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,
rList=[iLine, iPreUncRatio, iPostUncRatio, iPreRatio, iPostRatio],
logy=True)
zeroBins = []
for iBin in range(1, dataHist.GetNbinsX() + 1):
if dataHist.GetBinContent(iBin) == 0.:
zeroBins.append(iBin)
if len(zeroBins) != 0:
# OK we need to manually edit the ratio histograms after all the acrobat of making inverted ratios..
for prim in canvas.ratioPad.GetListOfPrimitives():
name = prim.GetName().replace('ratio_', '')
if name not in [
'prefitUncRatio', 'postfitUncRatio', 'prefitRatio',
'postfitRatio'
]:
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()
mcpdf.plotOn(frame, ROOT.RooFit.LineColor(ROOT.kRed), ROOT.RooFit.LineStyle(ROOT.kDotted))
# frame.SetTitle('min#Delta#phi(j, E_{T}^{miss}) < 0.5')
#frame.GetXaxis().SetTitle('E_{T}^{miss} (GeV)')
frame.GetXaxis().SetTitle('')
frame.GetXaxis().SetTitleSize(0.)
frame.GetXaxis().SetLabelSize(0.)
frame.GetYaxis().SetLabelSize(0.)
frame.GetYaxis().SetTickSize(0.)
canvas.Clear()
canvas.xtitle = 'E_{T}^{miss} (GeV)'
canvas.ylimits = (0.1, 1000000.)
canvas.addHistogram(frame, clone = True, drawOpt = '')
canvas.Update(rList = [])
fitcurve = frame.findObject('model_Norm[met]')
rawcurve = frame.findObject('mcpdf_Norm[met]')
rdata = ROOT.TGraphErrors(dmet.GetNbinsX())
for iP in range(rdata.GetN()):
x = dmet.GetXaxis().GetBinCenter(iP + 1)
norm = fitcurve.interpolate(x)
rdata.SetPoint(iP, x, dmet.GetBinContent(iP + 1) / norm)
rdata.SetPointError(iP, 0., dmet.GetBinError(iP + 1) / norm)
rdata.SetMarkerStyle(8)
rdata.SetMarkerColor(ROOT.kBlack)
rdata.SetLineColor(ROOT.kBlack)
