if fr:
floatVars = [ fr.floatParsFinal().at(i).GetName() \
for i in range(0, fr.floatParsFinal().getSize()) ]
fitter.ws.defineSet('floatingParams', ','.join(floatVars))
fitter.ws.saveSnapshot("fitPars", ','.join(floatVars))
ndf = fr.floatParsFinal().getSize() - \
fitter.ws.set('constraintSet').getSize()
fr.Print('v')
print '%i free parameters in the fit' % ndf
firstCurve1 = plot1.getObject(1)
# firstCurve1.Print()
(chi2_1, ndf_1) = pulls.computeChi2(plot1.getHist('theData'), firstCurve1)
pull1 = pulls.createPull(plot1.getHist('theData'), firstCurve1)
firstCurve2 = plot2.getObject(1)
# firstCurve2.Print()
(chi2_2, ndf_2) = pulls.computeChi2(plot2.getHist('theData'), firstCurve2)
pull2 = pulls.createPull(plot2.getHist('theData'), firstCurve2)
chi2 = chi2_1 + chi2_2
ndf = ndf_1 + ndf_2 - ndf
cp1 = TCanvas("cp1", fitter.ws.var(pars.var[0]).GetTitle() + ' pull')
pull1.Draw('ap')
pull1.SetName(pars.var[0] + "_pull")
cp1.SetGridy()
if fr:
floatVars = [ fr.floatParsFinal().at(i).GetName() \
for i in range(0, fr.floatParsFinal().getSize()) ]
fitter.ws.defineSet('floatingParams', ','.join(floatVars))
fitter.ws.saveSnapshot("fitPars", ','.join(floatVars))
ndf = fr.floatParsFinal().getSize() - \
fitter.ws.set('constraintSet').getSize()
fr.Print('v')
print '%i free parameters in the fit' % ndf
firstCurve1 = plot1.getObject(1)
# firstCurve1.Print()
(chi2_1, ndf_1) = pulls.computeChi2(plot1.getHist('theData'), firstCurve1)
pull1 = pulls.createPull(plot1.getHist('theData'), firstCurve1)
chi2 = chi2_1
ndf = ndf_1-ndf
cp1 = TCanvas("cp1", xvar.GetTitle() + ' pull')
pull1.Draw('ap')
pull1.SetName(xvar.GetTitle() + "_pull")
cp1.SetGridy()
cp1.Update()
pull1.GetXaxis().SetLimits(pars.varRanges[pars.var[0]][1],
pars.varRanges[pars.var[0]][2])
pull1.GetXaxis().SetTitle(xvar.getTitle(True).Data())
pull1.GetYaxis().SetTitle("pull (#sigma)")
cp1.Update()
if fr:
floatVars = [ fr.floatParsFinal().at(i).GetName() \
for i in range(0, fr.floatParsFinal().getSize()) ]
fitter.ws.defineSet('floatingParams', ','.join(floatVars))
fitter.ws.saveSnapshot("fitPars", ','.join(floatVars))
ndf = fr.floatParsFinal().getSize() - \
fitter.ws.set('constraintSet').getSize()
# fr.Print('v')
print '%i free parameters in the fit' % ndf
firstCurve1 = plot1.getObject(1)
# firstCurve1.Print()
(chi2_1, ndf_1) = pulls.computeChi2(plot1.getHist('theData'), firstCurve1)
pull1 = pulls.createPull(plot1.getHist('theData'), firstCurve1)
chi2 = chi2_1
ndf = ndf_1-ndf
cp1 = TCanvas("cp1", pars.var[0] + ' pull')
pull1.Draw('ap')
pull1.SetName(pars.var[0] + "_pull")
cp1.SetGridy()
cp1.Update()
pull1.GetXaxis().SetLimits(pars.varRanges[pars.var[0]][1],
pars.varRanges[pars.var[0]][2])
pull1.GetXaxis().SetTitle(fitter.ws.var(pars.var[0]).getTitle(True).Data())
pull1.GetYaxis().SetTitle("pull (#sigma)")
RooFit.LineStyle(kDashed))
if ret:
