def runFit(evt, ip, vtrk, v, x, ktrkstr, kstr, param, img, hResoData, hResoMC):
ROOT.gROOT.Reset()
ROOT.gROOT.SetBatch()
pstyle = style.SetPlotStyle(1, 'FIT')
pstyle.SetOptFit(1111)
figs = []
if hResoData.GetEntries() < 10:
# print 'No stats in data: '+hResoData.GetName(), ktrkstr
return figs, x, ktrkstr, kstr, -1, -1, -1, -1, -1, -1, -1, -1
if hResoMC.GetEntries() < 10:
# print 'No stats in mc: '+hResoMC.GetName(), ktrkstr
return figs, x, ktrkstr, kstr, -1, -1, -1, -1, -1, -1, -1, -1
if param in ['pt', 'phi']:
if options.selection == '':
rResoData = fit.rebin(hResoData, 60, 30)
rResoMC = fit.rebin(hResoMC, 60, 30)
elif kstr == '_pt7p36to7p6':
rResoData = fit.rebin(hResoData, 80, 30)
rResoMC = fit.rebin(hResoMC, 80, 30)
else:
rResoData = fit.rebin(hResoData, 50, 30)
rResoMC = fit.rebin(hResoMC, 50, 30)
rmax = max(rResoData, rResoMC)
hResoData = hResoData.Rebin(rmax)
hResoMC = hResoMC.Rebin(rmax)
else:
if options.selection != '_pt3p0to10p0' and options.type == 'pv':
rResoData = fit.rebin(hResoData, 1000, 30)
rResoMC = fit.rebin(hResoMC, 1000, 30)
rmax = max(rResoData, rResoMC)
hResoData = hResoData.Rebin(rmax)
hResoMC = hResoMC.Rebin(rmax)
elif options.type == 'bs' and options.qcd:
rResoData = fit.rebin(hResoData, 500, 30)
rResoMC = fit.rebin(hResoMC, 500, 30)
rmax = max(rResoData, rResoMC)
hResoData = hResoData.Rebin(rmax)
hResoMC = hResoMC.Rebin(rmax)
elif options.type == 'bs' and options.selection == '_pt0p0to1p0':
rResoData = fit.rebin(hResoData, 1000, 30)
rResoMC = fit.rebin(hResoMC, 1000, 30)
rmax = max(rResoData, rResoMC)
hResoData = hResoData.Rebin(rmax)
hResoMC = hResoMC.Rebin(rmax)
else:
rResoData = fit.rebin(hResoData, 500, 30)
rResoMC = fit.rebin(hResoMC, 500, 30)
rmax = max(rResoData, rResoMC)
hResoData = hResoData.Rebin(rmax)
hResoMC = hResoMC.Rebin(rmax)
for h in [hResoData]:
h.SetMarkerStyle(c.datamrk)
h.SetMarkerSize(0.7)
h.SetMarkerColor(1)
h.SetLineColor(1)
for h in [hResoMC]:
h.SetMarkerSize(0)
h.SetMarkerColor(c.mccol)
h.SetLineColor(c.mccol)
h.SetFillColor(c.mccol)
h.SetLineStyle(1)
intResoMC = hResoMC.Integral()
intResoData = hResoData.Integral()
hResoMC.Scale(intResoData / intResoMC)
maxResoData = hResoData.GetMaximum()
maxResoMC = hResoMC.GetMaximum()
hResoMC.SetMaximum(1.2 * max(maxResoData, maxResoMC))
hResoMC.SetMinimum(0.)
