def plot_training_phoeres_with_shape_and_fit(): """Plot the nominal MC photon energy smearing overlayed with the pdf shape and fit.""" canvases.next('TrainingPhoEResWithShapeAndFit') plot = phoERes.frame(roo.Range(-7.5, 5)) plot.SetTitle("Photon energy smearing overlayed with PDF shape (blue) " "and it's parametrized fit (dashed red)") data.plotOn(plot) ## Define model for the photon energy smearing function Ereco/Etrue - 1. phoEResPdf = ParametrizedKeysPdf('phoEResPdf', 'phoEResPdf', phoERes, phoScale, phoRes, data, ROOT.RooKeysPdf.NoMirror, 1.5) ## PDF shape phoEResPdf.shape.plotOn(plot) ## Parametrized fit of the PDF shape phoEResPdf.fitTo(data, roo.Range(-50, 50), roo.PrintLevel(-1)) phoEResPdf.plotOn(plot, roo.LineColor(ROOT.kRed), roo.LineStyle(ROOT.kDashed)) plot.Draw() Latex([ 's_{shape}: %.3f %%' % phoEResPdf.shapemode, 's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()), 's_{fit} - s_{shape}: %.4f #pm %.4f %%' % (phoScale.getVal() - phoEResPdf.shapemode, phoScale.getError()), 'r_{shape}: %.3f %%' % phoEResPdf.shapewidth, 'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()), 'r_{fit} - r_{shape}: %.4f #pm %.4f %%' % (phoRes.getVal() - phoEResPdf.shapewidth, phoRes.getError()), 'r_{fit}/r_{shape}: %.4f #pm %.4f' % (phoRes.getVal() / phoEResPdf.shapewidth, phoRes.getError() / phoEResPdf.shapewidth), ], position=(0.2, 0.8)).draw()
def plot_nominal_mmgmass_with_shape_and_fit(): """Plot the nominal MC mmg mass data overlayed with the pdf shape and fit.""" canvases.next('NominalMmgMassWithShapeAndFit') plot = mmgMass.frame(roo.Range(75, 105)) plot.SetTitle("m(#mu#mu#gamma) overlayed with PDF shape (blue) " "and it's parametrized fit (dashed red)") data.plotOn(plot) ## Define the mmg mass model. mmgMassPdf = ParametrizedKeysPdf('mmgMassPdf', 'mmgMassPdf', mmgMass, massPeak, massWidth, data, ROOT.RooKeysPdf.NoMirror, 1.5) ## PDF shape mmgMassPdf.shape.plotOn(plot) ## Parametrized fit of the PDF shape mmgMassPdf.fitTo(data, roo.Range(60, 120), roo.PrintLevel(-1)) mmgMassPdf.plotOn(plot, roo.LineColor(ROOT.kRed), roo.LineStyle(ROOT.kDashed)) plot.Draw() sshape = 100 * (mmgMassPdf.shapemode / mZ.getVal() - 1) rshape = 100 * mmgMassPdf.shapewidth / mmgMassPdf.shapemode Latex([ 's_{shape}: %.3f %%' % sshape, 's_{fit}: %.3f #pm %.3f %%' % (massScale.getVal(), massScale.getError()), 's_{fit} - s_{shape}: %.4f #pm %.4f %%' % (massScale.getVal() - sshape, massScale.getError()), 'r_{shape}: %.3f %%' % rshape, 'r_{fit}: %.3f #pm %.3f %%' % (massRes.getVal(), massRes.getError()), 'r_{fit} - r_{shape}: %.4f #pm %.4f %%' % (massRes.getVal() - rshape, massRes.getError()), 'r_{fit}/r_{shape}: %.4f #pm %.4f' % (massRes.getVal() / rshape, massRes.getError() / rshape), ], position=(0.2, 0.8)).draw()
