if title:
canvas.legend.add('eff', title = title, opt = 'LP', color = ROOT.kBlack, mstyle = 8)
canvas.legend.apply('eff', eff)
else:
eff.SetMarkerColor(ROOT.kBlack)
eff.SetMarkerStyle(8)
canvas.addHistogram(eff, drawOpt = 'EP')
canvas.xtitle = vtitle
canvas.ylimits = (0., 1.2)
canvas.Update()
if type(binning) is tuple:
canvas.addLine(binning[0], 1., binning[2], 1.)
eff.GetXaxis().SetLimits(binning[1], binning[2])
else:
canvas.addLine(binning[0], 1., binning[-1], 1.)
eff.GetXaxis().SetLimits(binning[0], binning[-1])
eff.GetYaxis().SetRangeUser(0., 1.2)
canvas.printWeb('trigger', vname + '_' + tname + '_' + sname + '_' + oname, logy = False)
func = None
marker = None
if vname == 'pt' and (tname == 'l1' or tname == 'l1hlt') and sname == 'jht':
func = work.factory('PolyVar::func(x, {intercept[1.04,0.,2.], slope[-0.001,-0.01,0.]})')
params = ROOT.RooArgList(work.arg('intercept'), work.arg('slope'))
canvas.xtitle = binningTitle
canvas.printWeb('efake', 'frate_' + dataType + '_' + binningName, logy = False)
for iBin, (binName, cut) in enumerate(fitBins):
print '%15s [%.3f +- %.3f (stat.) +- %.3f (syst.)] x 10^{-2}' % (binName, contents[iBin] * 100., staterrs[iBin] * 100., systerrs[iBin] * 100.)
if toyUncert and binningName == 'pt':
binName = 'pt_100_6500'
for conf in ['ee', 'eg']:
canvas.Clear(full = True)
canvas.ylimits = (0., 0.05)
canvas.xtitle = 'N_{Z}'
toydist = toydists[binName][conf]
toydist.Scale(1. / toydist.GetSumOfWeights())
canvas.legend.setPosition(0.7, 0.7, 0.9, 0.9)
canvas.legend.add('toys', title = 'Toys', opt = 'LF', color = ROOT.kBlue - 7, lwidth = 2, fstyle = 3003)
canvas.legend.add('nominal', title = 'Nominal', opt = 'L', color = ROOT.kBlack, lwidth = 2)
canvas.legend.apply('toys', toydist)
canvas.addHistogram(toydist, drawOpt = 'HIST')
arrow = canvas.addLine(nominal[conf][binName], toydist.GetMaximum() * 0.2, nominal[conf][binName], 0., width = 2, cls = ROOT.TArrow)
canvas.legend.apply('nominal', arrow)
canvas.printWeb('efake', 'toys_' + dataType + '_' + conf + '_' + binName, logy = False)
outputFile.Close()
graph.SetPoint(iX - 1, cent.GetXaxis().GetBinCenter(iX), y)
graph.SetPointError(iX - 1, binw * 0.5, binw * 0.5,
up.GetBinContent(iX) - y,
y - down.GetBinContent(iX))
bars.SetPoint(iX - 1, cent.GetXaxis().GetBinCenter(iX), y)
bars.SetPointError(iX - 1, binw * 0.5, 0.)
cent.SetBinError(iX, 0.)
canvas.legend.apply(hname, cent)
canvas.legend.apply(hname, bars)
canvas.legend.apply(hname, graph)
cent.GetXaxis().SetTitle('p_{T}^{#gamma} (GeV)')
cent.GetYaxis().SetTitle('#sigma_{QCD}^{NNLO} / #sigma^{LO}')
canvas.addHistogram(cent, drawOpt='EP')
canvas.addHistogram(graph, drawOpt='2')
canvas.addHistogram(bars, drawOpt='EZ')
canvas.addLine(cent.GetXaxis().GetXmin(),
1.,
cent.GetXaxis().GetXmax(),
1.,
style=ROOT.kDashed)
canvas.ylimits = (0.8, 1.8)
canvas.printWeb('kfactor', 'zgwg', logy=False)
canvas.Clear()
if nompset.find('tpconf').getLabel() == conf and nompset.find(
'binName').getLabel() == bin:
break
else:
raise RuntimeError('Nom pset for ' + suffix + ' not found')
nZ = nompset.find('nZ').getVal()
toydist = toydists[bin][conf]
toydist.Scale(1. / toydist.GetSumOfWeights())
canvas.legend.apply('toys', toydist)
canvas.addHistogram(toydist, drawOpt='HIST')
canvas.Update(logy=False)
arrow = canvas.addLine(nZ,
toydist.GetMaximum() * 0.2,
nZ,