sigPlot.getCurve().SetTitle('tail')
# sigPlot.GetYaxis().SetTitle('Events / GeV')
sigPlot.getHist('theData').SetTitle('%s MC' % compName)
leg = RooWjj2DFitter.Wjj2DFitter.legend4Plot(sigPlot)
sigPlot.addObject(leg)
sigPlot.Draw()
c1.Update()
sigPlot.SetMinimum(1e-6)
cans.append(c1)
plots.append(sigPlot)
(chi2_1, ndf_1) = pulls.computeChi2(sigPlot.getHist('theData'),
sigPlot.getCurve('fitCurve'))
chi2s.append(chi2_1)
ndfs.append(ndf_1)
residuals = pulls.createResid(sigPlot.getHist('theData'),
sigPlot.getCurve('fitCurve'))
plots.append(residuals)
residuals.SetName('%s_Residuals' % par)
c3 = TCanvas('c%i_residual' % i, par + ' residual')
c3.SetGridy()
residuals.Draw('ap')
residuals.GetXaxis().SetLimits(fitter.ws.var(par).getMin(),
fitter.ws.var(par).getMax())
c3.Update()
cans.append(c3)
fitter4.loadData()
fitter4.make4BodyPdf(theFitter)
fitter4.loadParameters(sigYieldFilename)
## assert(False)
mf4 = fitter4.stackedPlot(False, RooWjjMjjFitter.mlnujj)
mf4.SetName("Mlvjj_Stacked")
## sf4 = fitter4.residualPlot(mf4, "h_background", "dibosonPdf", False)
# pf4 = fitter4.residualPlot(mf4, "h_total", "", True)
pf4 = pulls.createPull(mf4.getHist('theData'), mf4.getCurve('h_total'))
pf4.SetName("Mlvjj_Pull")
rf4 = pulls.createResid(mf4.getHist('theData'), mf4.getCurve('h_total'))
rf4.SetName("Mlvjj_Residuals")
(chi2_4, ndf_4) = pulls.computeChi2(mf4.getHist('theData'), mf4.getCurve('h_total'))
lf4 = fitter4.stackedPlot(True, RooWjjMjjFitter.mlnujj)
lf4.SetName("Mlvjj_log")
fitUtils = RooWjjFitterUtils(pars4)
iwt = 0
if (opts.mH >= 500):
iwt = 1
sigHists = HWWSignalShapes.GenHiggsHists(pars4, opts.mH, fitUtils, iwt = iwt)
extraFactor = 2.
otherdata = HWWSignalShapes.NgenHiggs(opts.mH, 'ggH')
SigVisual = TH1D(sigHists['ggH'])
SigVisual.Print()
SigVisual.SetName('SigVisual')
SigVisual.SetLineColor(kBlue)
def mjjfitting(fitter):
if opts.ws:
fitter.loadWorkspaceFromFile(opts.ws, getFloatPars = False,
wsname = 'w_mjj')
fitter.readParametersFromFile()
fitter.expectedFromPars()
fitter.resetYields()
startpars = totalPdf.getParameters(fitter.ws.set('obsSet'))
fitter.ws.defineSet("params", startpars)
fitter.ws.saveSnapshot("initPars", startpars)
if opts.toy and not opts.ws:
#generate toy dataset
print 'Generated parameters'
fitter.ws.set('params').Print('v')
fitter.ws.saveSnapshot("genPars", startpars)
data = totalPdf.generate(fitter.ws.set('obsSet'), RooFit.Name('data_obs'),
RooFit.Extended())
if fitter.pars.binData:
data = RooDataHist('data_obs', 'data_obs', fitter.ws.set('obsSet'),
data)
data.Print('v')
getattr(fitter.ws, 'import')(data)
else:
data = fitter.loadData()
data.Print()
startpars.IsA().Destructor(startpars)
print 'Time elapsed: %.1f sec' % timer.RealTime()
print 'CPU time used: %.1f sec' % timer.CpuTime()
print 'starting fitting routine'
timer.Continue()