def loop_over_smearings(): "Extract scale and resolution for mass and energy." ## Set initial value for the mode calculation of the phoERes shape. phoERes.setVal(0) ## Set initial value for the mode calculation of the mmgMass shape. mmgMass.setVal(91.2) ## Define then nominal model for the photon energy smearing function. phoEResPdf = ParametrizedKeysPdf('phoEResPdf_nominal', 'phoEResPdf_nominal', phoERes, phoScale, phoRes, data, ROOT.RooKeysPdf.NoMirror, 1.5) ## Define the nominal mmg mass model. mmgMassPdf = ParametrizedKeysPdf('mmgMassPdf_nominal', 'mmgMassPdf_nominal', mmgMass, massPeak, massWidth, data, ROOT.RooKeysPdf.NoMirror, 1.5) w.Import(phoEResPdf) w.Import(mmgMassPdf) w.Import(phoEResPdf.shape) w.Import(mmgMassPdf.shape) for i, (s, r) in enumerate(zip(stargets, rtargets)): ## Get the smeared data. phoScaleTarget.setVal(s) phoResTarget.setVal(r) sdata = calibrator.get_smeared_data(s, r) ## Save the smeared data in the workspace. sdata.SetName('sdata_%d' % i) sdata.SetTitle('smeared mmg data %d' % i) w.Import(sdata) ## Build the energy and mass models for each smearing since the ## shapes may have changed. ## Guess the right values of the fit parameters. phoScale.setVal(s) phoRes.setVal(phoEResPdf.shapewidth) massScale.setVal(100 * (mmgMassPdf.shapemode / mZ.getVal() - 1)) massRes.setVal(100 * mmgMassPdf.shapewidth / mmgMassPdf.shapemode) ## Fit the models. phoFit = phoEResPdf.fitTo( sdata, roo.Range(-50, 50), # roo.Range(s - 5*r, s + 5*r), # roo.PrintLevel(-1), roo.Save()) massFit = mmgMassPdf.fitTo( sdata, roo.Range(60, 120), # roo.PrintLevel(-1), roo.Save()) ## Store results in the workspace. w.Import(phoFit) w.Import(massFit) w.saveSnapshot('smear_%d' % i, params, True)
def _get_mctruth_scale_and_resolution(self): ## Enlarge the range of the observable to get vanishing tails for ## the photon energy scale resolution # savrange = (self.phoERes.getMin(), self.phoERes.getMax()) # self.phoERes.setRange(savrange[0] - 10, savrange[1] + 10) ## Build the model for the photon energy resolution. self.phoEResPdf = ParametrizedKeysPdf('phoEResPdf', 'phoEResPdf', self.phoERes, self.s, self.r, self.data, ROOT.RooKeysPdf.NoMirror, self.rho) # self.phoERes.setRange(*savrange) ## Set sensible initial values self.s.setVal(self.phoEResPdf.shapemode) self.r.setVal(self.phoEResPdf.shapewidth) ## Extract the MC truth scale and resolution from MC self.fitresult_mctruth = self.phoEResPdf.fitTo( self.data, roo.PrintLevel(self.printlevel), roo.SumW2Error(False), roo.Range(-50, 50), roo.Save(), roo.Strategy(2)) self.w.Import(self.fitresult_mctruth) ## Store the MC truth scale and resolution for source, target in zip([self.s, self.r], [self.s0, self.r0]): target.setVal(source.getVal()) target.setError(source.getError()) target.setAsymError(source.getErrorLo(), source.getErrorHi()) # self.s0.setVal(self.s.getVal()) # self.r0.setVal(self.r.getVal()) self.w.saveSnapshot('sr_mctruth', self.sr) self.w.saveSnapshot('sr0_mctruth', self.sr0) self.s0.setConstant(True) self.r0.setConstant(True)
def _fit_smeared_data(self, name): 'Fit the current smeared data to get the smeared s and r.' self.fitresult_sdata = self.phoEResPdf.fitTo( self.sdata, roo.PrintLevel(self.printlevel), roo.SumW2Error(False), roo.Range(-50, 50), roo.Save(), roo.Strategy(2)) self.w.saveSnapshot(name + '_sr', self.sr) self.w.Import(self.fitresult_sdata, name + '_fitresult')
