canvas.addText(rlabel, 0.185, 0.82, 0.35, 0.86, size=0.04, align=12)
resultLegend.setPosition(0.4, 0.88 - 0.04 * len(resultLegend.entries),
0.63, 0.88)
canvas.legend.setPosition(0.63, 0.88 - 0.04 * len(canvas.legend.entries),
0.9, 0.88)
resultLegend.SetTextSize(0.04)
canvas.legend.SetTextSize(0.04)
if config == 'monoph':
canvas.xtitle = 'E_{T}^{#gamma} [GeV]'
else:
canvas.xtitle = 'M_{T}(#gamma, E_{T}^{miss}) [GeV]'
canvas.ytitle = 'Events / GeV'
canvas.rtitle = 'Data / Pred.'
if pdir == 'p':
outname = 'prefit_' + region
elif pdir == 'c':
outname = 'CRonly_' + region
elif pdir == 'b':
outname = 'bonly_' + region
elif pdir == 's':
outname = 'splusb_' + region
resultLegend.construct()
canvas.addObject(resultLegend.legend, clone=False)
canvas.Update(
hList=hList,
def plotFit(mass,
targHist,
model,
dataType,
suffix,
bkgModel='nombkgModel',
hmcbkg=None,
alt='',
plotName=''):
global canvas
if canvas is None:
canvas = RatioCanvas(lumi=lumi, sim=(dataType == 'mc'))
canvas.Clear(full=True)
canvas.titlePave.SetX2NDC(0.5)
canvas.legend.setPosition(0.6, 0.7, 0.9, 0.9)
canvas.legend.add('obs',
title='Observed',
opt='LP',
color=ROOT.kBlack,
mstyle=8)
canvas.legend.add('fit',
title='Fit',
opt='L',
lcolor=ROOT.kBlue,
lwidth=2,
lstyle=ROOT.kSolid)
canvas.legend.add('bkg',
title='Bkg component',
opt='L',
lcolor=ROOT.kGreen,
lwidth=2,
lstyle=ROOT.kDashed)
if hmcbkg:
canvas.legend.add('mcbkg',
title='Bkg (MC truth)',
opt='LF',
lcolor=ROOT.kRed,
lwidth=1,
fcolor=ROOT.kRed,
fstyle=3003)
if targHist.sumEntries() > 500.:
plotBinning = plotBinningT
else:
plotBinning = plotBinningT2
frame = mass.frame()
targHist.plotOn(frame, ROOT.RooFit.Binning(*plotBinning))
model.plotOn(frame)
model.plotOn(frame, ROOT.RooFit.Components(bkgModel + '_' + suffix),
ROOT.RooFit.LineStyle(ROOT.kDashed),
ROOT.RooFit.LineColor(ROOT.kGreen))
frame.SetTitle('')
frame.SetMinimum(0.)
canvas.addHistogram(frame, clone=True, drawOpt='')
if hmcbkg:
htruth = hmcbkg.Rebin(hmcbkg.GetNbinsX() / plotBinning[0], 'truth')
canvas.legend.apply('mcbkg', htruth)
canvas.addHistogram(htruth)
canvas.rlimits = (-2., 2.)
canvas.Update(rList=[], logy=False)
frame.Print()
# adding ratio pad
fitcurve = frame.findObject(model.GetName() + '_Norm[mass]')
hresid = targHist.createHistogram('residual', mass,
ROOT.RooFit.Binning(*plotBinning))
rdata = ROOT.TGraphErrors(hresid.GetNbinsX())
for iP in range(rdata.GetN()):
x = hresid.GetXaxis().GetBinCenter(iP + 1)
nData = hresid.GetBinContent(iP + 1)
statErr = hresid.GetBinError(iP + 1)
nFit = fitcurve.interpolate(x)
if statErr > 0.:
rdata.SetPoint(iP, x, (nData - nFit) / statErr)
else:
rdata.SetPoint(iP, x, (nData - nFit))
# rdata.SetPointError(iP, 0., dmet.GetBinError(iP + 1) / norm)
rdata.SetMarkerStyle(8)
rdata.SetMarkerColor(ROOT.kBlack)
rdata.SetLineColor(ROOT.kBlack)
canvas.ratioPad.cd()
canvas.rtitle = '(data - fit) / #sigma_{data}'
rframe = ROOT.TH1F('rframe', '', 1, *plotBinning[1:])
rframe.GetYaxis().SetRangeUser(-2., 2.)
rframe.Draw()
line = ROOT.TLine(plotBinning[1], 0., plotBinning[2], 0.)
line.SetLineWidth(2)
line.SetLineColor(ROOT.kBlue)
line.Draw()
rdata.Draw('EP')
canvas._needUpdate = False
if not plotName:
plotName = 'fit_' + dataType + '_' + suffix
if alt:
plotName += '_' + alt
canvas.printWeb(plotDir, plotName, logy=False)
rframe.Delete()
if hmcbkg:
htruth.Delete()
hresid.Delete()
nominal.GetXaxis().SetBinLabel(ibin + 1, binlabels[ibin])
measured.SetPoint(ibin, (binning[ibin] + binning[ibin + 1]) * 0.5,
dsigma * cent[0])
measured.SetPointEYhigh(ibin, dsigma * up[0])
measured.SetPointEYlow(ibin, dsigma * down[0])
nominalLine = nominal.Clone('line')
canvas.legend.apply('nominal', nominalLine)
nominalLine.SetFillStyle(0)
canvas.legend.apply('nominal', nominal)
nominal.SetMaximum(nominal.GetMaximum() * 1.2)
canvas.legend.apply('obs', measured)
measured.SetTitle('')
canvas.addHistogram(nominal, drawOpt='E2')
canvas.addHistogram(nominalLine, drawOpt='HIST')
canvas.addHistogram(measured, drawOpt='EP')
canvas.xtitle = nominal.GetXaxis().GetTitle()
canvas.ytitle = nominal.GetYaxis().GetTitle()
canvas.rtitle = 'Fit / prediction'
canvas.printWeb('hww/20190205', 'sigma_%s' % observable, logy=False)
canvas.Clear()
nominalLine.Delete()
nominal.Delete()
