lwidth=2)
canvas.legend.apply(base + mod, pGraph)
canvas.addHistogram(pGraph, drawOpt='EP')
outFile.cd()
pGraph.Write()
canvas.ylimits = (0.0, 15.0)
canvas.ytitle = 'Photon Impurity'
canvas.xtitle = 'E_{T}^{#gamma} (GeV)'
canvas.SetGridy(True)
plotName = 'Plot_' + tune + '_impurity_' + str(metCut) + '_' + str(
loc) + '_' + str(base)
canvas.printWeb('purity/' + s.Version + '/Fitting',
plotName,
logy=False)
outFile.Close()
for loc in s.Locations[:1]:
for base in bases:
for metCut in MetSels:
for iMod, mod in enumerate(mods):
# start new table
purityFileName = 'table_' + tune + '_impurity_' + str(
metCut) + '_' + str(loc) + '_' + str(base + mod) + '.tex'
purityFilePath = outDir + '/' + purityFileName
purityFile = open(purityFilePath, 'w')
xString + ">>" + sample + region,
str(lumi) +
'* weight * (photons.pt[0] > 175. && !photons.pixelVeto[0] && t1Met.minJetDPhi > 0.5 && t1Met.met > 170.)',
'goff')
else:
dataTree.Draw(
xString + ">>" + sample + region,
str(lumi) +
'* weight * (photons.pt[0] > 175. && t1Met.minJetDPhi > 0.5 && t1Met.met > 170.)',
'goff')
print "Integrals for", sample, region
print "Events in dPhi < 0.52:", dataHist.Integral(1, 5)
print "Events in dPhi < 1.05:", dataHist.Integral(1, 10)
print "Events in dPhi > 1.99:", dataHist.Integral(
19,
dataHist.GetNbinsX() + 1)
print '\n'
canvas.legend.add('data',
title=sample + region,
mcolor=ROOT.kRed,
msize=1,
lcolor=ROOT.kRed,
lwidth=4)
canvas.legend.apply('data', dataHist)
canvas.addHistogram(dataHist, drawOpt='HIST')
canvas.printWeb('monophoton/phoMet',
'dPhi' + '_' + sample + '_' + region,
logy=False)
# this distribution will be convoluted with mumug efficiency
if name == 'ht':
monophHt = monophDist
# znng efficiency
monophEff = eff.Clone('monoph_' + eff.GetName())
effCanvas.legend.apply('monoph', monophEff)
mcMonoph.Draw(
'electrons.size == 0 && muons.size == 0:' + expr + '>>' +
monophEff.GetName(), weight, 'prof goff')
# print plots
distCanvas.addHistogram(dataDist, drawOpt='HIST')
distCanvas.addHistogram(mumugDist, drawOpt='HIST')
distCanvas.addHistogram(monophDist, drawOpt='HIST')
distCanvas.printWeb('veto_eff', 'dist_' + name)
distCanvas.Clear()
effCanvas.addHistogram(dataEff, drawOpt='EP')
effCanvas.addHistogram(mumugEff, drawOpt='EP')
if name == 'incl':
effCanvas.addHistogram(datajetEff, drawOpt='EP')
effCanvas.addHistogram(mumugjetEff, drawOpt='EP')
effCanvas.addHistogram(monophEff, drawOpt='EP')
effCanvas.addHistogram(sf, drawOpt='P')
effCanvas.printWeb('veto_eff', 'eff_' + name, logy=False)
effCanvas.Clear()
effCanvas.SetGrid(True)
# save histograms
outputFile.cd()
for iH, hList in enumerate(histograms):
canvas.Clear()
canvas.addHistogram(hList[0], drawOpt = 'HIST')
canvas.addHistogram(hList[2], drawOpt = 'HIST')
canvas.addHistogram(hList[1], drawOpt = 'HIST')
if iH:
canvas.ylimits = (0., 100.)
else:
if pid == 'none':
canvas.ylimits = (0., 30.)
else:
canvas.ylimits = (0., 10.)
plotName = "purity_data_"+str(loc)+"_"+str(pid)+"_ptbinned_"+suffix[iH]
canvas.printWeb('purity/'+Version+'/Fitting', plotName, logy = False)
canvas.Clear()
canvas.legend.Clear()
canvas.legend.add("final", title = str(loc)+" "+str(pid), lcolor = kBlack, lwidth = 2)
canvas.legend.apply("final", finalHist)
iH = canvas.addHistogram(finalHist, drawOpt = 'EP')
if pid == 'none':
canvas.ylimits = (0., 30.)
else:
canvas.ylimits = (0., 10.)
plotName = "purity_data_"+str(loc)+"_"+str(pid)+"_ptbinned_final"
canvas.printWeb('purity/'+Version+'/Fitting', plotName, hList = [iH], logy = False)
pdflatex = Popen( ["pdflatex",purityFilePath,"-interaction nonstopmode"]
,stdout=PIPE,stderr=PIPE,cwd=outDir)
yields['ee'].Write()
yields['eg'].Write()
lumi = 0.
for sname in lumiSamples:
lumi += allsamples[sname].lumi
canvas = SimpleCanvas(lumi = lumi, sim = (dataType == 'mc'))
canvas.legend.setPosition(0.7, 0.8, 0.9, 0.9)
canvas.legend.add('frate', 'R_{e}', opt = 'LP', color = ROOT.kBlack, mstyle = 8)
canvas.legend.apply('frate', frate)
canvas.ylimits = (0., 0.03)
canvas.addHistogram(frate, drawOpt = 'EP')
canvas.xtitle = binningTitle
canvas.printWeb('efake', 'frate_' + dataType + '_' + binningName, logy = False)
for iBin, (binName, cut) in enumerate(fitBins):
if dataType == 'mc':
print '%15s [%.3f +- %.3f (stat.) +- %.3f (syst.)] x 10^{-2} (mc %.3f)' % (binName, contents[iBin] * 100., staterrs[iBin] * 100., systerrs[iBin] * 100., mctruth[iBin] * 100.)