def plot_fit_to_real_data(label): ''' Plot fit to real data for a dataset specified by the label: "data" (full 2011A+B), "2011A" or "2011B". ''' # mmgMass.setRange('plot', 70, 110) mmgMass.setBins(80) plot = mmgMass.frame(roo.Range('plot')) if label == 'data': title_start = '2011A+B' else: title_start = label plot.SetTitle('%s, %s' % (title_start, latex_title)) data[label].plotOn(plot) pm.plotOn(plot, roo.Range('plot'), roo.NormRange('plot')) pm.plotOn(plot, roo.Range('plot'), roo.NormRange('plot'), roo.Components('*zj*'), roo.LineStyle(ROOT.kDashed)) canvases.next(name + '_' + label).SetGrid() plot.Draw()
def fit_real_data(label): ''' Fit dataset specified by the label: "data" (full 2011A+B), "2011A" or "2011B". ''' fit_result = pm.fitTo(data[label], roo.Range('fit'), roo.NumCPU(8), roo.Timer(), # roo.Verbose() roo.InitialHesse(True), roo.Minos(), roo.Save(), ) w.Import(fit_result, 'fitresult_' + label)
def plot_sanity_checks(data): pdfname = '_'.join(['bananaPdf', data.GetName()]) pdf = ROOT.RooNDKeysPdf(pdfname, pdfname, ROOT.RooArgList(mmMass, mmgMass), data, "a", 1.5) canvases.next(pdf.GetName() + '_mmgMassProj') plot = mmgMass.frame(roo.Range(60, 120)) data.plotOn(plot) pdf.plotOn(plot) plot.Draw() canvases.next(pdf.GetName() + '_mmMassProj') plot = mmMass.frame(roo.Range(10, 120)) data.plotOn(plot) pdf.plotOn(plot) plot.Draw() canvases.next(pdf.GetName()).SetGrid() h_pdf = pdf.createHistogram('h_' + pdf.GetName(), mmMass, roo.Binning(40, 40, 80), roo.YVar(mmgMass, roo.Binning(40, 70, 110))) h_pdf.Draw("cont1") canvases.next(data.GetName()).SetGrid() h_data = data.createHistogram(mmMass, mmgMass, 130, 100, '', 'h_' + data.GetName()) h_data.GetXaxis().SetRangeUser(40, 80) h_data.GetYaxis().SetRangeUser(70, 110) h_data.Draw("cont1") canvases.next('_'.join([pdf.GetName(), 'mmgMassSlices'])) plot = mmgMass.frame(roo.Range(60, 120)) for mmmassval, color in zip([55, 60, 65, 70], 'Red Orange Green Blue'.split()): mmMass.setVal(mmmassval) color = getattr(ROOT, 'k' + color) pdf.plotOn(plot, roo.LineColor(color)) plot.Draw() canvases.update()
def plot_nominal_and_smeared_mmgmass(): canvases.next('SmearedMMGMass').SetGrid() plot = mmgMass.frame(roo.Range(76, 106)) plot.SetTitle("Nominal (black) and smeared (red) mmg mass") data.plotOn(plot) datasmeared.plotOn(plot, roo.MarkerColor(ROOT.kRed), roo.LineColor(ROOT.kRed)) plot.Draw() Latex([ 's_{0}: %.2g %%, s: %.2g %%' % (phoScaleRef, targets), 'r_{0}: %.2g %%, r: %.2g %%' % (phoResRef, targetr) ], position=(0.2, 0.8)).draw()
def plot_smeared_phoeres_with_fit(): phoEResPdf.fitTo(datasmeared, roo.PrintLevel(-1), roo.SumW2Error(False)) canvases.next('SmearedSampleWithFit') plot = phoERes.frame(roo.Range(-30, 30)) plot.SetTitle("Smeared MC with paremetrized fit") datasmeared.plotOn(plot) phoEResPdf.plotOn(plot) phoEResPdf.paramOn(plot) plot.Draw() Latex([ 'target s: %.3g %%' % targets, 'target r: %.3g %%' % targetr, ], position=(0.2, 0.75)).draw()