selName = 'N_{trk}'
units = ''
if options.parampv == 'sumTrackPt':
selName = '#sump_{T}'
units = ' GeV'
elif options.parampv == 'sumTrackPtSq':
selName = '#sqrt{#sump^{2}_{T}}'
units = ' GeV'
# Resolution
c1 = ROOT.TCanvas()
hResoMC.Draw('hist')
hResoData.Draw('e1 sames')
fun.adjust(hResoMC, hResoData, nsig=2.0)
if options.fit:
if options.method == 'fwhm':
nsig = 2.0
else:
ffit = '3g'
nsig = 2.0
if x == 'dz': ffit = '3g'
resoChi2MC = 1e+10
resResoMC, resoMC, resoErrMC, resoChi2MC = fit.doFit('mcfit',
hResoMC,
x,
kstr,
c.mcfit,
ffit,
nsig=nsig,
nTries=3)
resoChi2Data = 1e+10
resResoData, resoData, resoErrData, resoChi2Data = fit.doFit('datafit',
hResoData,
x,
kstr,
1,
ffit,
nsig=nsig,
nTries=3)
sysErrData, sysErrMC = hResoData.GetXaxis().GetBinWidth(
2), hResoMC.GetXaxis().GetBinWidth(2)
resResoMC.Draw("same")
resResoData.Draw("same")
if options.method == 'fwhm':
# get resolution estimation from bins using the results of the maximum fit
resoData, resoErrData, sysErrData = fit.fwhm(hResoData,
resResoData,
nmin=10000)
resoMC, resoErrMC, sysErrMC = fit.fwhm(hResoMC,
resResoMC,
nmin=10000)
resResoData.SetLineStyle(2)
c1.Update()
finfoData = hResoData.GetListOfFunctions().FindObject("stats")
finfoData.__class__ = ROOT.TPaveStats
finfoData.SetX1NDC(0.7)
finfoData.SetX2NDC(0.95)
finfoData.SetY1NDC(0.20)
finfoData.SetY2NDC(0.40)
lData = ROOT.TText(0.81, 0.41, "Data (fit)")
lData.SetTextSize(0.035)
lData.SetNDC()
lData.Draw()
finfoMC = hResoMC.GetListOfFunctions().FindObject("stats")
finfoMC.__class__ = ROOT.TPaveStats
finfoMC.SetX1NDC(0.7)
finfoMC.SetX2NDC(0.95)
finfoMC.SetY1NDC(0.45)
finfoMC.SetY2NDC(0.65)
lMC = ROOT.TText(0.81, 0.66, "Simulation (fit)")
lMC.SetTextSize(0.035)
lMC.SetNDC()
lMC.Draw()
else:
resoData, resoErrData, sysErrData = fit.fwhm(hResoData, nmin=10000)
resoMC, resoErrMC, sysErrMC = fit.fwhm(hResoMC, nmin=10000)
if resoData * resoMC == 0.:
resoData, resoErrData, sysErrData = 0., 0., 0.
resoMC, resoErrMC, sysErrMC = 0., 0., 0.
xLabel = x
if x == 'd0': xLabel = 'd_{xy}'
elif x == 'dz': xLabel = 'd_{z}'
lDataReso = ROOT.TLatex(
0.20, 0.61, "#sigma^{Data}_{" + xLabel + "} = %.1f #mum" % (resoData))
lDataReso.SetNDC()
lDataReso.SetTextFont(43)
lDataReso.SetTextSize(20)
lDataReso.Draw()
lMCReso = ROOT.TLatex(
0.20, 0.70, "#sigma^{Sim.}_{" + xLabel + "} = %.1f #mum" % (resoMC))
lMCReso.SetNDC()
lMCReso.SetTextFont(43)
lMCReso.SetTextSize(20)
lMCReso.Draw()
lBin = ROOT.TLatex(0.43, 0.20, "Bin = %.1f #mum" % (sysErrData))
lBin.SetNDC()
lBin.SetTextFont(43)
lBin.SetTextSize(14)
lBin.Draw()
if ip != 'bs':
lSelPV = ROOT.TLatex(
0.20, 0.85,
str(vtrk['bins'][1]) + ' < ' + selName + ' < ' +
str(vtrk['bins'][2]) + units)
lSelPV.SetTextSize(0.035)
lSelPV.SetNDC()
lSelPV.Draw()
else:
lSelPVx = ROOT.TLatex(
0.20, 0.87,
'Beam width (x) = %.1f #mum' % (vtrk['beamwidthx'][int(ktrkstr)]))
lSelPVy = ROOT.TLatex(
0.20, 0.83,
'Beam width (y) = %.1f #mum' % (vtrk['beamwidthy'][int(ktrkstr)]))
lSelPVx.SetTextSize(0.032)
lSelPVy.SetTextSize(0.032)
lSelPVx.SetNDC()
lSelPVy.SetNDC()
lSelPVx.Draw()
lSelPVy.Draw()
pLabel = 'p_{T}'
pUnits = 'GeV'
pPrec = '%.2f'
if param == 'eta':
pLabel = '#eta'
pUnits = ''
elif param == 'phi':
pLabel = '#phi'
pUnits = ''
elif param == 'npv':
pLabel = 'N_{PV}'
pUnits = ''
pPrec = '%d'
elif param == 'dr':
pLabel = '#DeltaR'
pUnits = ''
lSel = ROOT.TLatex(
0.20, 0.78, (pPrec + ' < ' + pLabel + ' < ' + pPrec + ' ' + pUnits) %