else:
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_160_6500'
for conf in ['ee', 'eg']:
canvas.Clear(full = True)
canvas.ylimits = (0., 0.05)
canvas.xtitle = 'N_{Z}'
toydist = toydists[binName][conf]
canvas.Update()
if type(binning) is tuple:
eff.GetXaxis().SetLimits(binning[1], binning[2])
eff.GetXaxis().SetRangeUser(binning[1], binning[2])
else:
eff.GetXaxis().SetLimits(binning[0], binning[-1])
eff.GetXaxis().SetRangeUser(binning[0], binning[-1])
eff.GetYaxis().SetRangeUser(0., 1.2)
# if tname == 'sph165abs' and vname.startswith('pt'):
# canvas.addLine(175., canvas.ylimits[0], 175., canvas.ylimits[1], color = ROOT.kRed, width = 2, style = ROOT.kDashed)
canvas.printWeb(outName,
oname + '_' + mname + '_' + tname + '_' + vname,
logy=False)
source.Close()
# TO BE FIXED
# func = None
# marker = 0.
#
# if (vname, tname) in fitconfs[oname]:
# func = work.factory('FormulaVar::func("(@0 < @1) * @2 * (1. + TMath::Erf((@3 - @2) * @4 / @2 * (@0 - @1))) + (@0 > @1) * (@2 + (@3 - @2) * TMath::Erf(@4 * (@0 - @1)))", {x, turn[170.,0.,300.], norm1[0.5,0.,1.], norm[0.9,0.,1.], rise2[0.1,0.,1.]})')
# params = ROOT.RooArgList(work.arg('turn'), work.arg('norm1'), work.arg('norm'), work.arg('rise2'))
# fitmin = 150.
# fitmax = 250.
#
# marker = 175.
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()
Xmin = min(xmin, Xmin)
Xmax = max(xmax, Xmax)
# sys.stdin.readline()
print Xmin, Xmax
hbase = r.TH1F("hbase"+weight, weight, 100, Xmin, Xmax)
canvas.Clear()
canvas.legend.Clear()
hists = []
for iS, (sample, tree) in enumerate(trees):
hsample = hbase.Clone(sample+weight)
tree.Draw(weight+" >> "+sample+weight, '', "goff")
# if hsample.Integral():
print tree.GetEntries()
print hsample.Integral()
hsample.Scale(1./tree.GetEntries())
canvas.legend.add(sample+weight, title = sample, mcolor = iS+1, lcolor = iS+1, lwidth = 2)
canvas.legend.apply(sample+weight, hsample)
hists.append(hsample)
canvas.addHistogram(hists[iS])
canvas.printWeb('monophoton/compWeights', weight, logy = False)
canvas = SimpleCanvas(lumi = sum(t.lumi for t in targs))
plotName = 'fit'
### fit to halo distribution and parametrize
# draw
dataTree.Draw(phimpipi + '>>haloTemp(40,-{pi},{pi})'.format(pi = math.pi), 'photons.mipEnergy > 4.9 && photons.isEB && photons.scRawPt > 175. && t1Met.met > 140. && photons.chWorstIso < 1.37 && photons.nhIso < 1.06', 'goff')
haloTemp = ROOT.gDirectory.Get('haloTemp')
haloTemp.SetLineColor(ROOT.kBlack)
haloTemp.SetLineWidth(2)
haloTemp.SetTitle(';#phi\'')
canvas.addHistogram(haloTemp)
canvas.xtitle = '#phi\''
canvas.printWeb('monophoton/halo', 'haloTemp', logy = False)
canvas.Clear()
# first get the halo phi values
nHalo = dataTree.Draw(phivar + '>>haloTemp(40,-{pi},{pi})'.format(pi = math.pi), 'photons.mipEnergy > 4.9 && photons.isEB && photons.scRawPt > 175. && t1Met.met > 140. && photons.chWorstIso < 1.37 && photons.nhIso < 1.06', 'goff')
print nHalo, 'halo events'
# dump them into a RooDataSet
haloData = ROOT.RooDataSet('halo', 'halo', phiset)
haloPhi = dataTree.GetV1()
for iHalo in range(nHalo):
phi.setVal(haloPhi[iHalo])
haloData.add(phiset)
# then fit with gaus + gaus + uniform
base = work.factory('Uniform::base({phi})')
canvas.legend.apply('data', dataHist)
canvas.addHistogram(dataHist, drawOpt='EP')
# sys.stdin.readline()
"""
for sample, color, mcTree in mcTrees:
mcName = sample+'_'+label
mcHist = varDef.makeHist(mcName)
mcTree.Draw(varDef.expr+'>>'+mcName, str(lumi)+' * weight * ('+cutString+')')
canvas.addStacked(mcHist, title = sample, color = color)
"""
canvas.ylimits = (0.5, -1.)
canvas.printWeb('monophoton/phoMet/' + probeCut[1], label, logy=False)
for var in vars2D:
allCuts = list(probeCuts)
if var[1] != '':
allCuts.append(var[1])
cutString = ' && '.join(['(%s)' % c for c in allCuts])
print cutString
label = var[0] + '_' + probeCut[1]
canvas.Clear(xmax=0.90)
canvas.legend.Clear()
canvas.legend.setPosition(0.1, 0.7, 0.9, 0.9)
# plot fit
frame = mass.frame(ROOT.RooFit.Range('fitWindow'), ROOT.RooFit.Bins(mass.getBins('fitWindow')))
targHist.plotOn(frame)
model.plotOn(frame)
model.plotOn(frame, ROOT.RooFit.Components(bkgModelName), ROOT.RooFit.LineStyle(ROOT.kDashed), ROOT.RooFit.LineColor(ROOT.kGreen))
frame.SetTitle('')
frame.SetMinimum(0.)
