# if samp == 'Down':
rcanvas.ylimits = (0., -1.)
# else:
# rcanvas.ylimits = (0., 0.05)
rcanvas.SetLogy(False)
rcanvas.legend.add(tname,
title='transfer factor',
lcolor=ROOT.kBlack,
lwidth=2)
rcanvas.legend.add(tname + 'Syst',
title='impurity #pm 1#sigma',
lcolor=ROOT.kBlack,
lwidth=2,
lstyle=ROOT.kDashed)
rcanvas.legend.apply(tname, tfact)
rcanvas.legend.apply(tname + 'Syst', tfactUp)
rcanvas.legend.apply(tname + 'Syst', tfactDown)
iNom = rcanvas.addHistogram(tfact, drawOpt='HIST')
iUp = rcanvas.addHistogram(tfactUp, drawOpt='HIST')
iDown = rcanvas.addHistogram(tfactDown, drawOpt='HIST')
rcanvas.printWeb('monophoton/hadronTFactor' + suffix,
'tfactor' + confName,
hList=[iUp, iDown, iNom],
rList=[iNom, iUp, iDown])
fstyle=3003,
lwidth=2)
canvas.legend.add('mc', '', opt='LF', color=ROOT.kBlue, fstyle=3003, lwidth=2)
canvas.legend.apply('data', dataDist)
dataClone = dataDist.Clone('points')
dataClone.SetFillStyle(0)
dataClone.SetMarkerStyle(1)
dataClone.SetMarkerColor(ROOT.kBlack)
dataClone.SetLineWidth(2)
dataClone.SetLineColor(ROOT.kBlack)
for _, scenarioName in mcConfs:
mcDist = outputFile.Get(scenarioName)
canvas.legend.entries['mc'].SetLabel(scenarioName)
canvas.legend.apply('mc', mcDist)
imc = canvas.addHistogram(mcDist)
idata = canvas.addHistogram(dataDist)
iclone = canvas.addHistogram(dataClone, drawOpt='P')
canvas.printWeb('puweight',
scenarioName,
logy=False,
hList=[imc, idata],
rList=[imc, iclone])
canvas.Clear()
outputFile.Close()
effError = eff * math.sqrt( (passes[1]/passes[0])**2 + (totals[1]/totals[0])**2 + 2*corr*(passes[1]/passes[0])*(totals[1]/passes[0]) )
dataEff.SetPoint(iB, center, eff)
dataEff.SetPointError(iB, exl, exh, effError, effError)
sf = eff / mceffs[0]
sfErrLow = effError / mceffs[0]
sfErrHigh = effError / mceffs[0]
gSF.SetPoint(iB, center, sf)
gSF.SetPointError(iB, exl, exh, sfErrLow, sfErrHigh)
print sf, sfErrLow, sfErrLow / sf
rcanvas.legend.add("mc", title = "MC", mcolor = r.kRed, lcolor = r.kRed, lwidth = 2)
rcanvas.legend.apply("mc", mcEff)
rcanvas.addHistogram(mcEff, drawOpt = 'EP')
rcanvas.legend.add("data", title = "Data", lcolor = r.kBlack, lwidth = 2)
rcanvas.legend.apply("data", dataEff)
rcanvas.addHistogram(dataEff, drawOpt = 'EP')
rcanvas.ylimits = (0.0, 1.1)
rcanvas.ytitle = 'Photon Efficiency'
rcanvas.xtitle = 'E_{T}^{#gamma} (GeV)'
plotName = "efficiency_"+str(loc)+"_"+str(pid)
rcanvas.printWeb('purity/'+s.Version+'/ScaleFactors', plotName, logy = False)
canvas.legend.add(loc+'-'+pid, title = loc+'-'+pid, color = r.kBlack, lwidth = 2)
canvas.legend.apply(loc+'-'+pid, gSF)
canvas.addHistogram(gSF, drawOpt = 'EP')
mcpdf.plotOn(frame, ROOT.RooFit.LineColor(ROOT.kRed), ROOT.RooFit.LineStyle(ROOT.kDotted))
frame.SetTitle("min#Delta#phi(j, E_{T}^{miss}) < 0.5")
# frame.GetXaxis().SetTitle('E_{T}^{miss} (GeV)')
frame.GetXaxis().SetTitle("")
frame.GetXaxis().SetTitleSize(0.0)
