for iBin in range(1, nOutputBins + 1):
sigVal = sigScoreHist.GetBinContent(iBin)
bkgVal = bkgScoreHist.GetBinContent(iBin)
sigScoreHist.SetBinContent(iBin, 1.)
if sigVal > 0.:
bkgScoreHist.SetBinContent(iBin, bkgVal / sigVal)
else:
bkgScoreHist.SetBinContent(iBin, 0)
sigScoreHist.Scale(1. / sigScoreHist.Integral())
bkgScoreHist.Scale(1. / bkgScoreHist.Integral())
sigScoreHist.SetLineColor(r.kBlue)
sigScoreHist.Draw('hist')
bkgScoreHist.SetLineColor(r.kRed)
bkgScoreHist.Draw('hist,same')
useSty.drawCMS()
useSty.drawEnPu(lumi='35.9 fb^{-1}')
theCanv.SaveAs('%s/outputScores.pdf' % plotDir)
#draw sig vs background distribution
theCanv = useSty.setCanvas()
sigScoreW = diphoTestFW * (diphoTestY == 1)
sigScoreHist = r.TH1F('sigScoreHist', 'sigScoreHist', nOutputBins, 0., 1.)
useSty.formatHisto(sigScoreHist)
sigScoreHist.SetTitle('')
sigScoreHist.GetXaxis().SetTitle('Diphoton BDT score')
fill_hist(sigScoreHist, altDiphoPredY, weights=sigScoreW)
bkgScoreW = diphoTestFW * (diphoTestY == 0)
bkgScoreHist = r.TH1F('bkgScoreHist', 'bkgScoreHist', nOutputBins, 0., 1.)
useSty.formatHisto(bkgScoreHist)
bkgScoreHist.SetTitle('')
def crossCheck(self, lumi, plotDir):
'''Run a check to ensure the random search found a good mimimum'''
for iName, name in enumerate(self.names):
for iCat in range(self.nCats):
best = self.boundaries[name][iCat]
rnge = 0.2 * self.highs[name] - self.lows[name]
graph = r.TGraph()
for iVal, val in enumerate(
np.arange(best - rnge / 2., best + rnge / 2.,
rnge / 10.)):
sigs = []
bkgs = []
nons = []
cuts = {}
cuts[name] = self.boundaries[name]
cuts[name][iCat] = val
bests = Bests(self.nCats)
for jCat in range(self.nCats):
lastCat = (jCat + 1 == self.nCats)
sigWeights = self.sigWeights
bkgWeights = self.bkgWeights
if self.addNonSig: nonSigWeights = self.nonSigWeights
for jName, jname in enumerate(self.names):
sigWeights = sigWeights * (self.sigDiscrims[jname]
> cuts[jname][jCat])
bkgWeights = bkgWeights * (self.bkgDiscrims[jname]
> cuts[jname][jCat])
if self.addNonSig:
nonSigWeights = nonSigWeights * (
self.nonSigDiscrims[jname] >
cuts[jname][jCat])
if not lastCat:
if jName == 0 or self.sortOthers:
sigWeights = sigWeights * (
self.sigDiscrims[jname] <
cuts[jname][jCat + 1])
bkgWeights = bkgWeights * (
self.bkgDiscrims[jname] <
cuts[jname][jCat + 1])
if self.addNonSig:
nonSigWeights = nonSigWeights * (
self.nonSigDiscrims[jname] <
cuts[jname][jCat + 1])
sigHist = r.TH1F('sigHistTemp', 'sigHistTemp', 160,
100, 180)
fill_hist(sigHist, self.sigMass, weights=sigWeights)
sigCount = 0.68 * lumi * sigHist.Integral()
sigWidth = self.getRealSigma(sigHist)
bkgHist = r.TH1F('bkgHistTemp', 'bkgHistTemp', 160,
100, 180)
fill_hist(bkgHist, self.bkgMass, weights=bkgWeights)
bkgCount = self.computeBkg(bkgHist, sigWidth)
if self.addNonSig:
nonSigHist = r.TH1F('nonSigHistTemp',
'nonSigHistTemp', 160, 100,
180)
fill_hist(nonSigHist,
self.nonSigMass,
weights=nonSigWeights)
nonSigCount = 0.68 * lumi * nonSigHist.Integral()
else:
nonSigCount = 0.