canvas.Clear()
canvas.addHistogram(frame)
if dataType == 'mc':
canvas.legend.apply('mcbkg', hmcbkg)
canvas.addHistogram(hmcbkg)
if pdf == 'altbkg':
canvas.printWeb(plotDir, 'fit_' + dataType + '_altbkg_' + conf + '_' + binName, logy = False)
elif pdf == 'altsig':
canvas.printWeb(plotDir, 'fit_' + dataType + '_altsig_' + conf + '_' + binName, logy = False)
else:
canvas.printWeb(plotDir, 'fit_' + dataType + '_' + conf + '_' + binName, logy = False)
# run toys
nNominal = vals['nsignal'] + vals['nbkg']
ROOT.RooMsgService.instance().setStreamStatus(1, False)
iToy = 0
while iToy < nToys:
iToy += 1
for param, val in vals.items():
if matches.group(1) == 'a':
dma.append((float(matches.group(2)), float(matches.group(3))))
else:
dmv.append((float(matches.group(2)), float(matches.group(3))))
canvas = SimpleCanvas(cms = False)
canvas.SetGrid(True)
gdma = ROOT.TGraph(len(dma))
for iP, (med, dm) in enumerate(dma):
gdma.SetPoint(iP, med, dm)
gdma.SetTitle('DMA;M_{med} (GeV);M_{DM} (GeV)')
gdma.SetMarkerStyle(21)
canvas.addHistogram(gdma, drawOpt = 'P')
canvas.printWeb('signal_points', 'dma', logx = True)
canvas.Clear()
canvas.SetGrid(True)
gdmv = ROOT.TGraph(len(dmv))
for iP, (med, dm) in enumerate(dmv):
gdmv.SetPoint(iP, med, dm)
gdmv.SetTitle('DMV;M_{med} (GeV);M_{DM} (GeV)')
gdmv.SetMarkerStyle(21)
canvas.addHistogram(gdmv, drawOpt = 'P')
canvas.printWeb('signal_points', 'dmv', logx = True)
lwidth=2)
canvas.legend.apply(loc + '-' + base, gSF)
canvas.addHistogram(gSF, drawOpt='EP')
rcanvas.ylimits = (0.75, 1.0)
# rcanvas.rlimits = (0.9, 1.1)
rcanvas.ytitle = 'Pixel Veto Efficiency'
rcanvas.xtitle = 'E_{T}^{#gamma} (GeV)'
rcanvas.SetGridy(True)
suffix = str(tune) + '_' + str(metCut) + '_' + str(
loc) + '_' + str(base)
plotName = 'efficiency_' + suffix
rcanvas.printWeb('purity/' + s.Version + '/ScaleFactors',
plotName,
logy=False)
canvas.ylimits = (0.95, 1.05)
canvas.ytitle = 'Pixel Veto Scale Factor'
canvas.xtitle = 'E_{T}^{#gamma} (GeV)'
canvas.SetGridy(True)
plotName = 'scalefactor_' + suffix
canvas.printWeb('purity/' + s.Version + '/ScaleFactors',
plotName,
logy=False)
outFile.Close()
for loc in s.Locations[:1]:
scanvas.legend.Clear()
scanvas.ylimits = (0.01, 2.5)
scanvas.SetLogy(True)
scanvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
scanvas.legend.add(tname,
title='Transfer factor',
lcolor=r.kBlack,
lwidth=1)
scanvas.legend.apply(tname, tfact)
scanvas.addHistogram(tfact, drawOpt='EP')
scanvas.printWeb('monophoton/gjetsTFactor', 'tfactor' + method)
canvas.Clear()
canvas.legend.Clear()
canvas.SetLogy(True)
canvas.ylimits = (0.01, 2.5)
canvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
canvas.addObs(tfacts[0], 'Data')
canvas.addSignal(tfacts[1], title='MC', color=r.kRed, idx=-1)
canvas.addStacked(tfacts[1], title='MC', idx=-1)
canvas.printWeb('monophoton/gjetsTFactor', 'tfactorRatio', ymax=2.5)
yString = "( ( (photons.e55[0] / TMath::CosH(photons.eta[0])) - photons.pt[0] ) / t1Met.met )"
dataHist = ROOT.TH2D(sample, "", 30, 0., math.pi, 10, -1.0, 1.0)
dataHist.GetXaxis().SetTitle('#Delta#phi(#gamma, E_{T}^{miss})')
dataHist.GetYaxis().SetTitle(
'(E_{55}^{#gamma} - E_{T}^{#gamma}) / E_{T}^{miss}')
dataHist.Sumw2()
dataTree.Draw(
yString + ":" + xString + ">>" + sample,
'(photons.pt[0] > 175. && t1Met.minJetDPhi > 0.5 && t1Met.met > 170.)',
'goff')
ColorGrad()
canvas.addHistogram(dataHist, drawOpt='COLZ TEXT')
canvas.printWeb('monophoton/phoMet',
'DiffVsPhi' + '_' + sample,
logy=False)
canvas.Clear(xmax=0.90)
dataTree = ROOT.TChain('events')
dataTree.Add('/scratch5/ballen/hist/monophoton/skim/sph-d*_monoph.root')
xString = "photons.eta[0]"
yString = "photons.phi[0]"
dataHist = ROOT.TH2D("lego", "", 30, -1.5, 1.5, 30, -math.pi, math.pi)
dataHist.GetXaxis().SetTitle('#eta')
dataHist.GetYaxis().SetTitle('#phi')
dataHist.Sumw2()
dataTree.Draw(
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()
noniso += ' && photons.chIsoMaxX[0][%d] < %f' % (itune, ROOT.cutvalue)
ROOT.gROOT.ProcessLine("cutvalue = panda::XPhoton::nhIsoCuts[%s][0][2];" %
tune)
noniso += ' && (photons.nhIsoX[0][%d] > %f' % (itune, ROOT.cutvalue)
ROOT.gROOT.ProcessLine("cutvalue = panda::XPhoton::phIsoCuts[%s][0][2];" %
tune)
noniso += ' || photons.phIsoX[0][%d] > %f)' % (itune, ROOT.cutvalue)
outputFile.cd()
addPlot(
haloPlotter, 'phiHalo',
'photons.sieie[0] > 0.015 && photons.mipEnergy[0] > 4.9 && metFilters.globalHalo16'
)
addPlot(emjetPlotter, 'phiCand', noniso)
addPlot(mcPlotter, 'phiZnng')
haloPlotter.fillPlots()
emjetPlotter.fillPlots()
mcPlotter.fillPlots()
canvas = SimpleCanvas()
canvas.legend.setPosition(0.4, 0.7, 0.92, 0.92)
canvas.legend.SetTextSize(0.03)
for plot in plots:
canvas.Clear()
canvas.ylimits = (0., plot.GetMaximum() * 1.5)
canvas.addHistogram(plot)
canvas.printWeb('monophoton/halo', plot.GetName(), logy=False)
canvas.legend.add(fname, title = '#gamma + jet #times impurity', lcolor = ROOT.kRed, lwidth = 2, lstyle = ROOT.kDashed)
canvas.legend.add(hname, title = 'EMobject + jet', lcolor = ROOT.kBlue, lwidth = 2)
canvas.legend.setPosition(0.6, 0.7, 0.95, 0.9)
canvas.legend.apply(gname, gpt)
canvas.legend.apply(fname, fpt)
canvas.legend.apply(hname, hpt)
canvas.addHistogram(gpt, drawOpt = 'HIST')
canvas.addHistogram(fpt, drawOpt = 'HIST')
canvas.addHistogram(hpt, drawOpt = 'HIST')
canvas.ylimits = (0.1, 2000.)