frame.GetXaxis().SetLabelSize(0.0)
frame.GetYaxis().SetLabelSize(0.0)
frame.GetYaxis().SetTickSize(0.0)
canvas.xtitle = "E_{T}^{miss} (GeV)"
canvas.legend.add("data", title="Data", mstyle=8, color=ROOT.kBlack, opt="LP")
canvas.legend.add("fit", title="Fit", color=ROOT.kBlue, lwidth=2, opt="L")
canvas.legend.add("gjets", title="#gamma+jets", color=ROOT.kGreen, lstyle=ROOT.kDashed, lwidth=2, opt="L")
canvas.legend.add("mcpdf", title="#gamma+jets raw", color=ROOT.kRed, lstyle=ROOT.kDotted, lwidth=2, opt="L")
canvas.addHistogram(frame, clone=False, drawOpt="")
canvas.Update()
fitcurve = frame.findObject("model_Norm[met]")
rawcurve = frame.findObject("mcpdf_Norm[met]")
rdata = ROOT.TGraphErrors(dmet.GetNbinsX())
for iP in range(rdata.GetN()):
x = dmet.GetXaxis().GetBinCenter(iP + 1)
norm = fitcurve.interpolate(x)
rdata.SetPoint(iP, x, dmet.GetBinContent(iP + 1) / norm)
rdata.SetPointError(iP, 0.0, dmet.GetBinError(iP + 1) / norm)
rdata.SetMarkerStyle(8)
rdata.SetMarkerColor(ROOT.kBlack)
loSumw = loTree.GetEntries()
for vname, title, binning, cond, logy in plots:
if type(binning) is tuple:
hlo = ROOT.TH1D('lo', title, *binning)
hnlo = ROOT.TH1D('nlo', title, *binning)
else:
hlo = ROOT.TH1D('lo', title,
len(binning) - 1, array.array('d', binning))
hnlo = ROOT.TH1D('nlo', title,
len(binning) - 1, array.array('d', binning))
nloTree.Draw(vname + '>>nlo',
'weight * ((%s) && (%s))' % (baseline, cond), 'goff')
hnlo.Scale(nloSample.crosssection / nloSumw, 'width')
canvas.addHistogram(hnlo)
if lo is not None:
loTree.Draw(vname + '>>lo', '(%s) && (%s)' % (baseline, cond),
'goff')
hlo.Scale(loSample.crosssection / loSumw, 'width')
canvas.addHistogram(hlo)
canvas.applyStyles()
canvas.printWeb('kfactor', name + '_' + vname, logy=logy)
canvas.Clear()
hnlo.Delete()
hlo.Delete()
def plotFit(mass,
targHist,
model,
dataType,
suffix,
bkgModel='nombkgModel',
hmcbkg=None,
alt='',
plotName=''):
global canvas
if canvas is None:
canvas = RatioCanvas(lumi=lumi, sim=(dataType == 'mc'))
canvas.Clear(full=True)
canvas.titlePave.SetX2NDC(0.5)
canvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
canvas.legend.add('obs',
title='Observed',
opt='LP',
color=ROOT.kBlack,
mstyle=8)
canvas.legend.add('fit',
title='Fit',
opt='L',
lcolor=ROOT.kBlue,
lwidth=2,
lstyle=ROOT.kSolid)
canvas.legend.add('bkg',
title='Bkg component',
opt='L',
lcolor=ROOT.kGreen,
lwidth=2,
lstyle=ROOT.kDashed)
if hmcbkg:
canvas.legend.add('mcbkg',
title='Bkg (MC truth)',
opt='LF',
lcolor=ROOT.kRed,
lwidth=1,
fcolor=ROOT.kRed,
fstyle=3003)
if targHist.sumEntries() > 500.:
plotBinning = plotBinningT
else:
plotBinning = plotBinningT2
frame = mass.frame()
targHist.plotOn(frame, ROOT.RooFit.Binning(*plotBinning))
model.plotOn(frame)
model.plotOn(frame, ROOT.RooFit.Components(bkgModel + '_' + suffix),
ROOT.RooFit.LineStyle(ROOT.kDashed),
ROOT.RooFit.LineColor(ROOT.kGreen))
frame.SetTitle('')
frame.SetMinimum(0.)
