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.kBlue + iMod,
lcolor=r.kBlue + iMod,
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:
eff.Write(vname + '_' + tname + '_' + sname)
canvas.SetGrid()
canvas.legend.Clear()
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
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()
canvas.legend.Clear()
canvas.legend.add(gname, title = '#gamma + jet', lcolor = ROOT.kBlack, lwidth = 2)
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')
mcMonoph = ROOT.TChain("skim")
mcMonoph.Add(config.histDir + "/veto_eff/monoph_znng-130.root")
distCanvas = SimpleCanvas("cdist")
distCanvas.legend.add("data", title="2#mu data", opt="L", color=ROOT.kBlack, fstyle=0)
distCanvas.legend.add("dimu", title="2#mu MC", opt="L", color=ROOT.kRed, fstyle=0)
distCanvas.legend.add("monoph", title="Z#gamma MC", opt="L", color=ROOT.kBlue, fstyle=0)
distCanvas.legend.setPosition(0.7, 0.7, 0.9, 0.9)
effCanvas = SimpleCanvas("ceff")
effCanvas.legend.add("data", title="2#mu data", opt="LP", color=ROOT.kBlack, mstyle=8)
effCanvas.legend.add("dimu", title="2#mu MC", opt="LP", color=ROOT.kRed, mstyle=8)
effCanvas.legend.add("monoph", title="Z#gamma MC", opt="LP", color=ROOT.kBlue, mstyle=4)
effCanvas.legend.add("sf", title="2#mu data/MC", opt="LP", color=ROOT.kBlack, mstyle=25, lwidth=2)
effCanvas.legend.setPosition(0.7, 0.7, 0.9, 0.9)
effCanvas.ylimits = (0.9, 1.05)
effCanvas.SetGrid(True)
configs = {
"incl": ("0.5", "", (1, 0.0, 1.0)),
"njet": ("njet", "N_{jet}", (8, 0.0, 8.0)),
"ht": ("ht", "H_{T} (GeV)", [100.0 * x for x in range(5)] + [500.0, 700.0, 900.0, 1200.0, 2000.0]),
"npv": ("npv", "N_{vtx}", (20, 0.0, 40.0)),
}
outputFile = ROOT.TFile.Open(config.histDir + "/veto_eff/veto_eff.root", "recreate")
for name, config in configs.items():
expr, title, binning = config
if type(binning) is tuple:
mcEff.Write('mcEff')
mcTruthEff.Write('mcTruthEff')
### Visualize
lumi = sum(allsamples[s].lumi for s in lumiSamples)
# scaleFactor.SetMaximum(1.05)
canvas = SimpleCanvas(lumi = lumi)
canvas.SetGrid(False, True)
canvas.legend.setPosition(0.7, 0.8, 0.9, 0.9)
canvas.legend.add('sf', 'Scale Factor', opt = 'LP', color = ROOT.kBlack, mstyle = 8)
canvas.legend.add('sf_truth', 'MC truth', opt = 'LP', color = ROOT.kGreen, mstyle = 4)
canvas.ylimits = (0.9, 1.10)
canvas.legend.apply('sf_truth', sfTruth)
canvas.addHistogram(sfTruth, drawOpt = 'EP')
canvas.legend.apply('sf', scaleFactor)
canvas.addHistogram(scaleFactor, drawOpt = 'EP')
if ADDFIT:
flat = ROOT.TF1('flat', '[0]', scaleFactor.GetXaxis().GetXmin(), scaleFactor.GetXaxis().GetXmax())
flat.SetParameter(0, 1.)
flat.SetParLimits(0, 0.95, 1.05)
sfTruth.Fit(flat)
canvas.addObject(flat)
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)
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)
dataTreeAll = ROOT.TChain("events")
for sample in sphs:
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)
# print sf, sfErrLow, sfErrLow / sf
if not 'max' in mod:
rcanvas.legend.add("mc"+mod, title = base+mod, mcolor = r.kRed+iMod, lcolor = r.kRed+iMod, lwidth = 2)
rcanvas.legend.apply("mc"+mod, mcEff)
rcanvas.addHistogram(mcEff, drawOpt = 'EP')
# rcanvas.legend.add("data"+mod, title = "Data"+mod, mcolor = r.Blue+iMod, lcolor = r.kBlue+iMod, lwidth = 2)
# rcanvas.legend.apply("data"+mod, dataEff)
# rcanvas.addHistogram(dataEff, drawOpt = 'EP')
canvas.legend.add(loc+'-'+base+mod, title = loc+'-'+base+mod, color = r.kBlack+iMod, lwidth = 2)
canvas.legend.apply(loc+'-'+base+mod, gSF)
canvas.addHistogram(gSF, drawOpt = 'EP')
rcanvas.ylimits = (0.0, 1.3)
rcanvas.ytitle = 'Photon Efficiency'
rcanvas.xtitle = 'E_{T}^{#gamma} (GeV)'
rcanvas.SetGridy(True)
plotName = "efficiency_"+str(loc)+"_"+str(base)
rcanvas.printWeb('purity/'+s.Version+'/ScaleFactors', era+'_'+plotName, logy = False)
# canvas.ylimits = (0.0, 2.0)
canvas.ytitle = 'Photon Scale Factor'
canvas.xtitle = 'E_{T}^{#gamma} (GeV)'
plotName = "scalefactor_"+str(loc)+"_"+str(base)
# canvas.printWeb('purity/'+s.Version+'/ScaleFactors', plotName, logy = False)
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(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', '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)
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()
lcolor=r.kBlack,
lwidth=1)
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()
rcanvas.legend.add("data",
title="data",
mcolor=r.kBlack,
lcolor=r.kBlack,
lwidth=2)
rcanvas.legend.apply("data", gDataEff)
rcanvas.addHistogram(gDataEff, drawOpt='EP')
canvas.legend.add(loc + '-' + base,
title=loc + ' ' + base,
color=r.kBlack,
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.addObs(dataHist, title = 'Data')
canvas.legend.add('data', title = '#Delta#phi(tag,probe) > 3. && '+varDef.cut, mcolor = r.kBlack, lcolor = r.kBlack, lwidth = 1)
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()
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)
tfact = hists[1].Clone(tname)
tfact.Divide(hists[0])
tfact.GetYaxis().SetTitle("")
tfact.SetMarkerStyle(8)
tfact.SetMarkerSize(0.8)
outputFile.cd()
tfact.Write()
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.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(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 = (0.1, 250000.)