canvas.SetLogy(True)
canvas.printWeb('monophoton/hadronTFactor', 'distributions'+iso[0]+samp)
canvas.Clear()
canvas.legend.Clear()
canvas.ylimits = (0., -1.)
canvas.SetLogy(False)
canvas.legend.add(tname, title = 'Transfer factor', lcolor = ROOT.kBlack, lwidth = 1)
canvas.legend.apply(tname, tfact)
canvas.addHistogram(tfact, drawOpt = 'EP')
canvas.printWeb('monophoton/hadronTFactor', 'tfactor'+iso[0]+samp)
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)
canvas.legend.apply(gname, gpt)
canvas.legend.apply(fname, fpt)
canvas.legend.apply(fname + 'Syst', fptUp)
canvas.legend.apply(fname + 'Syst', fptDown)
canvas.legend.apply(hname, hpt)
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' + 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)
dataTreeAll.Add(config.ntuplesDir + "/" + sample.book + "/" + sample.fullname + "/*.root")
znngTree = ROOT.TChain("events")
znngTree.Add(config.photonSkimDir + "/znng-130.root")
## halo EB
phiHalo = makeFullPlot(dataTree, "phiHalo", "photons.mipEnergy > 4.9 && photons.isEB")
phiHalo.SetDirectory(outputFile)
outputFile.cd()
phiHalo.Write()
canvas.ylimits = (0.0, phiHalo.GetMaximum() * 1.5)
canvas.addHistogram(phiHalo)
canvas.printWeb("monophoton/halo", "phiHalo", logy=False)
canvas.Clear()
phiHalo = makeFoldedPlot(dataTree, "phiHaloFolded", "photons.mipEnergy > 4.9 && photons.isEB")
phiHalo.SetDirectory(outputFile)
outputFile.cd()
phiHalo.Write()
canvas.ylimits = (0.0, phiHalo.GetMaximum() * 1.5)
canvas.addHistogram(phiHalo)
canvas.printWeb("monophoton/halo", "phiHaloFolded", logy=False)
canvas.Clear()
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()
def printCanvas(canvas, plotdef, plotConfig, plotDir):
"""
Print the canvas content as pdf and png.
"""
eil = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kWarning
canvas.xtitle = plotdef.xtitle()
canvas.ytitle = plotdef.ytitle(binNorm=True)
canvas.selection = plotdef.cutName
if plotdef.logy is None:
logy = True
addLinear = True
else:
logy = plotdef.logy
addLinear = False
canvas.ylimits = (plotdef.ymin, plotdef.ymax)
canvas.rlimits = (plotdef.rmin, plotdef.rmax)
canvas.Update(logy=logy)
if plotdef.fullyBlinded():
# remove ratio pad. Hack to use SimpleCanvas interface
simple = SimpleCanvas(lumi=canvas.lumi)
garbage = []
cnv = ROOT.TCanvas('tmp', 'tmp', 600, 600)
cnv.cd()
plotPad = canvas.plotPad.DrawClone()
garbage.append(plotPad)
plotPad.SetTopMargin(simple.canvas.GetTopMargin())
plotPad.SetRightMargin(simple.canvas.GetRightMargin())
plotPad.SetBottomMargin(simple.canvas.GetBottomMargin())
plotPad.SetLeftMargin(simple.canvas.GetLeftMargin())
xaxis = canvas.xaxis.DrawClone()
garbage.append(xaxis)
xaxis.SetX1(simple.canvas.GetLeftMargin())
xaxis.SetX2(1. - simple.canvas.GetRightMargin())
xaxis.SetY1(simple.canvas.GetBottomMargin())
xaxis.SetY2(simple.canvas.GetBottomMargin())
yaxis = canvas.yaxis.DrawClone()
garbage.append(yaxis)
yaxis.SetX1(simple.canvas.GetLeftMargin())
yaxis.SetX2(simple.canvas.GetLeftMargin())
yaxis.SetY1(simple.canvas.GetBottomMargin())
yaxis.SetY2(1. - simple.canvas.GetTopMargin())
simple.canvas.IsA().Destructor(simple.canvas)
simple.canvas = cnv
simple._needUpdate = False
simple.printWeb(plotDir, plotdef.name)
sys.stdout.write(' main')
if logy:
sys.stdout.write(' (log)')
else:
sys.stdout.write(' (lin)')
if addLinear:
plotPad.SetLogy(False)
plotPad.Update()
yaxis.SetOption('')
yaxis.SetWmin(plotPad.GetUymin())
yaxis.SetWmax(plotPad.GetUymax())
simple.printWeb(plotDir, plotdef.name + 'Linear', logy=False)
sys.stdout.write(' (lin)')
sys.stdout.write('\n')
sys.stdout.flush()
# cleanup the mess
for obj in garbage:
obj.IsA().Destructor(obj)
cnv.IsA().Destructor(cnv)
else:
# normal, (partially) unblinded distributions
canvas.printWeb(plotDir, plotdef.name, drawLegend=False)
sys.stdout.write(' main')
if logy:
sys.stdout.write(' (log)')
else:
sys.stdout.write(' (lin)')
if addLinear:
canvas.ylimits = (0., -1.)
canvas.minimum = -1.
plotdef.ymax = -1.
plotdef.ymin = 0.
canvas._needUpdate = True
canvas.printWeb(plotDir, plotdef.name + 'Linear', logy=False)
sys.stdout.write(' (lin)')
sys.stdout.write('\n')
sys.stdout.flush()
ROOT.gErrorIgnoreLevel = eil
dataCorr = ROOT.TH1D('dataCorr', '', 60, 60., 120.)
dataRaw = ROOT.TH1D('dataRaw', '', 60, 60., 120.)