0.,
width=2,
cls=ROOT.TArrow)
arrow.SetArrowSize(0.1)
canvas.legend.apply('nominal', arrow)
canvas.printWeb('efake/toys_' + binningName,
dataType + '_' + conf + '_' + bin,
logy=False)
outputFile.Close()
canvas.addHistogram(sf, drawOpt='EP')
if tname + '_' + vname == fit_targ:
func = ROOT.TF1('fitfunc', formula, *fit_range)
if len(params) > 1:
func.SetParameters(*params)
else:
func.SetParameter(0, params[0])
sf.Fit(func, "", "", *fit_range)
canvas.addHistogram(func, drawOpt='')
canvas.xtitle = vtitle
canvas.ylimits = (0.8, 1.2)
canvas.Update()
if type(binning) is tuple:
canvas.addLine(binning[0], 1., binning[2], 1., style=ROOT.kDashed)
sf.GetXaxis().SetLimits(binning[1], binning[2])
else:
canvas.addLine(binning[0], 1., binning[-1], 1., style=ROOT.kDashed)
sf.GetXaxis().SetLimits(binning[0], binning[-1])
sf.GetYaxis().SetRangeUser(0.8, 1.2)
canvas.printWeb(outName,
'sf_' + oname + '_' + mname1 + '_' + mname2 + '_' +
tname + '_' + vname,
logy=False)
tree.Add('/scratch5/yiiyama/hist/triggertree/t2mit/filefi/042/' + allsamples['sph-d4'].directory + '/l1t_*.root')
binning = array.array('d', [30. + 5. * x for x in range(14)] + [100. + 10. * x for x in range(10)] + [200. + 20. * x for x in range(5)])
passing = ROOT.TH1D('passing', ';p_{T}^{#gamma} (GeV)', len(binning) - 1, binning)
denom = ROOT.TH1D('denom', ';p_{T}^{#gamma} (GeV)', len(binning) - 1, binning)
tree.Draw('pt>>denom')
tree.Draw('pt>>passing', 'l1dr2 < 0.25')
eff = ROOT.TGraphAsymmErrors(passing, denom)
canvas = SimpleCanvas(lumi = allsamples['sph-d3'].lumi + allsamples['sph-d4'].lumi)
canvas.legend.setPosition(0.7, 0.3, 0.9, 0.5)
canvas.legend.add('eff', title = 'L1 seed', opt = 'LP', color = ROOT.kBlack, mstyle = 8)
canvas.SetGrid()
canvas.legend.apply('eff', eff)
canvas.addHistogram(eff, drawOpt = 'EP')
canvas.addLine(0., 1., 300., 1.)
eff.GetXaxis().SetLimits(0., 300.)
canvas.xtitle = 'p_{T}^{#gamma} (GeV)'
canvas.ytitle = 'L1 seed eff.'
canvas.ylimits = (0., 1.2)
canvas.printWeb('trigger', 'l1seed', logy = False)
if y > 5.:
y = 5.
if y < -5.:
y = -5.
graph.SetPoint(ip, x, y)
graph.SetTitle('')
outputFile.cd()
graph.Write('%s_%s' % (sigs, faken))
graphs.append(graph)
canvas.legend.apply('n%s' % faken, graph)
canvas.addHistogram(graph, drawOpt='P')
canvas.addLine(-5., 0., 5., 0., style=ROOT.kDashed)
canvas.addLine(0., -5., 0., 5., style=ROOT.kDashed)
canvas.xlimits = (-5., 5.)
canvas.ylimits = (-5., 5.)
canvas.title = '#sigma#timesBR = %.2f' % mu
canvas.xtitle = 'Fake E_{T}^{miss}: (N_{fit} - N_{true})/#sigma_{fit}'
canvas.ytitle = 'Signal: (N_{fit} - N_{true})/#sigma_{fit}'
canvas.printWeb('monophoton/fakemet', sourceName + '_' + sigs, logy=False)
canvas.Clear()
outputFile.Close()
if x > 2.:
x = 2.
if y > 2.:
y = 2.
graph.SetPoint(ip, x, y)
graph.SetTitle('')
outputFile.cd()
graph.Write('%s_%s' % (sigs, faken))
graphs.append(graph)
canvas.legend.apply('n%s' % faken, graph)
canvas.addHistogram(graph, drawOpt='P')
canvas.addLine(0., 1., 2., 1., style=ROOT.kDashed)
canvas.addLine(1., 0., 1., 2., style=ROOT.kDashed)
canvas.xlimits = (0., 2.)
canvas.ylimits = (0., 2.)
canvas.title = '#sigma#timesBR = %.2f' % mu
canvas.xtitle = 'Fake E_{T}^{miss} extracted / injected'
canvas.ytitle = 'Signal extracted / injected'
canvas.printWeb('monophoton/fakemet', sigs, logy=False)
canvas.Clear()
outputFile.Close()