canvas.addHistogram(fpt, drawOpt='HIST')
canvas.addHistogram(fptUp, drawOpt='HIST')
canvas.addHistogram(fptDown, drawOpt='HIST')
canvas.addHistogram(hpt, drawOpt='HIST')
canvas.ylimits = (1.0, 2500000.)
canvas.SetLogy(True)
canvas.printWeb('monophoton/hadronTFactor' + suffix,
'distributions' + confName)
rcanvas.Clear()
rcanvas.legend.Clear()
# 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)
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')
canvas.ylimits = (0.0, 2.0)
canvas.ytitle = 'Photon Scale Factor'
canvas.xtitle = 'E_{T}^{#gamma} (GeV)'
plotName = "scalefactor_"+str(loc)+"_"+str(pid)
canvas.addHistogram(gpt, drawOpt = 'HIST')
canvas.addHistogram(fpt, drawOpt = 'HIST')
canvas.addHistogram(fptUp, drawOpt = 'HIST')
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')
resultLegend.apply('signal', shist)
shist.SetTitle('')
iS = canvas.addHistogram(shist)
groupList.append(iS)
except ReferenceError:
print region + ' region has no signal processes.'
pass
hList = groupList + [iPreUnc, iPre, iPostUnc, iPost, iObs]
canvas.rlimits = (0.0, 2.5)
if 'monoph' in region or 'horizontal' in region or 'vertical' in region:
canvas.ylimits = (0.0003, 100.)
rlabel = region
elif 'mono' in region:
canvas.ylimits = (0.0003, 60.)
if 'mu' in region:
rlabel = '#mu#gamma'
elif 'el' in region:
rlabel = 'e#gamma'
elif 'di' in region:
canvas.ylimits = (0.0003, 8.)
if 'mu' in region:
rlabel = '#mu#mu#gamma'
elif 'el' in region:
rlabel = 'ee#gamma'
canvas.cmsPave.SetTextSize(0.04)
frame = met.frame()
ddata.plotOn(frame)
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)
canvas.legend.apply('signal', shist)
shist.SetTitle('')
iS = canvas.addHistogram(shist)
groupList.append(iS)
except ReferenceError:
print region + ' region has no signal processes.'
pass
hList = groupList + [iPreUnc, iPre, iPostUnc, iPost, iObs]
canvas.rlimits = (0.0, 2.5)
if 'monoph' in region or 'horizontal' in region or 'vertical' in region:
canvas.ylimits = (0.0003, 200.)
elif 'mono' in region:
canvas.ylimits = (0.0003, 20.)
elif 'di' in region:
canvas.ylimits = (0.0003, 5.)
canvas.legend.setPosition(0.65, 0.92 - 0.04 * len(canvas.legend.entries), 0.9, 0.92)
if config == 'monoph':
canvas.xtitle = 'E_{T}^{#gamma} (GeV)'
else:
canvas.xtitle = 'M_{T}(#gamma, E_{T}^{miss}) (GeV)'
canvas.ytitle = 'Events / GeV'
canvas.rtitle = 'Data / Pred.'
if pdir == 'p':