canvas.addHistogram(frame, clone=True, drawOpt='')
if hmcbkg:
htruth = hmcbkg.Rebin(hmcbkg.GetNbinsX() / plotBinning[0], 'truth')
canvas.legend.apply('mcbkg', htruth)
canvas.addHistogram(htruth)
canvas.rlimits = (-2., 2.)
canvas.Update(rList=[], logy=False)
frame.Print()
# adding ratio pad
fitcurve = frame.findObject(model.GetName() + '_Norm[mass]')
hresid = targHist.createHistogram('residual', mass,
ROOT.RooFit.Binning(*plotBinning))
rdata = ROOT.TGraphErrors(hresid.GetNbinsX())
for iP in range(rdata.GetN()):
x = hresid.GetXaxis().GetBinCenter(iP + 1)
nData = hresid.GetBinContent(iP + 1)
statErr = hresid.GetBinError(iP + 1)
nFit = fitcurve.interpolate(x)
if statErr > 0.:
rdata.SetPoint(iP, x, (nData - nFit) / statErr)
else:
rdata.SetPoint(iP, x, (nData - nFit))
# rdata.SetPointError(iP, 0., dmet.GetBinError(iP + 1) / norm)
rdata.SetMarkerStyle(8)
rdata.SetMarkerColor(ROOT.kBlack)
rdata.SetLineColor(ROOT.kBlack)
canvas.ratioPad.cd()
canvas.rtitle = '(data - fit) / #sigma_{data}'
rframe = ROOT.TH1F('rframe', '', 1, *plotBinning[1:])
rframe.GetYaxis().SetRangeUser(-2., 2.)
rframe.Draw()
line = ROOT.TLine(plotBinning[1], 0., plotBinning[2], 0.)
line.SetLineWidth(2)
line.SetLineColor(ROOT.kBlue)
line.Draw()
rdata.Draw('EP')
canvas._needUpdate = False
if not plotName:
plotName = 'fit_' + dataType + '_' + suffix
if alt:
plotName += '_' + alt
canvas.printWeb(plotDir, plotName, logy=False)
rframe.Delete()
if hmcbkg:
htruth.Delete()
hresid.Delete()
canvas.addHistogram(fptDown, drawOpt = 'HIST')
canvas.addHistogram(hpt, drawOpt = 'HIST')
canvas.ylimits = (1.0, 2500000.)
canvas.SetLogy(True)
canvas.printWeb('monophoton/hadronTFactor', 'distributions'+samp)
rcanvas.Clear()
rcanvas.legend.Clear()
if samp == 'Down':
rcanvas.ylimits = (0., -1.)
else:
rcanvas.ylimits = (0., 0.15)
rcanvas.SetLogy(False)
rcanvas.legend.add(tname, title = 'transfer factor', lcolor = ROOT.kBlack, lwidth = 2)
rcanvas.legend.add(tname+'Syst', title = 'impurity #pm 1#sigma', lcolor = ROOT.kBlack, lwidth = 2, lstyle = ROOT.kDashed)
rcanvas.legend.apply(tname, tfact)
rcanvas.legend.apply(tname+'Syst', tfactUp)
rcanvas.legend.apply(tname+'Syst', tfactDown)
iNom = rcanvas.addHistogram(tfact, drawOpt = 'HIST')
iUp = rcanvas.addHistogram(tfactUp, drawOpt = 'HIST')
iDown = rcanvas.addHistogram(tfactDown, drawOpt = 'HIST')
rcanvas.printWeb('monophoton/hadronTFactor', 'tfactor'+samp, hList = [iUp, iDown, iNom], rList = [iUp, iDown, iNom] )
canvas.legend.apply('prefit', prefitUncRatio, opt = 'F')
canvas.legend.apply('postfit', postfitTotal, opt = 'L')
canvas.legend.apply('postfit', postfitUnc, opt = 'F')
canvas.legend.apply('postfit', postfitRatio, opt = 'LP')
canvas.legend.apply('postfit', postfitUncRatio, opt = 'F')
if postfitSub:
canvas.legend.apply('subdom', postfitSub)