(v['bins'][1], v['bins'][2]))
lSel.SetTextSize(0.035)
lSel.SetNDC()
lSel.Draw()
c1.Update()
leg = ROOT.TLegend(0.82, 0.92, 0.990, 0.75)
leg.SetFillColor(253)
leg.SetBorderSize(0)
leg.AddEntry(hResoData, "Data", "p")
leg.AddEntry(hResoMC, "Simulation", "f")
if options.fit:
leg.AddEntry(resResoData, "Data (fit)", "l")
leg.AddEntry(resResoMC, "Simulation (fit)", "l")
leg.Draw()
t1, t2, t3, t4 = style.cmslabel(1, c.year, evt)
t1.Draw()
t2.Draw()
t3.Draw()
t4.Draw()
# b = fun.isbadfit(resoChi2MC, resoChi2Data)
# b.Draw()
foutput = 'ip' + ip + 'Reso_' + x + ktrkstr + kstr
if ip == 'bs': foutput = 'ip' + ip + 'Reso_' + x + kstr + '_' + ktrkstr
figs.append(foutput)
c1.Print(options.output + '/' + foutput + '.' + img)
c1.Clear()
return figs, x, ktrkstr, kstr, resoData, resoErrData, intResoData, resoMC, resoErrMC, intResoMC, sysErrData, sysErrMC
leg.AddEntry(h[p], "QCD", "f")
elif p == 'TTGJets':
leg.AddEntry(h[p], "t#bar{t}#gamma(#gamma)+jets", "f")
elif p == 'TGJets':
leg.AddEntry(h[p], "t/#bar{t}#gamma+jets", "f")
elif p == 'VJets':
leg.AddEntry(h[p], "V(#gamma)+jets", "f")
elif p == 'Higgs':
leg.AddEntry(h[p], "Higgs", "f")
elif p == 'TT':
leg.AddEntry(h[p], "t#bar{t}", "f")
elif p == 'GJet':
leg.AddEntry(h[p], "#gamma+jets", "f")
leg.Draw()
t1, t2, t3 = style.cmslabel(1)
t1.Draw()
t2.Draw()
t3.Draw()
t = style.channel(chan)
t.Draw()
hist_tqh_ratio.Divide(hSMbis)
hSMbis_uncertainty = hSMbis.Clone()
nbins = hSMbis.GetNbinsX()
for i in range(nbins):
mean = hSMbis_uncertainty.GetBinContent(i + 1)
error = hSMbis_uncertainty.GetBinError(i + 1)
upper_rel_error = 0. if mean == 0 else (mean + error) / mean
lower_rel_error = 0 if mean == 0 else (mean - error) / mean
error_mean = (upper_rel_error + lower_rel_error) / 2.
hData.SetLineColor(1)
hMC.SetMarkerSize(0)
hMC.SetMarkerColor(c.mccol)
hMC.SetLineColor(c.mccol)
hMC.SetFillColor(c.mccol)
hMC.SetLineStyle(1)
leg = ROOT.TLegend(0.82, 0.92, 0.990, 0.75)
leg.SetFillColor(253)
leg.SetBorderSize(0)
leg.AddEntry(hData, "Data", "p")
leg.AddEntry(hMC, "Simulation", "f")
leg.Draw()
t1, t2, t3, t4 = style.cmslabel(1, c.year, evt)
t1.Draw()
t2.Draw()
t3.Draw()
t4.Draw()
if h in ['ipPt', 'ipDrTrkJet']:
hData.SetMinimum(10)
hMC.SetMinimum(10)
c1.SetLogy(1)
else:
c1.SetLogy(0)
c1.Print(options.output + '/' + h + '.eps')
c1.Clear()
elif 'reso_x' in f:
h0.GetYaxis().SetTitle('PV resolution in x [#mum]')
elif 'reso_y' in f:
h0.GetYaxis().SetTitle('PV resolution in y [#mum]')
elif 'reso_z' in f:
h0.GetYaxis().SetTitle('PV resolution in z [#mum]')
elif 'pull_x' in f:
h0.GetYaxis().SetTitle('PV pull in x')
elif 'pull_y' in f:
h0.GetYaxis().SetTitle('PV pull in y')
elif 'pull_z' in f:
h0.GetYaxis().SetTitle('PV pull in z')
leg.Draw()
t1, t2, t3, t4 = style.cmslabel(1, c.year, '', False)
t1.Draw()
t2.Draw()
t3.Draw()
t4.Draw()
if 'eta' in options.param and 'dz' in files[0]: c1.SetLogy(1)
if 'pt' in options.param:
if options.log:
h0.SetMinimum(5.)
h0.SetMaximum(1000.)
c1.SetLogy(1)
# if options.selection:
if options.param in ['phi', 'npv']:
def plot(c1, hData, hMC, mode, m, x, isDeconv=False):
hData.SetMarkerStyle(20)
hData.SetMarkerSize(1.0)
hData.SetMarkerColor(1)
hData.SetLineColor(1)
hMC.SetMarkerStyle(22)
hMC.SetMarkerSize(1.0)
hMC.SetMarkerColor(c.mcfit)
hMC.SetLineColor(c.mcfit)
if mode == 'pv':
if m == 'reso':
if options.process == 'zb': h0 = c1.DrawFrame(0., 0., 20., 70.)