dataChain.Draw('tp.mass>>dataCorr', 'probes.pt > 100.', 'goff')
dataChain.Draw(rawMass + '>>dataRaw', 'probes.pt > 100.', 'goff')
canvas = SimpleCanvas()
canvas.legend.setPosition(0.7, 0.7, 0.9, 0.9)
canvas.legend.add('dataCorr', title = 'Corrected', opt = 'LF', color = ROOT.kBlue, fstyle = 3003, lwidth = 2)
canvas.legend.add('dataRaw', title = 'Raw', opt = 'LF', color = ROOT.kRed, fstyle = 3003, lwidth = 2)
canvas.addHistogram(dataCorr)
canvas.addHistogram(dataRaw)
canvas.applyStyles()
canvas.printWeb('rawEScale', 'data', logy = False)
canvas.Clear()
mcCorr = ROOT.TH1D('mcCorr', '', 60, 60., 120.)
mcRaw = ROOT.TH1D('mcRaw', '', 60, 60., 120.)
mcChain.Draw('tp.mass>>mcCorr', 'probes.pt > 100.', 'goff')
mcChain.Draw(rawMass + '>>mcRaw', 'probes.pt > 100.', 'goff')
canvas = SimpleCanvas()
canvas.legend.setPosition(0.7, 0.7, 0.9, 0.9)
canvas.legend.add('mcCorr', title = 'Corrected', opt = 'LF', color = ROOT.kBlue, fstyle = 3003, lwidth = 2)
canvas.legend.add('mcRaw', title = 'Raw', opt = 'LF', color = ROOT.kRed, fstyle = 3003, lwidth = 2)
canvas.addHistogram(mcCorr)
canvas.addHistogram(mcRaw)
lcolor=ROOT.kGreen,
lwidth=1,
opt='L')
canvas.legend.add('obs',
title='Data',
mcolor=ROOT.kBlack,
mstyle=8,
msize=1,
lcolor=ROOT.kBlack,
lwidth=1,
opt='LP')
canvas.ytitle = 'Events / (#pi/%d)' % nbins
canvas.addHistogram(frame)
canvas.printWeb('monophoton/halo', 'fit_' + fitName, logy=False)
if not FITPSEUDODATA:
# generate 10 toy distributions - does your target distribution look "normal"?
for iT in range(10):
toyData = model.generate(phiset, nTarg)
frame = phi.frame()
frame.SetTitle('')
toyData.plotOn(frame)
canvas.Clear(full=True)
canvas.ytitle = 'Events / (#pi/25)'
canvas.addHistogram(frame)
canvas.printWeb('monophoton/halo', 'toy%d' % iT, logy=False)
toyPlotYield.Fill(yieldDiff)
bkgdUncertainty = toyPlot.GetStdDev()
bkgdUncYield = toyPlotYield.GetStdDev()
tcanvas = SimpleCanvas(lumi = s.sphLumi, name = 'toys')
toyPlot.SetTitle('')
tcanvas.legend.add('toys', title = 'toys', opt = 'L', lcolor = ROOT.kBlue, lwidth = 2, lstyle = ROOT.kSolid)
tcanvas.legend.apply('toys', toyPlot)
tcanvas.addHistogram(toyPlot)
tcanvas.xtitle = 'Impurity Difference (%)'
tcanvas.ytitle = '# of Toys'
tcanvas.printWeb(plotDir, 'ssfit_toy_dist', logy = False)
tcanvas.Clear()
toyPlotYield.SetTitle('')
tcanvas.legend.apply('toys', toyPlotYield)
tcanvas.addHistogram(toyPlotYield)
tcanvas.xtitle = '#Delta(# of True Photons)'
tcanvas.ytitle = '# of Toys'
tcanvas.printWeb(plotDir, 'ssfit_toyyield_dist', logy = False)
else:
bkgdUncertainty = 0.
}
for sample in samples:
for region in samples[sample]:
canvas.Clear()
canvas.legend.Clear()
canvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
dataTree = ROOT.TChain('events')
dataTree.Add('/scratch5/ballen/hist/monophoton/skim/'+sample+'-*_'+region+'.root')
xString = "t1Met.photonDPhi"
dataHist = ROOT.TH1D(sample+region, "", 30, 0., math.pi)
dataHist.GetXaxis().SetTitle('#Delta#phi(#gamma, E_{T}^{miss})')
dataHist.GetYaxis().SetTitle('Events / 0.10')
dataHist.Sumw2()
if sample == 'sph':
dataTree.Draw(xString+">>"+sample+region, '(photons.pt[0] > 175. && t1Met.minJetDPhi > 0.5 && t1Met.met > 170.)', 'goff')
else:
dataTree.Draw(xString+">>"+sample+region, str(lumi)+'* weight * (photons.pt[0] > 175. && t1Met.minJetDPhi > 0.5 && t1Met.met > 170.)', 'goff')
canvas.legend.add('data', title = sample+region, mcolor = ROOT.kRed, msize = 1, lcolor = ROOT.kRed, lwidth = 4)
canvas.legend.apply('data', dataHist)
canvas.addHistogram(dataHist, drawOpt = 'HIST')
canvas.printWeb('monophoton/phoMet', 'dPhi'+'_'+sample+'_'+region, logy = False)
canvas.legend.apply('data', dataHist)
canvas.addHistogram(dataHist, drawOpt = 'EP')
# sys.stdin.readline()
"""
for sample, color, mcTree in mcTrees:
mcName = sample+'_'+label
mcHist = varDef.makeHist(mcName)
mcTree.Draw(varDef.expr+'>>'+mcName, str(lumi)+' * weight * ('+cutString+')')
canvas.addStacked(mcHist, title = sample, color = color)
"""
canvas.ylimits = (0.5, -1.)
canvas.printWeb('monophoton/phoMet/'+probeCut[1], label, logy = False)
for var in vars2D:
allCuts = list(probeCuts)
if var[1] != '':
allCuts.append(var[1])
cutString = ' && '.join(['(%s)' % c for c in allCuts])
print cutString
label = var[0]+'_'+probeCut[1]
canvas.Clear(xmax = 0.90)
canvas.legend.Clear()
canvas.legend.setPosition(0.1, 0.7, 0.9, 0.9)
# plot fit
frame = mass.frame(ROOT.RooFit.Range('fitWindow'), ROOT.RooFit.Bins(mass.getBins('fitWindow')))
targHist.plotOn(frame)
model.plotOn(frame)
model.plotOn(frame, ROOT.RooFit.Components(bkgModelName), ROOT.RooFit.LineStyle(ROOT.kDashed), ROOT.RooFit.LineColor(ROOT.kGreen))
frame.SetTitle('')
frame.SetMinimum(0.)