# reuse dataHist for unity line in ratio pad
dataHist.SetLineColor(ROOT.kBlack)
# dataHist.SetLineStyle(ROOT.kDashed)
dataHist.SetLineWidth(2)
iObs = canvas.addHistogram(obs)
iPreUnc = canvas.addHistogram(prefitUnc, drawOpt = 'E2')
iPre = canvas.addHistogram(prefitTotal, drawOpt = 'HIST')
iPostUnc = canvas.addHistogram(postfitUnc, drawOpt = 'E2')
iPost = canvas.addHistogram(postfitTotal, drawOpt = 'HIST')
iLine = canvas.addHistogram(dataHist, drawOpt = 'HIST')
iPreUncRatio = canvas.addHistogram(prefitUncRatio, drawOpt = 'E2')
iPreRatio = canvas.addHistogram(prefitRatio, drawOpt = 'LP')
iPostUncRatio = canvas.addHistogram(postfitUncRatio, drawOpt = 'E2')
iPostRatio = canvas.addHistogram(postfitRatio, drawOpt = 'LP')
if postfitSub:
iSub = canvas.addHistogram(postfitSub)
hList = [iSub, iPreUnc, iPre, iPostUnc, iPost, iObs]
nominal.GetXaxis().SetBinLabel(ibin + 1, binlabels[ibin])
measured.SetPoint(ibin, (binning[ibin] + binning[ibin + 1]) * 0.5,
dsigma * cent[0])
measured.SetPointEYhigh(ibin, dsigma * up[0])
measured.SetPointEYlow(ibin, dsigma * down[0])
nominalLine = nominal.Clone('line')
canvas.legend.apply('nominal', nominalLine)
nominalLine.SetFillStyle(0)
canvas.legend.apply('nominal', nominal)
nominal.SetMaximum(nominal.GetMaximum() * 1.2)
canvas.legend.apply('obs', measured)
measured.SetTitle('')
canvas.addHistogram(nominal, drawOpt='E2')
canvas.addHistogram(nominalLine, drawOpt='HIST')
canvas.addHistogram(measured, drawOpt='EP')
canvas.xtitle = nominal.GetXaxis().GetTitle()
canvas.ytitle = nominal.GetYaxis().GetTitle()
canvas.rtitle = 'Fit / prediction'
canvas.printWeb('hww/20190205', 'sigma_%s' % observable, logy=False)
canvas.Clear()
nominalLine.Delete()
nominal.Delete()
model.plotOn(frame)
model.plotOn(frame, ROOT.RooFit.Components('gjets'), ROOT.RooFit.LineColor(ROOT.kGreen), ROOT.RooFit.LineStyle(ROOT.kDashed))
mcpdf.plotOn(frame, ROOT.RooFit.LineColor(ROOT.kRed), ROOT.RooFit.LineStyle(ROOT.kDotted))
# frame.SetTitle('min#Delta#phi(j, E_{T}^{miss}) < 0.5')
#frame.GetXaxis().SetTitle('E_{T}^{miss} (GeV)')
frame.GetXaxis().SetTitle('')
frame.GetXaxis().SetTitleSize(0.)
frame.GetXaxis().SetLabelSize(0.)
frame.GetYaxis().SetLabelSize(0.)
frame.GetYaxis().SetTickSize(0.)
canvas.Clear()
canvas.xtitle = 'E_{T}^{miss} (GeV)'
canvas.ylimits = (0.1, 1000000.)
canvas.addHistogram(frame, clone = True, drawOpt = '')
canvas.Update(rList = [])
fitcurve = frame.findObject('model_Norm[met]')
rawcurve = frame.findObject('mcpdf_Norm[met]')
rdata = ROOT.TGraphErrors(dmet.GetNbinsX())
for iP in range(rdata.GetN()):
x = dmet.GetXaxis().GetBinCenter(iP + 1)
norm = fitcurve.interpolate(x)
rdata.SetPoint(iP, x, dmet.GetBinContent(iP + 1) / norm)
rdata.SetPointError(iP, 0., dmet.GetBinError(iP + 1) / norm)
rdata.SetMarkerStyle(8)
rdata.SetMarkerColor(ROOT.kBlack)