else: h0 = c1.DrawFrame(0., 0., 400., 30.)
else:
if options.process == 'zb': h0 = c1.DrawFrame(0., 0., 20., 1.7)
else: h0 = c1.DrawFrame(0., 0., 400., 1.7)
if mode == 'pv':
hMC.Draw('same')
h0.GetXaxis().SetTitle(hMC.GetXaxis().GetTitle())
h0.GetYaxis().SetTitle(hMC.GetYaxis().GetTitle())
else:
hMC.Draw('')
hMC.Draw('pe1 same')
hData.Draw('same')
hData.Draw('pe1 same')
if mode != 'pv':
hMC.GetYaxis().SetRangeUser(0., 450.)
if 'eta' in mode and x == 'dz': hMC.GetYaxis().SetRangeUser(0., 3000.)
leg = ROOT.TLegend(0.62, 0.55, 0.86, 0.73)
leg.SetFillColor(253)
leg.SetBorderSize(0)
leg.AddEntry(hData, "Data", "p")
leg.AddEntry(hMC, "Simulation", "p")
if mode == 'pv' and m == 'pull':
leg = ROOT.TLegend(0.67, 0.25, 0.910, 0.43)
leg.SetFillColor(253)
leg.SetBorderSize(0)
leg.AddEntry(hData, "Data", "p")
leg.AddEntry(hMC, "Simulation", "p")
leg.Draw()
t1, t2, t3, t4 = style.cmslabel(2, c.year, evt, False)
t1.Draw()
t2.Draw()
t3.Draw()
t4.Draw()
if isDeconv == False:
for outdir in [options.output, 'pub']:
if not options.pub and outdir == 'pub': continue
c1.Print(outdir + '/' + mode + '_' + m + '_' + x + '_' +
options.process + '.pdf')
if options.pub:
c1.SaveAs('pub/' + mode + '_' + m + '_' + x + '_' +
options.process + '.root')
else:
for outdir in [options.output, 'pub']:
if not options.pub and outdir == 'pub': continue
c1.Print(outdir + '/' + mode + '_' + m + '_' + x + '_deconv_' +
options.process + '.pdf')
if options.pub:
c1.SaveAs('pub/' + mode + '_' + m + '_' + x + '_deconv_' +
options.process + '.root')
c1.Clear()
def runFit(evt, v, x, kstr, img, hResoData, hResoMC, hPullData, hPullMC):
ROOT.gROOT.Reset()
ROOT.gROOT.SetBatch()
pstyle = style.SetPlotStyle(1, 'FIT')
pstyle.SetOptFit(1111)
figs = []
if hResoData.GetEntries() < 10:
print 'No stats in data: ' + hResoData.GetName()
sys.exit()
if hResoMC.GetEntries() < 10:
print 'No stats in mc: ' + hResoMC.GetName()
sys.exit()
for h in [hResoData, hPullData]:
h.SetMarkerStyle(c.datamrk)
h.SetMarkerSize(0.7)
h.SetMarkerColor(1)
h.SetLineColor(1)
for h in [hResoMC, hPullMC]:
h.SetMarkerSize(0)
h.SetMarkerColor(c.mccol)
h.SetLineColor(c.mccol)
h.SetFillColor(c.mccol)
h.SetLineStyle(1)
intResoMC = hResoMC.Integral()
intResoData = hResoData.Integral()
hResoMC.Scale(intResoData / intResoMC)
intPullMC = hPullMC.Integral()
intPullData = hPullData.Integral()
hPullMC.Scale(intPullData / intPullMC)
# Rebin in case of fine-bin measurement
# hResoData = hResoData.Rebin(2)
# hResoMC = hResoMC.Rebin(2)
# hPullData = hPullData.Rebin(5)
# hPullMC = hPullMC.Rebin(5)
maxResoData = hResoData.GetMaximum()
maxResoMC = hResoMC.GetMaximum()
hResoMC.SetMaximum(1.2 * max(maxResoData, maxResoMC))
hResoMC.SetMinimum(0.)
maxPullData = hPullData.GetMaximum()
maxPullMC = hPullMC.GetMaximum()
hPullMC.SetMaximum(1.2 * max(maxPullData, maxPullMC))
hPullMC.SetMinimum(0.)