canvas.addHistogram(frame)
if dataType == 'mc':
canvas.legend.apply('mcbkg', hmcbkg)
canvas.addHistogram(hmcbkg)
if pdf == 'altbkg':
canvas.printWeb('efake', 'fit_' + dataType + '_altbkg_' + conf + '_' + binName, logy = False)
elif pdf == 'altsig':
canvas.printWeb('efake', 'fit_' + dataType + '_sigbkg_' + conf + '_' + binName, logy = False)
else:
canvas.printWeb('efake', 'fit_' + dataType + '_' + conf + '_' + binName, logy = False)
# run toys
nNominal = vals['nsignal'] + vals['nbkg']
ROOT.RooMsgService.instance().setStreamStatus(1, False)
iToy = 0
while iToy < nToys:
iToy += 1
for param, val in vals.items():
monophDist = dist.Clone('monoph_' + dist.GetName())
distCanvas.legend.apply('monoph', monophDist)
mcMonoph.Draw(expr + '>>' + monophDist.GetName(), weight, 'goff')
monophDist.Scale(1. / monophDist.GetSumOfWeights(), scaleopt)
if name == 'ht':
monophHt = monophDist
monophEff = eff.Clone('monoph_' + eff.GetName())
effCanvas.legend.apply('monoph', monophEff)
mcMonoph.Draw('1. - (eleveto || muveto):' + expr + '>>' + monophEff.GetName(), weight, 'prof goff')
distCanvas.addHistogram(dataDist, drawOpt = 'HIST')
distCanvas.addHistogram(mumugDist, drawOpt = 'HIST')
distCanvas.addHistogram(monophDist, drawOpt = 'HIST')
distCanvas.printWeb('veto_eff', 'dist_' + name)
distCanvas.Clear()
effCanvas.addHistogram(dataEff, drawOpt = 'EP')
effCanvas.addHistogram(mumugEff, drawOpt = 'EP')
if name == 'incl':
effCanvas.addHistogram(datajetEff, drawOpt = 'EP')
effCanvas.addHistogram(mumugjetEff, drawOpt = 'EP')
effCanvas.addHistogram(monophEff, drawOpt = 'EP')
effCanvas.addHistogram(sf, drawOpt = 'P')
effCanvas.printWeb('veto_eff', 'eff_' + name, logy = False)
effCanvas.Clear()
effCanvas.SetGrid(True)
outputFile.cd()
for iX in range(hnominal.GetNbinsX() + 2):
if ROOT.TMath.IsNaN(hnominal.GetBinContent(iX)) or ROOT.TMath.IsNaN(hnominal.GetBinError(iX)):
print hnominal.GetName(), 'cannot be plotted because it contains NaN'
plot = False
break
if huncert and (ROOT.TMath.IsNaN(huncert.GetBinContent(iX)) or ROOT.TMath.IsNaN(huncert.GetBinError(iX))):
print huncert.GetName(), 'cannot be plotted because it contains NaN'
plot = False
break
if not plot:
continue
canvas1.legend.apply('stat', hnominal)
if huncert:
canvas1.Clear()
canvas1.legend.apply('total', huncert)
canvas1.addHistogram(huncert, drawOpt = 'E2')
canvas1.addHistogram(hnominal, drawOpt = 'EP')
canvas1.printWeb(plotDir, hnominal.GetName(), logy = not proc.startswith('tf_'))
else:
canvas2.Clear()
canvas2.addHistogram(hnominal, drawOpt = 'EP')
canvas2.printWeb(plotDir, hnominal.GetName(), logy = not proc.startswith('tf_'))
rcanvas = RatioCanvas(name = 'datamc', lumi = 36400)
rcanvas.legend.setPosition(0.7, 0.7, 0.9, 0.9)
linear.SetParLimits(0, 0.95, 1.05)
linear.SetParLimits(1, -0.0001, 0.0001)
sfTruth.Fit(linear)
canvas.addObject(linear)
text = 'flat = %.3f #pm %.3f' % (flat.GetParameter(0), flat.GetParError(0))
canvas.addText(text, 0.3, 0.25, 0.5, 0.2)
text = 'line = %.6f + %.6f * p_{T}' % (linear.GetParameter(0), linear.GetParameter(1))
canvas.addText(text, 0.3, 0.35, 0.5, 0.25)
canvas.xtitle = binningTitle
canvas.printWeb(outputName, 'scaleFactor_' + binningName, logy = False)
print 'Fit Results:'
for iBin, (bin, _) in enumerate(fitBins):
print '%15s [%.3f +- %.3f]' % (bin, scaleFactor.GetBinContent(iBin + 1), scaleFactor.GetBinError(iBin + 1))
print '\nTruth Results:'
for iBin, (bin, _) in enumerate(fitBins):
print '%15s [%.3f +- %.3f]' % (bin, sfTruth.GetBinContent(iBin + 1), sfTruth.GetBinError(iBin + 1))
outFileName = 'table_' + binningName + '.tex'
outDir = WEBDIR + '/' + outputName
outFilePath = outDir + '/' + outFileName
outFile = open(outFilePath, 'w')
lowEdge = float(ptCut.split('t')[2])
highEdge = ptCut.split('to')[-1]
if highEdge == 'Inf':
highEdge = 500.
highEdge = float(highEdge)
center = (lowEdge + highEdge) / 2.
