetParLimits(2, 0., 10.)
dexpo = r.TF1("Expo", "[0] * TMath::Exp([2] * x) + [1] * TMath::Exp([3] * x)",
0., 600.)
scanvas.legend.apply(tname, tfact)
scanvas.addHistogram(tfact, drawOpt = 'EP')
scanvas.printWeb('monophoton/gjetsTFactor', 'tfactor'+method)
canvas.Clear()
canvas.legend.Clear()
canvas.ylimits = (0.01, 2.5)
canvas.SetLogy(True)
canvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
canvas.addObs(tfacts[0], 'Data')
canvas.addSignal(tfacts[1], title = 'MC', color = r.kRed, idx = -1)
canvas.addStacked(tfacts[1], title = 'MC', idx = -1)
canvas.printWeb('monophoton/gjetsTFactor', 'tfactorRatio')
###########################################
####### Plain Root Attempt ################
###########################################
expo = r.TF1("SingleExpo", "[0] * TMath::Exp([1] * x) + [2]", 0., 600.)
expo.SetParameters(1., -0.1, 0.)
expo.SetParLimits(1, -10., 0.)
expo.SetParLimits(2, 0., 10.)
dexpo = r.TF1("Expo", "[0] * TMath::Exp([2] * x) + [1] * TMath::Exp([3] * x)", 0., 600.)
dexpo.SetParameters(10., 0.1, -0.1, -0.1)
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)
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)
if vardef.name == 'count' or vardef.name == args.bbb:
counters[group.name] = hist
# plot signal distributions for sensitive variables
if isSensitive or outFile:
usedPoints = []
for sspec in plotConfig.signalPoints:
hist = groupHist(sspec.group, vardef, plotConfig, args.skimDir, samples = [sspec.name], name = sspec.name, postscale = postscale, outFile = outFile)
usedPoints.append(sspec.name)
if vardef.name == 'count' or vardef.name == args.bbb:
counters[sspec.name] = hist
elif plotDir:
canvas.addSignal(hist, title = sspec.title, color = sspec.color)
# write out all signal distributions if asked for
if isSensitive and args.allSignal:
for group in plotConfig.sigGroups:
for sample in allsamples.getmany(group.samples):
if sample.name not in usedPoints:
groupHist(group, vardef, plotConfig, args.skimDir, samples = [sample.name], name = sample.name, postscale = postscale, outFile = outFile)
usedPoints.append(sample.name)
obshist = vardef.makeHist('data_obs', outDir = outFile)
for sname in plotConfig.obs.samples:
obshist.Add(getHist(sname, allsamples[sname], plotConfig, vardef, args.skimDir, prescale = prescale, outDir = sampleDir))
writeHist(obshist)
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,
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)'