selName = 'N_{trk}'
units = ''
if options.param == 'sumTrackPt':
selName = '#sump_{T}'
units = ' GeV'
elif options.param == 'sumTrackPtSq':
selName = '#sqrt{#sump^{2}_{T}}'
units = ' GeV'
# Resolution
c1 = ROOT.TCanvas()
fun.adjust(hResoMC, hResoData, nsig=5)
hResoMC.Draw('hist')
hResoData.Draw('e1 sames')
resResoMC, resoMC, resoErrMC, resoChi2MC = fit.doFit('mcfit_reso',
hResoMC,
x,
kstr,
c.mcfit,
'2g',
nsig=3,
nTries=3)
resResoMC.Draw("same")
resResoData, resoData, resoErrData, resoChi2Data = fit.doFit(
'datafit_reso', hResoData, x, kstr, 1, '2g', nsig=3, nTries=3)
resResoData.Draw("same")
resResoData.SetLineStyle(2)
sysResoData = hResoData.GetXaxis().GetBinWidth(2)
sysResoMC = hResoMC.GetXaxis().GetBinWidth(2)
c1.Update()
finfoData = hResoData.GetListOfFunctions().FindObject("stats")
finfoData.__class__ = ROOT.TPaveStats
finfoData.SetX1NDC(0.7)
finfoData.SetX2NDC(0.95)
finfoData.SetY1NDC(0.20)
finfoData.SetY2NDC(0.40)
lData = ROOT.TText(0.81, 0.41, "Data (fit)")
lData.SetTextSize(0.035)
lData.SetNDC()
lData.Draw()
finfoMC = hResoMC.GetListOfFunctions().FindObject("stats")
finfoMC.__class__ = ROOT.TPaveStats
finfoMC.SetX1NDC(0.7)
finfoMC.SetX2NDC(0.95)
finfoMC.SetY1NDC(0.45)
finfoMC.SetY2NDC(0.65)
lMC = ROOT.TText(0.81, 0.66, "Simulation (fit)")
lMC.SetTextSize(0.035)
lMC.SetNDC()
lMC.Draw()
lDataReso = ROOT.TLatex(
0.20, 0.63, "#sigma^{Data}_{" + x + "} = %.1f #mum" % (resoData))
lDataReso.SetNDC()
lDataReso.SetTextFont(43)
lDataReso.SetTextSize(20)
lDataReso.Draw()
lMCReso = ROOT.TLatex(0.20, 0.70,
"#sigma^{Sim.}_{" + x + "} = %.1f #mum" % (resoMC))
lMCReso.SetNDC()
lMCReso.SetTextFont(43)
lMCReso.SetTextSize(20)
lMCReso.Draw()
lSel = ROOT.TLatex(
0.20, 0.80,
str(v['bins'][1]) + ' < ' + selName + ' < ' + str(v['bins'][2]) +
units)
lSel.SetTextSize(0.035)
lSel.SetNDC()
lSel.Draw()
c1.Update()
leg = ROOT.TLegend(0.82, 0.92, 0.990, 0.75)
leg.SetFillColor(253)
leg.SetBorderSize(0)
leg.AddEntry(hResoData, "Data", "p")
leg.AddEntry(hResoMC, "Simulation", "f")
leg.AddEntry(resResoData, "Data (fit)", "l")
leg.AddEntry(resResoMC, "Simulation (fit)", "l")
leg.Draw()
t1, t2, t3, t4 = style.cmslabel(1, c.year, evt)
t1.Draw()
t2.Draw()
t3.Draw()
t4.Draw()
b = fun.isbadfit(resoChi2MC, resoChi2Data)
b.Draw()
foutput = 'pvReso_' + x + kstr
figs.append(foutput)
c1.Print(options.output + '/' + foutput + '.' + img)
c1.Clear()
# Pull
fun.adjust(hPullMC, hPullData, nsig=5)
hPullMC.Draw('hist')
hPullData.Draw('e1 sames')
resPullMC, pullMC, pullErrMC, pullChi2MC = fit.doFit('mcfit_pull',
hPullMC,
x,
kstr,
c.mcfit,
'1g',
nsig=2.5,
nTries=3)
resPullMC.Draw("same")
resPullData, pullData, pullErrData, pullChi2Data = fit.doFit(
'datafit_pull', hPullData, x, kstr, 1, '1g', nsig=2.5, nTries=3)
resPullData.Draw("same")
resPullData.SetLineStyle(2)
sysPullData = hPullData.GetXaxis().GetBinWidth(2)
sysPullMC = hPullMC.GetXaxis().GetBinWidth(2)
c1.Update()
finfoData = hPullData.GetListOfFunctions().FindObject("stats")
finfoData.__class__ = ROOT.TPaveStats
finfoData.SetX1NDC(0.7)
finfoData.SetX2NDC(0.95)
finfoData.SetY1NDC(0.20)
finfoData.SetY2NDC(0.40)
lData = ROOT.TText(0.81, 0.41, "Data (fit)")
lData.SetTextSize(0.035)
lData.SetNDC()
lData.Draw()
finfoMC = hPullMC.GetListOfFunctions().FindObject("stats")
finfoMC.__class__ = ROOT.TPaveStats
finfoMC.SetX1NDC(0.7)
finfoMC.SetX2NDC(0.95)
finfoMC.SetY1NDC(0.45)
finfoMC.SetY2NDC(0.65)
lMC = ROOT.TText(0.81, 0.66, "Simulation (fit)")
lMC.SetTextSize(0.035)
lMC.SetNDC()
lMC.Draw()
lDataPull = ROOT.TLatex(0.20, 0.63,