fitter.ws.var('diboson_nrm').setConstant()
fitter.ws.var('top_nrm').setConstant()
fitter.ws.var('r_signal').setVal(1.0)
fitter.ws.var('r_signal').setError(0.04)
fr = fitter.fit(overrideRangeCmd=True)
plot1 = fitter.stackedPlot(pars.var[0])
sigPdf = fitter.ws.pdf('ggH')
n_sig = fitter.ws.var('n_ggH').getVal() + fitter.ws.var('n_qqH').getVal()
print "N HWW:", n_sig
sigPdf.plotOn(plot1, RooFit.Range('plotRange'),
RooFit.NormRange('plotRange'),
RooFit.Normalization(n_sig*sigScaler, RooAbsReal.NumEvent),
RooFit.Name('signal_HWW'),
RooFit.LineColor(kBlue+2))
plot1.getCurve().SetTitle("H(%i)#times%.0f" % (opts.mH, sigScaler))
leg1 = RooWjj2DFitter.Wjj2DFitter.legend4Plot(plot1)
c1 = TCanvas('c1', pars.var[0] + ' plot')
plot1.addObject(leg1)
plot1.Draw()
c1.Update()
ndf = 0
if fr:
floatVars = [ fr.floatParsFinal().at(i).GetName() \
for i in range(0, fr.floatParsFinal().getSize()) ]
fitter.ws.defineSet('floatingParams', ','.join(floatVars))
fitter.ws.saveSnapshot("fitPars", ','.join(floatVars))
ndf = fr.floatParsFinal().getSize() - \
fitter.ws.set('constraintSet').getSize()
# fr.Print('v')
print '%i free parameters in the fit' % ndf
firstCurve1 = plot1.getObject(1)
# firstCurve1.Print()
(chi2_1, ndf_1) = pulls.computeChi2(plot1.getHist('theData'), firstCurve1)
pull1 = pulls.createPull(plot1.getHist('theData'), firstCurve1)
chi2 = chi2_1
ndf = ndf_1-ndf
cp1 = TCanvas("cp1", pars.var[0] + ' pull')
pull1.Draw('ap')
pull1.SetName(pars.var[0] + "_pull")
cp1.SetGridy()
cp1.Update()
pull1.GetXaxis().SetLimits(pars.varRanges[pars.var[0]][1],
pars.varRanges[pars.var[0]][2])
pull1.GetXaxis().SetTitle(fitter.ws.var(pars.var[0]).getTitle(True).Data())
pull1.GetYaxis().SetTitle("pull (#sigma)")
blinder = plot1.findObject('TBox')
blinder2 = None
if blinder:
blinder2 = TBox(pars.exclude[pars.var[0]][0], pull1.GetYaxis().GetXmin(),
pars.exclude[pars.var[0]][1], pull1.GetYaxis().GetXmax())
blinder2.SetFillColor(blinder.GetFillColor())
blinder2.SetFillStyle(blinder.GetFillStyle())
if blinder2:
#blinder2.Print()
blinder2.Draw()
cp1.Update()
print 'Time elapsed: %.1f sec' % timer.RealTime()
print 'CPU time used: %.1f sec' % timer.CpuTime()
timer.Continue()
return (plot1, pull1, chi2, ndf, fr)
# errs.Print('v')
# upper.Print('v')
# lower.Print('v')
c_mWW = TCanvas('c_mWW', pars_mWW.var[0] + ' plot')
plot_mWW.addObject(leg_mWW)
plot_mWW.Draw()
# if blinder:
# plot_mWW.setInvisible('theData', True)
c_mWW.Update()
c_mWW_err = TCanvas('c_mWW_err', 'with errors')
plot_mWW_withErrs.Draw()
c_mWW_err.Update()
(chi2_mWW, ndf_mWW) = pulls.computeChi2(plot_mWW.getHist('theData'),
plot_mWW.getObject(1))
if fr_mWW:
ndf_mWW -= fr_mWW.floatParsFinal().getSize() - \
fitter_mWW.ws.set('constraintSet').getSize()
pull_mWW = pulls.createPull(plot_mWW.getHist('theData'),
plot_mWW.getObject(1))
cp_mWW = TCanvas('cp_mWW', pars_mWW.var[0] + ' mWW pull')