def plot_training_phoeres_with_shape_and_fit(): canvases.next('TrainingSampleWithShapeAndFit') plot = phoERes.frame(roo.Range(-7.5, 7.5)) plot.SetTitle( "MC overlayed with PDF shape (blue) and it's parametrized fit" "(dashed red)") data.plotOn(plot) phoEResPdf.shape.plotOn(plot) phoEResPdf.plotOn(plot, roo.LineColor(ROOT.kRed), roo.LineStyle(ROOT.kDashed)) plot.Draw() Latex([ 's_{shape}: %.3f %%' % phoEResPdf.shapemode, 's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()), 's_{fit} - s_{shape}: %.4f #pm %.4f' % (phoScale.getVal() - phoEResPdf.shapemode, phoScale.getError()), 'r_{shape}: %.3f %%' % phoEResPdf.shapewidth, 'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()), 'r_{fit}/r_{shape}: %.4f #pm %.4f' % (phoRes.getVal() / phoEResPdf.shapewidth, phoRes.getError() / phoEResPdf.shapewidth), ], position=(0.2, 0.75)).draw()
data.tree().GetV1(), minBinContent, maxBinContent) binning = bins.binning(ROOT.RooBinning()) ubinning = bins.uniformBinning(ROOT.RooUniformBinning()) ## Make sure that there are at least 1./epsilonPerBin curve points ## in each bin. The RooChi2Calculator fails if there is none perhaps ## due to a bug in RooCurve::average? Reason is not understood yet. epsilon = epsilonPerBin * ubinning.binWidth(0) / bins.length() ## Make frames log_plot = x.frame() log_plot_bins = x.frame() log_plot_medians = x.frame() bins.setSigmaLevel(3) lin_plot = x.frame(roofit.Range(-3, 3)) lin_plot_bins = x.frame(*bins.bounds()) lin_plot_medians = x.frame(*bins.bounds()) bins.setFraction(1) log_plot.SetTitle('Default RooFit') lin_plot.SetTitle(log_plot.GetTitle()) log_plot_bins.SetTitle("New - Bin Content > %d" % minBinContent) lin_plot_bins.SetTitle(log_plot_bins.GetTitle()) log_plot_medians.SetTitle( "New - Bin Content > %d and Data at Bin Medians" % minBinContent) lin_plot_medians.SetTitle(log_plot_medians.GetTitle()) ## Add the data and fit to the plots for plot in [log_plot, lin_plot]: data.plotOn(plot)
canvases.next('xt1_proj_t1') t.setRange(*t1range) t.SetTitle('log(m_{#mu#mu#gamma,E_{gen}^{#gamma}}^{2} - m_{#mu#mu}^{2})') plot = t.frame() xt1data.plotOn(plot) xt1pdf.plotOn(plot) plot.Draw() ## Plot fT2(t2|s,r) fitted to training data. canvases.next('t2pdf').SetGrid() t.setRange(*t2range) t.setVal(0) t.SetTitle('log(E_{reco}^{#gamma}/E_{gen}^{#gamma})') phoScale.setVal(t2pdf.s0val) phoRes.setVal(t2pdf.r0val) t2pdf.fitTo(t2data, roo.Range(ROOT.TMath.Log(0.5), ROOT.TMath.Log(1.5))) plot = t.frame(roo.Range(-0.3, 0.3)) t2data.plotOn(plot) t2pdf.plotOn(plot) plot.Draw() Latex([ 's_{shape}: %.3f %%' % t2pdf.s0val, 's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()), 's_{fit} - s_{shape}: %.4f #pm %.4f %%' % ( phoScale.getVal() - t2pdf.s0val, phoScale.getError() ), 'r_{shape}: %.3f %%' % t2pdf.r0val, 'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()), 'r_{fit}/r_{shape}: %.4f #pm %.4f' % ( phoRes.getVal() / t2pdf.r0val,