"#sigma^{Data}_{" + x + "} = %.2f" % (pullData))
lDataPull.SetNDC()
lDataPull.SetTextFont(43)
lDataPull.SetTextSize(20)
lDataPull.Draw()
lMCPull = ROOT.TLatex(0.20, 0.70,
"#sigma^{Sim.}_{" + x + "} = %.2f" % (pullMC))
lMCPull.SetNDC()
lMCPull.SetTextFont(43)
lMCPull.SetTextSize(20)
lMCPull.Draw()
lSel = ROOT.TLatex(
0.20, 0.80,
str(v['bins'][1]) + ' < ' + selName + ' < ' + str(v['bins'][2]) +
units)
lSel.SetTextSize(0.035)
lSel.SetNDC()
lSel.Draw()
c1.Update()
leg = ROOT.TLegend(0.82, 0.92, 0.990, 0.75)
leg.SetFillColor(253)
leg.SetBorderSize(0)
leg.AddEntry(hPullData, "Data", "p")
leg.AddEntry(hPullMC, "Simulation", "f")
leg.AddEntry(resPullData, "Data (fit)", "l")
leg.AddEntry(resPullMC, "Simulation (fit)", "l")
leg.Draw()
t1, t2, t3, t4 = style.cmslabel(1, c.year, evt)
t1.Draw()
t2.Draw()
t3.Draw()
t4.Draw()
b = fun.isbadfit(pullChi2MC, pullChi2Data)
b.Draw()
foutput = 'pvPull_' + x + kstr
figs.append(foutput)
c1.Print(options.output + '/' + foutput + '.' + img)
c1.Clear()
return figs, x, kstr, resoData, resoErrData, resoMC, resoErrMC, pullData, pullErrData, pullMC, pullErrMC, sysResoData, sysResoMC, sysPullData, sysPullMC
def makeModel(var, data_obs, sig, chan):
# Derive background pdf
dataPdf = {}
dataPdfPar = {}
ordMinBernstein = 1
ordMaxBernstein = 10
for ord in range(ordMinBernstein, ordMaxBernstein):
dataPdfName = 'Bernstein' + str(ord)
dataPdfPar[dataPdfName] = []
dataPdf[dataPdfName] = [
makePdf(var, 'Bernstein', ord, dataPdfPar[dataPdfName]), ord
]
dataPdf = collections.OrderedDict(
sorted(dataPdf.items(), key=lambda k: k[0]))
dataPdfPar = collections.OrderedDict(
sorted(dataPdfPar.items(), key=lambda k: k[0]))
var.setRange('low', 100, 120)
var.setRange('high', 130, 180)
llh0 = 10E+10
pval = 0.05
ord0 = ordMinBernstein - 1
bestBernstein = ''
for name, pdf in dataPdf.items():
res = dataPdf[name][0].fitTo(data_obs,\
ROOT.RooFit.Minimizer("Minuit2","minimize"),\
ROOT.RooFit.Minos(ROOT.kFALSE),ROOT.RooFit.Hesse(ROOT.kTRUE),\
ROOT.RooFit.PrintLevel(-1000),\
ROOT.RooFit.Save(),\
ROOT.RooFit.Range("low,high"),\
ROOT.RooFit.SumW2Error(ROOT.kTRUE))
llh = res.minNll()
chi2 = 2. * (llh0 - llh)
ord = dataPdf[name][1]
delta_dof = ord - ord0
prob = ROOT.TMath.Prob(chi2, delta_dof)
if prob > pval:
bestBernstein = name
break
ord0 = ord
llh0 = llh
dataPdfBernstein = dataPdf[bestBernstein]
if chan == 'leptonic':
nBins = 32
else:
nBins = 64
dataPlot = var.frame(ROOT.RooFit.Title("Fit"), ROOT.RooFit.Bins(nBins))
dataPdfFinal = [dataPdfBernstein]
for pdf in dataPdfFinal:
data_obs.plotOn(dataPlot, ROOT.RooFit.DataError(ROOT.RooAbsData.SumW2),
ROOT.RooFit.MarkerStyle(20),
ROOT.RooFit.Name('data_obs'),
ROOT.RooFit.XErrorSize(0))
pdf[0].plotOn(dataPlot, ROOT.RooFit.LineColor(ROOT.kBlue),
ROOT.RooFit.Name(pdf[0].GetName()))
dataPlot.GetXaxis().SetTitle('Diphoton invariant mass [GeV]')
bestDataPdf = dataPdfFinal[0]
c1 = ROOT.TCanvas("c1", "c1", 650, 500)
dataPlot.Draw()
t1, t2, t3 = style.cmslabel(2)
t1.Draw()
t3.Draw()
t = style.channel(chan)
t.Draw()
leg = ROOT.TLegend(0.65, 0.65, 0.88, 0.83)
leg.SetFillColor(253)
leg.SetBorderSize(0)
leg.AddEntry(dataPlot.findObject('data_obs'), 'Data', 'p')
leg.AddEntry(dataPlot.findObject(bestBernstein),
'Bernstein(' + str(dataPdfBernstein[1]) + ')', 'l')
leg.Draw()
# param = bestDataPdf[0].getParameters(ROOT.RooArgSet())
c1.Print('pics/bkgModel.eps')
# Derive signal pdf
sigPdf = {}
sigPdfPar = {}
sigPdfGaus = {}
sigPdfDm = {}
sigPdfMean = {}
sigPdfSigma = {}
mH = ROOT.RooRealVar("mH", "mH", 125., 115., 135.)