sigs.append(sigCount)
bkgs.append(bkgCount)
nons.append(nonSigCount)
bests.update(sigs, bkgs, nons)
graph.SetPoint(iVal, val - best, bests.getTotSignif())
canv = useSty.setCanvas()
graphName = 'CrossCheck_%s_Cat%g' % (name, iCat)
graph.SetTitle(graphName.replace('_', ' '))
graph.GetXaxis().SetTitle('Cut value - chosen value')
graph.GetYaxis().SetTitle('Significance (#sigma)')
graph.Draw()
useSty.drawCMS(text='Internal')
useSty.drawEnPu(lumi=lumi)
canv.Print('%s/%s.pdf' % (plotDir, graphName))
canv.Print('%s/%s.png' % (plotDir, graphName))
for iBin, iPar in enumerate(pars.values()):
for jBin, jPar in enumerate(pars.values()):
proc = theMatrix.GetXaxis().GetBinLabel(iPar + 1)
#print 'Got proc %s, expecting proc %s'%(proc, revPars[iPar])
theVal = theMatrix.GetBinContent(iPar + 1, jPar + 1)
#print 'Value for correlation between %s and %s is %.3f'%(revPars[iPar],revPars[jPar],theVal)
theHist.GetXaxis().SetBinLabel(iBin + 1, prettyProc(revPars[iPar]))
theHist.GetYaxis().SetBinLabel(jBin + 1, prettyProc(revPars[jPar]))
theHist.Fill(iBin, jBin, theVal)
set_color_palette('frenchFlag')
r.gStyle.SetNumberContours(500)
r.gStyle.SetPaintTextFormat('1.2f')
#canv = r.TCanvas('canv','canv')
canv = setCanvas()
formatHisto(theHist)
theHist.GetXaxis().SetTickLength(0.)
theHist.GetXaxis().SetLabelSize(0.05)
theHist.GetYaxis().SetTickLength(0.)
theHist.GetYaxis().SetLabelSize(0.05)
theHist.GetZaxis().SetRangeUser(-1., 1.)
theHist.GetZaxis().SetTickLength(0.)
theHist.Draw('colz,text')
drawCMS(True)
drawEnPu(lumi='%2.1f fb^{-1}' % lumi)
canv.Print('Plots/corrMatrixFormal_%s.png' % ext)
canv.Print('Plots/corrMatrixFormal_%s.pdf' % ext)
def main():
for fileName in fullFileNames:
if 'M125' not in fileName: continue
theProc = fileName.split('pythia8_')[1].split('.root')[0]
theProc0 = theProc.split('_')[0]
#if theProc not in procsOfInterest: continue
print 'processing %s'%theProc
theFile = r.TFile(fileName, 'READ')
theWS = theFile.Get('tagsDumper/cms_hgg_13TeV')
for cat in cats:
dataName = '%s_125_13TeV_%s'%(nameMap[theProc0], cat)
sumEntries = theWS.data(dataName).sumEntries()
if sumEntries < 0.: sumEntries = 0.
if theProc not in procsOfInterest:
theProc = 'OTHER'
sumwProcCatMap[ (theProc,cat) ] += sumEntries
sumwProcMap[ theProc ] += sumEntries
sumwCatMap[ cat ] += sumEntries
print 'got all values'
nBinsX = len(procsOfInterest)
nBinsY = len(cats)
procHist = r.TH2F('procHist','procHist', nBinsX, -0.5, nBinsX-0.5, nBinsY, -0.5, nBinsY-0.5)
procHist.SetTitle('')
catHist = r.TH2F('catHist','catHist', nBinsX, -0.5, nBinsX-0.5, nBinsY, -0.5, nBinsY-0.5)
catHist.SetTitle('')
for iProc,proc in enumerate(procsOfInterest):
#print 'iProc,proc',iProc,proc
for iCat,cat in enumerate(cats):
procWeight = 100. * sumwProcCatMap[(proc,cat)] / sumwProcMap[proc]
if procWeight < 0.5: procWeight=-1
catWeight = 100. * sumwProcCatMap[(proc,cat)] / sumwCatMap[cat]
if catWeight < 0.5: catWeight=-1
catLabel = cat.split('ECO_')[1]
catLabel = catLabel.replace('PTH_0_60','low')
catLabel = catLabel.replace('PTH_60_120','med')
catLabel = catLabel.replace('PTH_120_200','high')
catLabel = catLabel.replace('PTH_GT200','BSM')
catLabel = catLabel.replace('GE2J','2J')
catLabel = catLabel.replace('PTJET1_GT200','BSM')
catLabel = catLabel.replace('VBFTOPO','VBF')
catLabel = catLabel.replace('JET3VETO','2J-like')
catLabel = catLabel.replace('JET3','3J-like')
catLabel = catLabel.replace('VH2JET','VH-like')
catLabel = catLabel.replace('REST','rest')
catLabel = catLabel.replace('_',' ')
procHist.Fill( iProc, iCat, procWeight )
procHist.GetXaxis().SetBinLabel( iProc+1, prettyProc(proc) )
procHist.GetYaxis().SetBinLabel( iCat+1, catLabel )
catHist.Fill( iProc, iCat, catWeight )
catHist.GetXaxis().SetBinLabel( iProc+1, prettyProc(proc) )
catHist.GetYaxis().SetBinLabel( iCat+1, catLabel )
#canv = r.TCanvas('canv','canv')
set_color_palette('ed_noice')
canv = setCanvas()
formatHisto(procHist)
procHist.SetStats(0)
procHist.GetXaxis().SetTitle('STXS process')
procHist.GetXaxis().SetTitleOffset(3)
procHist.GetXaxis().SetTickLength(0.)