exl = center - lowEdge
exh = highEdge - center
purity = purities[loc][base+mod][ptCut][metCut]
pGraph.SetPoint(iB, center, purity[0])
pGraph.SetPointError(iB, exl, exh, purity[1], purity[1])
if not 'max' in mod:
canvas.legend.add(base+mod, title = base+mod, mcolor = r.kOrange+iMod-1, lcolor = r.kOrange+iMod-1, lwidth = 2)
canvas.legend.apply(base+mod, pGraph)
canvas.addHistogram(pGraph, drawOpt = 'EP')
outFile.cd()
pGraph.Write()
canvas.ylimits = (0.0, 15.0)
canvas.ytitle = 'Photon Impurity'
canvas.xtitle = 'E_{T}^{#gamma} (GeV)'
canvas.SetGridy(True)
plotName = "impurity_"+str(loc)+"_"+str(base)
canvas.printWeb('purity/'+s.Version+'/Fitting', era+'_'+plotName, logy = False)
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)
stack.Add(sig)
bkgTrue.Scale(1., 'width')
bkgTrue.SetLineColor(ROOT.kGreen + 2)
bkgTrue.SetLineWidth(2)
fakeTrue.Scale(fakeNorm / fakeTrue.GetSumOfWeights(), 'width')
fakeTrue.SetLineColor(ROOT.kRed + 2)
fakeTrue.SetLineWidth(2)
sigTrue.Scale(sigScale, 'width')
sigTrue.SetLineColor(ROOT.kBlue + 2)
sigTrue.SetLineWidth(2)
data.SetMarkerStyle(8)
data.SetLineColor(ROOT.kBlack)
data.SetLineWidth(1)
canvas.title = '#sigma#timesBR = %.2f, N_{fake} = %.0f' % (originalMu *
sigScale, fakeNorm)
canvas.xtitle = 'm_{T} (GeV)'
canvas.ytitle = 'Events / GeV'
canvas.addHistogram(stack, drawOpt='HIST')
canvas.addHistogram(bkgTrue, drawOpt='HIST')
canvas.addHistogram(fakeTrue, drawOpt='HIST')
canvas.addHistogram(sigTrue, drawOpt='HIST')
canvas.addHistogram(data, drawOpt='EP')
canvas.printWeb('monophoton/fakemet', name, logy=False)
xaxis.SetX1(simple.canvas.GetLeftMargin())
xaxis.SetX2(1. - simple.canvas.GetRightMargin())
xaxis.SetY1(simple.canvas.GetBottomMargin())
xaxis.SetY2(simple.canvas.GetBottomMargin())
yaxis = canvas.yaxis.DrawClone()
garbage.append(yaxis)
yaxis.SetX1(simple.canvas.GetLeftMargin())
yaxis.SetX2(simple.canvas.GetLeftMargin())
yaxis.SetY1(simple.canvas.GetBottomMargin())
yaxis.SetY2(1. - simple.canvas.GetTopMargin())
simple.canvas.IsA().Destructor(simple.canvas)
simple.canvas = cnv
simple._needUpdate = False
simple.printWeb(plotDir, vardef.name)
if addLinear:
simple.ylimits = (0., -1.)
simple.minimum = -1.
vardef.ymax = -1.
simple._needUpdate = True
simple.printWeb(plotDir, vardef.name + 'Linear', logy = False)
# cleanup the mess
for obj in garbage:
obj.IsA().Destructor(obj)
cnv.IsA().Destructor(cnv)
else:
Xmin = min(xmin, Xmin)
Xmax = max(xmax, Xmax)
# sys.stdin.readline()
print Xmin, Xmax
hbase = r.TH1F("hbase" + weight, weight, 100, Xmin, Xmax)
canvas.Clear()
canvas.legend.Clear()
hists = []
for iS, (sample, tree) in enumerate(trees):
hsample = hbase.Clone(sample + weight)
tree.Draw(weight + " >> " + sample + weight, '', "goff")
# if hsample.Integral():
print tree.GetEntries()
print hsample.Integral()
hsample.Scale(1. / tree.GetEntries())
canvas.legend.add(sample + weight,
title=sample,
mcolor=iS + 1,
lcolor=iS + 1,
lwidth=2)
canvas.legend.apply(sample + weight, hsample)
hists.append(hsample)
canvas.addHistogram(hists[iS])
canvas.printWeb('monophoton/compWeights', weight, logy=False)
reweighted += efficiency.GetBinContent(iX) * nproxy
tpDist.SetBinContent(iX, ntp)
original /= tpDist.GetSumOfWeights() # should in principle match the nominal pT > 40 GeV efficiency
reweighted /= proxyDist.GetSumOfWeights()
print 'Original fake rate =', 1. / original - 1.
print 'Reweighted fake rate =', 1. / reweighted - 1.
proxyDist.Scale(1., 'width')
tpDist.Scale(1., 'width')
proxyDist.Scale(8. / proxyDist.GetSumOfWeights())
tpDist.Scale(8. / tpDist.GetSumOfWeights())
canvas = SimpleCanvas()
canvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
canvas.legend.add('inefficiency', title = '1 - #epsilon_{e}', opt = 'LP', mstyle = 8, color = ROOT.kBlack)
canvas.legend.add('tp', title = 'T&P sample', opt = 'LF', color = ROOT.kRed - 7, fstyle = 3003, lwidth = 2)
canvas.legend.add('proxy', title = 'W#rightarrowe#nu', opt = 'LF', color = ROOT.kBlue - 7, fstyle = 3003, lwidth = 2)
canvas.addHistogram(inefficiency, drawOpt = 'EP')
canvas.addHistogram(tpDist)
canvas.addHistogram(proxyDist)
canvas.applyStyles()
canvas.ytitle = '1 - #epsilon (%)'
canvas.xtitle = title
canvas.printWeb('efake', 'convolution_' + variable, logy = False)
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)
monophDist = dist.Clone("monoph_" + dist.GetName())
distCanvas.legend.apply("monoph", monophDist)
mcMonoph.Draw(expr + ">>" + monophDist.GetName(), weight, "goff")
monophDist.Scale(1.0 / monophDist.GetSumOfWeights(), scaleopt)
if name == "ht":
monophHt = monophDist
monophEff = eff.Clone("monoph_" + eff.GetName())
effCanvas.legend.apply("monoph", monophEff)
mcMonoph.Draw("1. - (eleveto || muveto):" + expr + ">>" + monophEff.GetName(), weight, "prof goff")
distCanvas.addHistogram(dataDist, drawOpt="HIST")
distCanvas.addHistogram(dimuDist, drawOpt="HIST")
distCanvas.addHistogram(monophDist, drawOpt="HIST")
distCanvas.printWeb("veto_eff", "dist_" + name)
distCanvas.Clear()
effCanvas.addHistogram(dataEff, drawOpt="EP")
effCanvas.addHistogram(dimuEff, drawOpt="EP")
if name == "incl":
effCanvas.addHistogram(datajetEff, drawOpt="EP")
effCanvas.addHistogram(dimujetEff, drawOpt="EP")
effCanvas.addHistogram(monophEff, drawOpt="EP")
effCanvas.addHistogram(sf, drawOpt="P")
effCanvas.printWeb("veto_eff", "eff_" + name, logy=False)
effCanvas.Clear()
effCanvas.SetGrid(True)
outputFile.cd()
gup.SetLineColor(power.GetLineColor())
gup.SetLineWidth(power.GetLineWidth())
canvas.addHistogram(gup, drawOpt='CL')
gdown.SetLineStyle(ROOT.kDashed)
gdown.SetLineColor(power.GetLineColor())
gdown.SetLineWidth(power.GetLineWidth())
canvas.addHistogram(gdown, drawOpt='CL')
outputFile.cd()
gup.Write(PRODUCT + '_fitUp')
gdown.Write(PRODUCT + '_fitDown')
canvas.xtitle = binningTitle
canvas.printWeb(outputName,
PRODUCT + '_' + dataType + '_' + binningName,
logy=False)
for iBin, (bin, _) in enumerate(fitBins):
if dataType == 'mc':
print '%15s [%.3f +- %.3f (stat.) +- %.3f (syst.)] x 10^{-2} (mc %.3f)' % (
bin, result.GetBinContent(iBin + 1) * 100., staterrs[iBin] * 100.,
systerrs[iBin] * 100., trueResult.GetBinContent(iBin + 1) * 100.)
