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]
else:
hList = [iPreUnc, iPre, iPostUnc, iPost, iObs]
canvas.rlimits = (0.0, 2.0)
canvas.xtitle = 'E_{T}^{#gamma} (GeV)'
canvas.ytitle = 'Events / GeV'
canvas.printWeb('monophoton/fit', region, hList = hList, rList = [iLine, iPreUncRatio, iPostUncRatio, iPreRatio, iPostRatio])
canvas.Clear()
dataHist.Delete()
if postfitSub:
postfitSub.Delete()
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')
canvas.ylimits = (0.0, 2.0)
canvas.ytitle = 'Photon Scale Factor'
canvas.xtitle = 'E_{T}^{#gamma} (GeV)'
plotName = "scalefactor_"+str(loc)+"_"+str(pid)
canvas.printWeb('purity/'+s.Version+'/ScaleFactors', plotName, logy = False)
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()