mH.setConstant(True)
ordMaxGaus = 4
for ord in range(ordMaxGaus):
sigPdfName = 'Gaus' + str(ord)
sigPdfPar[sigPdfName] = []
sigPdfGaus[sigPdfName] = []
sigPdfDm[sigPdfName] = []
sigPdfMean[sigPdfName] = []
sigPdfSigma[sigPdfName] = []
sigPdf[sigPdfName] = [makePdf(var,'Gaus',ord,sigPdfPar[sigPdfName],sigPdfGaus[sigPdfName],\
sigPdfDm[sigPdfName],sigPdfMean[sigPdfName],sigPdfSigma[sigPdfName],mH),ord]
sigPdf = collections.OrderedDict(sorted(sigPdf.items(),
key=lambda k: k[0]))
sigPdfPar = collections.OrderedDict(
sorted(sigPdfPar.items(), key=lambda k: k[0]))
sigPdfGaus = collections.OrderedDict(
sorted(sigPdfGaus.items(), key=lambda k: k[0]))
sigPdfDm = collections.OrderedDict(
sorted(sigPdfDm.items(), key=lambda k: k[0]))
sigPdfMean = collections.OrderedDict(
sorted(sigPdfMean.items(), key=lambda k: k[0]))
sigPdfSigma = collections.OrderedDict(
sorted(sigPdfSigma.items(), key=lambda k: k[0]))
llh0 = 0
pval = 0.05
ord0 = 0
bestGaus = ''
for name, pdf in sigPdf.items():
print sigPdf[name][0]
# res = sigPdf[name][0].fitTo(sig,ROOT.RooFit.Minimizer("Minuit2","minimize"),\
# ROOT.RooFit.Minos(ROOT.kFALSE),ROOT.RooFit.Hesse(ROOT.kTRUE),\
# ROOT.RooFit.Warnings(ROOT.kTRUE),ROOT.RooFit.PrintLevel(-1000),\
# ROOT.RooFit.SumW2Error(True),\
# ROOT.RooFit.Save(),\
# ROOT.RooFit.Range(115.,135.))
res = sigPdf[name][0].fitTo(sig,ROOT.RooFit.Minimizer("Minuit2","minimize"),\
ROOT.RooFit.Minos(ROOT.kFALSE),ROOT.RooFit.Hesse(ROOT.kTRUE),\
ROOT.RooFit.SumW2Error(True),\
ROOT.RooFit.Save(),\
ROOT.RooFit.Range(115.,135.))