pull_mWW.SetName('mWW_pull')
pull_mWW.Draw('ap')
cp_mWW.SetGridy()
cp_mWW.Update()
pull_mWW.GetXaxis().SetLimits(
pars_mWW.varRanges[pars_mWW.var[0]][1],
pars_mWW.varRanges[pars_mWW.var[0]][2]
)
pull_mWW.GetXaxis().SetTitle(
ndf = 0
if fr:
floatVars = [fr.floatParsFinal().at(i).GetName() for i in range(0, fr.floatParsFinal().getSize())]
fitter.ws.defineSet("floatingParams", ",".join(floatVars))
fitter.ws.saveSnapshot("fitPars", ",".join(floatVars))
ndf = fr.floatParsFinal().getSize() - fitter.ws.set("constraintSet").getSize()
# fr.Print('v')
print "%i free parameters in the fit" % ndf
firstCurve1 = plot1.getObject(1)
# firstCurve1.Print()
(chi2_1, ndf_1) = pulls.computeChi2(plot1.getHist("theData"), firstCurve1)
pull1 = pulls.createPull(plot1.getHist("theData"), firstCurve1)
chi2 = chi2_1
ndf = ndf_1 - ndf
cp1 = TCanvas("cp1", vName1 + " pull")
pull1.Draw("ap")
pull1.SetName(vName1 + "_pull")
cp1.SetGridy()
cp1.Update()
pull1.GetXaxis().SetLimits(pars.varRanges[pars.var[0]][1], pars.varRanges[pars.var[0]][2])
pull1.GetXaxis().SetTitle(fitter.ws.var(vName1).getTitle(True).Data())
pull1.GetYaxis().SetTitle("pull (#sigma)")
if fr:
floatVars = [ fr.floatParsFinal().at(i).GetName() \
for i in range(0, fr.floatParsFinal().getSize()) ]
fitter.ws.defineSet('floatingParams', ','.join(floatVars))
fitter.ws.saveSnapshot("fitPars", ','.join(floatVars))
ndf = fr.floatParsFinal().getSize() - \
fitter.ws.set('constraintSet').getSize()
# fr.Print('v')
print '%i free parameters in the fit' % ndf
firstCurve1 = plot1.getObject(1)
# firstCurve1.Print()
(chi2_1, ndf_1) = pulls.computeChi2(plot1.getHist('theData'), firstCurve1)
pull1 = pulls.createPull(plot1.getHist('theData'), firstCurve1)
chi2 = chi2_1
ndf = ndf_1 - ndf
cp1 = TCanvas("cp1", vName1 + ' pull')
pull1.Draw('ap')
pull1.SetName(vName1 + "_pull")
cp1.SetGridy()
cp1.Update()
pull1.GetXaxis().SetLimits(pars.varRanges[pars.var[0]][1],
pars.varRanges[pars.var[0]][2])
pull1.GetXaxis().SetTitle(fitter.ws.var(vName1).getTitle(True).Data())
pull1.GetYaxis().SetTitle("pull (#sigma)")
RooFit.LineStyle(kDashed))
if ret:
sigPlot.getCurve().SetTitle('tail')
# sigPlot.GetYaxis().SetTitle('Events / GeV')
sigPlot.getHist('theData').SetTitle('%s MC' % compName)
leg = RooWjj2DFitter.Wjj2DFitter.legend4Plot(sigPlot)
sigPlot.addObject(leg)
sigPlot.Draw()
c1.Update()
sigPlot.SetMinimum(1e-6)
cans.append(c1)
plots.append(sigPlot)
(chi2_1, ndf_1) = pulls.computeChi2(sigPlot.getHist('theData'),
sigPlot.getCurve('fitCurve'))
chi2s.append(chi2_1)
ndfs.append(ndf_1)
pull = pulls.createPull(sigPlot.getHist('theData'),
sigPlot.getCurve('fitCurve'))
plots.append(pull)
c2 = TCanvas('c%i_pull' % i, par + ' pull')
c2.SetGridy()
pull.Draw('ap')
cans.append(c2)
c1.Print('%s_compFit.png' % opts.bn)
# c1.Print('%s_compFit.pdf' % opts.bn)
mode = 'muon'