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)
tfacts = []
for method, hists in methods:
tname = 'tfact' + method
tfact = hists[1].Clone(tname)
tfact.Divide(hists[0])
yaxis.SetY1(simple.canvas.GetBottomMargin())
yaxis.SetY2(1. - simple.canvas.GetTopMargin())
simple.canvas.IsA().Destructor(simple.canvas)
simple.canvas = cnv
simple._needUpdate = False
simple.printWeb(plotDir, vardef.name)
if addLinear:
simple.ylimits = (0., -1.)
simple.minimum = -1.
vardef.ymax = -1.
simple._needUpdate = True
simple.printWeb(plotDir, vardef.name + 'Linear', logy = False)
# cleanup the mess
for obj in garbage:
obj.IsA().Destructor(obj)
cnv.IsA().Destructor(cnv)
else:
canvas.printWeb(plotDir, vardef.name)
if addLinear:
canvas.ylimits = (0., -1.)
canvas.minimum = -1.
vardef.ymax = -1.
canvas._needUpdate = True
canvas.printWeb(plotDir, vardef.name + 'Linear', logy = False)
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)
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")
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)
tfacts = []
for method, hists in methods:
tname = 'tfact'+method
tfact = hists[1].Clone(tname)
tfact.Divide(hists[0])
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'
if varDef.logy is None:
logy = False
else:
logy = varDef.logy
canvas.printWeb('monophoton/zjets/'+skim, skimm[:3]+'_'+jetsCut[0]+'_Met'+metValue+'_'+varDef.name, logy = logy)
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)
dataHist.GetXaxis().SetTitle(varDef.title)
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(smuLumi) + ' * weight * (' + cutString + ')',
'goff')
canvas.addStacked(mcHist, title=sample, color=color)
# canvas.ylimits = (0.001, -1.)
canvas.xtitle = varDef.title
canvas.ytitle = 'Events'
if varDef.logy is None:
logy = False
else:
logy = varDef.logy
canvas.printWeb('monophoton/zjets/' + skim,
skimm[:3] + '_' + jetsCut[0] + '_Met' +
metValue + '_' + varDef.name,
logy=logy)