llh = res.minNll()
chi2 = 2. * (llh0 - llh)
ord = sigPdf[name][1]
delta_dof = (2 * (ord + 1) + ord) - (2 * (ord0 + 1) + ord0)
prob = ROOT.TMath.Prob(chi2, delta_dof)
print prob
if prob > pval:
bestGaus = name
break
ord0 = ord
llh0 = llh
sigPdfGausResult = sigPdf[bestGaus]
sigPlot = var.frame(ROOT.RooFit.Title("Fit"), ROOT.RooFit.Bins(64),
ROOT.RooFit.Range(110, 140))
sigPdfFinal = [sigPdfGausResult]
for pdf in sigPdfFinal:
sig.plotOn(sigPlot, ROOT.RooFit.DataError(ROOT.RooAbsData.SumW2),
ROOT.RooFit.MarkerStyle(20), ROOT.RooFit.Name('sig'),
ROOT.RooFit.XErrorSize(0))
pdf[0].getVariables().Print("v")
pdf[0].plotOn(sigPlot, ROOT.RooFit.LineColor(ROOT.kBlue),
ROOT.RooFit.Name(pdf[0].GetName()))
sigPlot.GetXaxis().SetTitle('Diphoton invariant mass [GeV]')
bestSigPdf = sigPdfFinal[0]
c1 = ROOT.TCanvas("c1", "c1", 650, 500)
sigPlot.Draw()
t1, t2, t3 = style.cmslabel(2)
t1.Draw()
t3.Draw()
t = style.channel(chan)
t.Draw()
leg = ROOT.TLegend(0.65, 0.65, 0.88, 0.83)
leg.SetFillColor(253)
leg.SetBorderSize(0)
leg.AddEntry(sigPlot.findObject('sig'), 'Data', 'p')
leg.AddEntry(sigPlot.findObject(bestGaus),
'Gaus(' + str(sigPdfGausResult[1] + 1) + ')', 'l')
leg.Draw()
# param = bestSigPdf[0].getParameters(ROOT.RooArgSet())
# iter = param.createIterator()
# while True:
# arg = iter.Next()
# if arg == None: break
# name = arg.GetName()
# print = param[name].getVal()
c1.Print('pics/sigModel.eps')
# Combine sig+bkg pdfs
c1 = ROOT.TCanvas("c1", "c1", 650, 500)
bkg = bestDataPdf[0]
sig = bestSigPdf[0]
fsig = ROOT.RooRealVar("fsig", "signal fraction", 0.001, 0., 1.)
combPdf = ROOT.RooAddPdf("comb", "comb", ROOT.RooArgList(sig, bkg),
ROOT.RooArgList(fsig))
param = combPdf.getParameters(ROOT.RooArgSet())
iter = param.createIterator()
while True:
arg = iter.Next()
if arg == None: break
name = arg.GetName()
if 'Gaus_dm' in name: param[name].setConstant(True)
# print name, param[name].getVal()
combPlot = var.frame(ROOT.RooFit.Title("Fit"), ROOT.RooFit.Bins(nBins))
data_obs.plotOn(combPlot, ROOT.RooFit.DataError(ROOT.RooAbsData.SumW2),
ROOT.RooFit.MarkerStyle(20), ROOT.RooFit.Name('data_obs'),
ROOT.RooFit.XErrorSize(0))
# sig.plotOn(combPlot,ROOT.RooFit.Normalization(0.1),ROOT.RooFit.LineColor(2))
sig.plotOn(combPlot, ROOT.RooFit.LineColor(2))
res = combPdf.fitTo(data_obs,ROOT.RooFit.Minimizer("Minuit2","minimize"),\
ROOT.RooFit.Minos(ROOT.kFALSE),ROOT.RooFit.Hesse(ROOT.kTRUE),\
ROOT.RooFit.SumW2Error(True),\
ROOT.RooFit.Save())
bkgComponent = ROOT.RooArgSet(bkg)
sigComponent = ROOT.RooArgSet(sig)
combPdf.plotOn(combPlot, ROOT.RooFit.Components(bkgComponent),
ROOT.RooFit.LineStyle(2))
combPdf.plotOn(combPlot, ROOT.RooFit.Components(sigComponent),
ROOT.RooFit.LineStyle(2), ROOT.RooFit.LineColor(2))
combPdf.plotOn(combPlot)
combPlot.GetXaxis().SetTitle('Diphoton invariant mass [GeV]')
combPlot.Draw()
t1, t2, t3 = style.cmslabel(2)
t1.Draw()
t3.Draw()
t = style.channel(chan)
t.Draw()
combPlot.Print('v')
leg = ROOT.TLegend(0.65, 0.65, 0.88, 0.83)
leg.SetFillColor(253)
leg.SetBorderSize(0)
leg.AddEntry(combPlot.findObject('data_obs'), 'Data', 'p')
leg.AddEntry(combPlot.findObject('comb_Norm[DiPhoMassFit]'),
'Combined fit', 'l')
leg.AddEntry(
combPlot.findObject('comb_Norm[DiPhoMassFit]_Comp[' + bestBernstein +
']'), 'Background', 'l')
leg.AddEntry(
combPlot.findObject('comb_Norm[DiPhoMassFit]_Comp[' + bestGaus + ']'),
'Signal', 'l')
leg.AddEntry(
combPlot.findObject(bestGaus + '_Norm[DiPhoMassFit]_Range[fit_nll_' +
bestGaus + '_sig]_NormRange[fit_nll_' + bestGaus +
'_sig]'), 'Signal fit', 'l')
leg.Draw()
c1.Print('pics/combModel.eps')