procHist.GetXaxis().LabelsOption('v')
procHist.GetYaxis().SetTitle('Event category')
procHist.GetYaxis().SetTitleOffset(2.7)
procHist.GetYaxis().SetTickLength(0.)
procHist.GetZaxis().SetTitle('Signal category destination (%)')
procHist.SetMinimum(-0.00001)
procHist.SetMaximum(100.)
procHist.Draw('colz,text')
lines = []
for iProc,proc in enumerate(procsOfInterest):
lines.append(r.TLine(iProc+0.5, -0.5, iProc+0.5, len(cats)-0.5))
lines[-1].SetLineColorAlpha(r.kGray, 0.5)
lines[-1].SetLineWidth(1)
lines.append(r.TLine(-0.5, 17.5, len(procsOfInterest)-0.5, 17.5)) #horiontal ggH VBF divider
lines[-1].SetLineColorAlpha(r.kBlack, 0.5)
lines[-1].SetLineWidth(1)
lines.append(r.TLine(10.5, -0.5, 10.5, len(cats)-0.5)) #vertical ggH VBF divider
lines[-1].SetLineColorAlpha(r.kBlack, 0.5)
lines[-1].SetLineWidth(1)
for line in lines: line.Draw()
drawCMS(True)
#drawEnPu(lumi='%.1f fb^{-1}'%lumi)
drawEnYear(year=opts.year)
canv.Print('procHist_%s.pdf'%ext)
canv.Print('procHist_%s.png'%ext)
formatHisto(catHist)
catHist.SetStats(0)
catHist.GetXaxis().SetTitle('STXS process')
catHist.GetXaxis().SetTitleOffset(3)
catHist.GetXaxis().SetTickLength(0.)
catHist.GetXaxis().LabelsOption('v')
catHist.GetYaxis().SetTitle('Event category')
catHist.GetYaxis().SetTitleOffset(2.7)
catHist.GetYaxis().SetTickLength(0.)
catHist.GetZaxis().SetTitle('Category signal composition (%)')
catHist.SetMinimum(-0.00001)
catHist.SetMaximum(100.)
catHist.Draw('colz,text')
lines = []
for iCat,cat in enumerate(cats):
if cat.count('Tag1') or cat.count('RECO_1J_PTH_GT200') or cat.count('RECO_VBFTOPO_REST'):
lines.append(r.TLine(-0.5, iCat+0.5, len(procsOfInterest)-0.5, iCat+0.5))
lines[-1].SetLineColorAlpha(r.kGray, 0.5)
lines[-1].SetLineWidth(1)
else:
lines.append(r.TLine(-0.5, iCat+0.5, len(procsOfInterest)-0.5, iCat+0.5))
lines[-1].SetLineColorAlpha(r.kGray, 0.25)
lines[-1].SetLineWidth(1)
lines.append(r.TLine(-0.5, 17.5, len(procsOfInterest)-0.5, 17.5)) #horiontal ggH VBF divider
lines[-1].SetLineColorAlpha(r.kBlack, 0.5)
lines[-1].SetLineWidth(1)
lines.append(r.TLine(10.5, -0.5, 10.5, len(cats)-0.5)) #vertical ggH VBF divider
lines[-1].SetLineColorAlpha(r.kBlack, 0.5)
lines[-1].SetLineWidth(1)
for line in lines: line.Draw()
drawCMS(True)
#drawEnPu(lumi='%.1f fb^{-1}'%lumi)
drawEnYear(year=opts.year)
canv.Print('catHist_%s.pdf'%ext)
canv.Print('catHist_%s.png'%ext)
#save hists
outFile = r.TFile('stxsStage1Hists_%s.root'%ext,'RECREATE')
procHist.Write()
catHist.Write()
outFile.Close()