canvas.SetLogy(True)
canvas.printWeb('monophoton/hadronTFactor', 'distributions'+iso[0]+samp)
canvas.Clear()
canvas.legend.Clear()
if samp == 'Down':
canvas.ylimits = (0., -1.)
else:
canvas.ylimits = (0., 0.15)
canvas.SetLogy(False)
canvas.legend.add(tname, title = 'transfer factor', lcolor = ROOT.kBlack, lwidth = 2)
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 dataType == 'mc':
trueResult.Write()
trueYields[meas[0]].Write()
trueYields[meas[1]].Write()
### Visualize
lumi = sum(allsamples[s].lumi for s in lumiSamples)
canvas = SimpleCanvas(lumi=lumi, sim=(dataType == 'mc'))
canvas.SetGrid(False, True)
canvas.legend.setPosition(0.7, 0.8, 0.9, 0.9)
if PRODUCT == 'frate':
result.SetMaximum(0.05)
canvas.legend.add(PRODUCT, 'R_{e}', opt='LP', color=ROOT.kBlack, mstyle=8)
canvas.ylimits = (0., 0.05)
else:
canvas.legend.add(PRODUCT,
'#epsilon_{e}',
opt='LP',
color=ROOT.kBlack,
mstyle=8)
canvas.ylimits = (0.75, 1.)
if dataType == 'mc':
canvas.legend.add(PRODUCT + '_truth',
'MC truth',
opt='LP',
color=ROOT.kGreen,
mstyle=4)
canvas.legend.apply(PRODUCT + '_truth', trueResult)
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:
canvas.printWeb(plotDir, vardef.name)
if addLinear:
tfact = hists[1].Clone(tname)
tfact.Divide(hists[0])
tfact.GetYaxis().SetTitle("")
tfact.SetMarkerStyle(8)
tfact.SetMarkerSize(0.8)
outputFile.cd()
tfact.Write()
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)
mctruth.append(ratio)
outputFile.cd()
frate.Write()
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)
opt='LP',
color=ROOT.kRed,
mstyle=8)
effCanvas.legend.add('monoph',
title='Z#gamma MC',
opt='LP',
color=ROOT.kBlue,
mstyle=4)
effCanvas.legend.add('sf',
title='2#mu data/MC',
opt='LP',
color=ROOT.kBlack,
mstyle=34,
lwidth=2)
effCanvas.legend.setPosition(0.7, 0.7, 0.9, 0.9)
effCanvas.ylimits = (0.9, 1.05)
effCanvas.SetGrid(True)
# plot configs
configs = {
'incl': ('0.5', '', (1, 0., 1.)),
'njet': ('jets.size', 'N_{jet}', (8, 0., 8.)),
'ht': ('Sum$(jets.pt_)', 'H_{T} (GeV)',
[100. * x for x in range(5)] + [500., 700., 900., 1200., 2000.]),
'npv': ('npv', 'N_{vtx}', (20, 0., 40.))
}
outputFile = ROOT.TFile.Open(config.histDir + '/veto_eff/veto_eff.root',
'recreate')
for name, config in configs.items():
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(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)
purityFile.write("\n")
purityFile.write(r"\end{tabular}")
purityFile.write("\n")
purityFile.write(r"\end{document}")
purityFile.close()
suffix = [ "central", "error" ]
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.legend.Clear()
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', clone=False)
canvas.xtitle = vtitle
canvas.ylimits = (0., 1.1)
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)
if dataType == 'mc':
trueResult.Write()
trueYields[meas[0]].Write()
trueYields[meas[1]].Write()
### Visualize
lumi = sum(allsamples[s].lumi for s in lumiSamples)
canvas = SimpleCanvas(lumi=lumi, sim=(dataType == 'mc'))
canvas.SetGrid(False, True)
canvas.legend.setPosition(0.7, 0.8, 0.9, 0.9)
if PRODUCT == 'frate':
result.SetMaximum(0.05)
canvas.legend.add(PRODUCT, 'R_{e}', opt='LP', color=ROOT.kBlack, mstyle=8)
canvas.ylimits = (0., .1)
else:
canvas.legend.add(PRODUCT,
'#epsilon_{e}',
opt='LP',
color=ROOT.kBlack,
mstyle=8)
canvas.ylimits = (0.75, -1.)
if dataType == 'mc':
canvas.legend.add(PRODUCT + '_truth',
'MC truth',
opt='LP',
color=ROOT.kGreen,
mstyle=4)
canvas.legend.apply(PRODUCT + '_truth', trueResult)