else:
print '%15s [%.3f +- %.3f (stat.) +- %.3f (syst.)] x 10^{-2}' % (
bin, result.GetBinContent(iBin + 1) * 100., staterrs[iBin] * 100.,
systerrs[iBin] * 100.)
if uncSource:
for conf in meas:
for bin, _ in fitBins:
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'))
fitmin = 300.
fitmax = 1000.
elif vname == 'ptzoom' and tname == 'hlt' and sname == 'sel':
func = work.factory('FormulaVar::func("(@0 < @1) * @2 * (1. + TMath::Erf((@3 - @2) * @4 / @2 * (@0 - @1))) + (@0 > @1) * (@2 + (@3 - @2) * TMath::Erf(@4 * (@0 - @1)))", {x, turn[170.,0.,300.], norm1[0.5,0.,1.], norm[0.9,0.,1.], rise2[0.1,0.,1.]})')
params = ROOT.RooArgList(work.arg('turn'), work.arg('norm1'), work.arg('norm'), work.arg('rise2'))
fitmin = 150.
fitmax = 250.
tfacts.append(tfact)
scanvas.Clear()
scanvas.legend.Clear()
scanvas.ylimits = (0.01, 2.5)
scanvas.SetLogy(True)
scanvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
scanvas.legend.add(tname, title = 'Transfer factor', lcolor = r.kBlack, lwidth = 1)
scanvas.legend.apply(tname, tfact)
scanvas.addHistogram(tfact, drawOpt = 'EP')
scanvas.printWeb('monophoton/gjetsTFactor', 'tfactor'+method)
canvas.Clear()
canvas.legend.Clear()
canvas.ylimits = (0.01, 2.5)
canvas.SetLogy(True)
canvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
canvas.addObs(tfacts[0], 'Data')
canvas.addSignal(tfacts[1], title = 'MC', color = r.kRed, idx = -1)
canvas.addStacked(tfacts[1], title = 'MC', idx = -1)
canvas.printWeb('monophoton/gjetsTFactor', 'tfactorRatio')
for sample in samples:
dataTree = ROOT.TChain('events')
dataTree.Add('/scratch5/ballen/hist/monophoton/skim/sph-d*_'+sample+'.root')
xString = "t1Met.photonDPhi"
yString = "( ( (photons.e55[0] / TMath::CosH(photons.eta[0])) - photons.pt[0] ) / t1Met.met )"
dataHist = ROOT.TH2D(sample, "", 30, 0., math.pi, 10, -1.0, 1.0)
dataHist.GetXaxis().SetTitle('#Delta#phi(#gamma, E_{T}^{miss})')
dataHist.GetYaxis().SetTitle('(E_{55}^{#gamma} - E_{T}^{#gamma}) / E_{T}^{miss}')
dataHist.Sumw2()
dataTree.Draw(yString+":"+xString+">>"+sample, '(photons.pt[0] > 175. && t1Met.minJetDPhi > 0.5 && t1Met.met > 170.)', 'goff')
ColorGrad()
canvas.addHistogram(dataHist, drawOpt = 'COLZ TEXT')
canvas.printWeb('monophoton/phoMet', 'DiffVsPhi'+'_'+sample, logy = False)
canvas.Clear(xmax = 0.90)
dataTree = ROOT.TChain('events')
dataTree.Add('/scratch5/ballen/hist/monophoton/skim/sph-d*_monoph.root')
xString = "photons.eta[0]"
yString = "photons.phi[0]"
dataHist = ROOT.TH2D("lego", "", 30, -1.5, 1.5, 30, -math.pi, math.pi)
dataHist.GetXaxis().SetTitle('#eta')
dataHist.GetYaxis().SetTitle('#phi')
dataHist.Sumw2()
dataTree.Draw(yString+":"+xString+">>lego", 'photons.pt[0] > 175. && t1Met.minJetDPhi > 0.5 && t1Met.met > 170. && t1Met.photonDPhi < 2.', 'goff')
color=ROOT.kBlue,
fstyle=3003,
lwidth=2)
canvas.legend.add('dataRaw',
title='Raw',
opt='LF',
color=ROOT.kRed,
fstyle=3003,
lwidth=2)
canvas.addHistogram(dataCorr)
canvas.addHistogram(dataRaw)
canvas.applyStyles()
canvas.printWeb('rawEScale', 'data', logy=False)
canvas.Clear()
mcCorr = ROOT.TH1D('mcCorr', '', 60, 60., 120.)
mcRaw = ROOT.TH1D('mcRaw', '', 60, 60., 120.)
mcChain.Draw('tp.mass>>mcCorr', 'probes.pt > 100.', 'goff')
mcChain.Draw(rawMass + '>>mcRaw', 'probes.pt > 100.', 'goff')
canvas = SimpleCanvas()
canvas.legend.setPosition(0.7, 0.7, 0.9, 0.9)
canvas.legend.add('mcCorr',
title='Corrected',
opt='LF',
color=ROOT.kBlue,
fstyle=3003,