print 'CPU time:', sw.CpuTime(), 's, real time:', sw.RealTime(), 's' ## End of main() ##------------------------------------------------------------------------------ sw = ROOT.TStopwatch() sw.Start() init() get_data() phoEResPdf = ParametrizedKeysPdf('phoEResPdf', 'phoEResPdf', phoERes, phoScale, phoRes, data, ROOT.RooKeysPdf.NoMirror, 1.5) phoEResPdf.fitTo(data, roo.Range(-50, 50)) canvases.next('phoEResPdf').SetGrid() plot = phoERes.frame(roo.Range(-10, 10)) data.plotOn(plot) phoEResPdf.plotOn(plot) phoEResPdf.paramOn(plot) plot.Draw() t = w.factory('t[0,-1,1]') t.SetTitle('log(E_{reco}^{#gamma}/E_{gen}^{#gamma})') tfunc = w.factory('expr::tfunc("log(0.01 * phoERes + 1)", {phoERes})') tfunc.SetName('t') data.addColumn(tfunc) ## Build the model for log(Ereco/Egen) ft2(t2|r,s)
def process_monte_carlo(): ''' Get, fit and plot monte carlo. ''' global fitdata1 fitdata1 = fit_calibrator.get_smeared_data( sfit, rfit, 'fitdata1', 'fitdata1', True ) fitdata1.reduce(ROOT.RooArgSet(mmgMass, mmMass)) fitdata1.append(data['zj1']) fitdata1.SetName('fitdata1') data['fit1'] = fitdata1 if reduce_data == True: fitdata1 = fitdata1.reduce(roo.Range(reduced_entries, fitdata1.numEntries())) check_timer('3. get fit data (%d entries)' % fitdata1.numEntries()) nll = pm.createNLL(fitdata1, roo.Range('fit'), roo.NumCPU(8)) minuit = ROOT.RooMinuit(nll) minuit.setProfile() minuit.setVerbose() phoScale.setError(1) phoRes.setError(1) ## Initial HESSE status = minuit.hesse() fitres = minuit.save(name + '_fitres1_inithesse') w.Import(fitres, fitres.GetName()) check_timer('4. initial hesse (status: %d)' % status) ## Minimization minuit.setStrategy(2) status = minuit.migrad() fitres = minuit.save(name + '_fitres2_migrad') w.Import(fitres, fitres.GetName()) check_timer('5. migrad (status: %d)' % status) ## Parabolic errors status = minuit.hesse() fitres = minuit.save(name + '_fitres3_hesse') w.Import(fitres, fitres.GetName()) check_timer('6. hesse (status: %d)' % status) ## Minos errors status = minuit.minos() fitres = minuit.save(name + '_fitres4_minos') w.Import(fitres, fitres.GetName()) check_timer('7. minos (status: %d)' % status) #fres = pm.fitTo(fitdata1, roo.SumW2Error(True), #roo.Range('fit'), ## roo.Strategy(2), #roo.InitialHesse(True), #roo.Minos(), #roo.Verbose(True), #roo.NumCPU(8), roo.Save(), roo.Timer()) signal_model._phorhist.GetXaxis().SetRangeUser(75, 105) signal_model._phorhist.GetYaxis().SetRangeUser(0, 15) signal_model._phorhist.GetXaxis().SetTitle('%s (%s)' % (mmgMass.GetTitle(), mmgMass.getUnit())) signal_model._phorhist.GetYaxis().SetTitle('E^{#gamma} Resolution (%)') signal_model._phorhist.GetZaxis().SetTitle('Probability Density (1/GeV/%)') signal_model._phorhist.SetTitle(latex_title) signal_model._phorhist.GetXaxis().SetTitleOffset(1.5) signal_model._phorhist.GetYaxis().SetTitleOffset(1.5) signal_model._phorhist.GetZaxis().SetTitleOffset(1.5) signal_model._phorhist.SetStats(False) canvases.next(name + '_phorhist') signal_model._phorhist.Draw('surf1') global graph graph = signal_model.make_mctrue_graph() graph.GetXaxis().SetTitle('E^{#gamma} resolution (%)') graph.GetYaxis().SetTitle('m_{#mu^{+}#mu^{-}#gamma} effective #sigma (GeV)') graph.SetTitle(latex_title) canvases.next(name + '_mwidth_vs_phor').SetGrid() graph.Draw('ap') mmgMass.setBins(80) plot = mmgMass.frame(roo.Range('plot')) plot.SetTitle('Fall11 MC, ' + latex_title) fitdata1.plotOn(plot) pm.plotOn(plot, roo.Range('plot'), roo.NormRange('plot')) pm.plotOn(plot, roo.Range('plot'), roo.NormRange('plot'), roo.Components('*zj*'), roo.LineStyle(ROOT.kDashed)) canvases.next(name + '_fit').SetGrid() plot.Draw() ## Estimate the MC truth phos and phor: old_precision = set_default_integrator_precision(2e-9, 2e-9) calibrator0.s.setRange(-15, 15) calibrator0.r.setRange(0,25) calibrator0.phoEResPdf.fitTo(fitdata1, roo.Range(-50, 50), roo.Strategy(2)) set_default_integrator_precision(*old_precision) set_mc_truth(calibrator0.s, calibrator0.r) ## Store the result in the workspace: w.saveSnapshot('mc_fit', ROOT.RooArgSet(phoScale, phoRes, phoScaleTrue, phoResTrue)) ## Draw the results on the canvas: Latex([ 'E^{#gamma} Scale (%)', ' MC Truth: %.2f #pm %.2f' % (calibrator0.s.getVal(), calibrator0.s.getError()), ' MC Fit: %.2f #pm %.2f ^{+%.2f}_{%.2f}' % ( phoScale.getVal(), phoScale.getError(), phoScale.getErrorHi(), phoScale.getErrorLo() ), '', 'E^{#gamma} Resolution (%)', ' MC Truth: %.2f #pm %.2f' % (calibrator0.r.getVal(), calibrator0.r.getError()), ' MC Fit: %.2f #pm %.2f ^{+%.2f}_{%.2f}' % ( phoRes.getVal(), phoRes.getError(), phoRes.getErrorHi(), phoRes.getErrorLo() ), '', 'Signal Purity (%)', ' MC Truth: %.2f' % fsr_purity, ' MC Fit: %.2f #pm %.2f' % ( 100 * w.var('signal_f').getVal(), 100 * w.var('signal_f').getError() ) ], position=(0.2, 0.8) ).draw() check_timer('8. fast plots')
## pdf = ROOT.RooNDKeysPdf(pdfname, pdfname, ## ROOT.RooArgList(mmgMass), sdata, "a", 1.5) ## -> Stick to deprecated RooKeysPdf for now. ## mmgMass.setRange(40, 140) peak = w.factory('%s_mode[91.2, 60, 120]' % pdfname) width = w.factory('%s_effsigma[3, 0.1, 20]' % pdfname) pdf = ParametrizedKeysPdf(pdfname + '_pkeys', pdfname + '_pkyes', mmgMass, peak, width, sdata, ROOT.RooKeysPdf.NoMirror, 1.5) savrange = (mmgMass.getMin(), mmgMass.getMax()) normrange = (40, 130) fitrange = (60, 120) mmgMass.setRange(*fitrange) peak.setVal(pdf.shapemode) width.setVal(pdf.shapewidth) pdf.fitTo(sdata, roo.Range(*fitrange), roo.Strategy(2)) w.Import(pdf) ## peak.setConstant() ## width.setConstant() hist = pdf.createHistogram(pdfname + '_hist', mmgMass, roo.Binning(1000)) dhist = ROOT.RooDataHist(pdfname + '_dhist', pdfname + '_hist', ROOT.RooArgList(mmgMass), hist) w.Import(dhist) hpdf = w.factory( 'HistPdf::{name}({{mmgMass}}, {name}_dhist, 1)'.format(name=pdfname)) ## bdata = data.reduce(ROOT.RooArgSet(mmgMass)) ## bdata = bdata.binnedClone(data.GetName() + '_binned', data.GetTitle()) ## pdf = ROOT.RooHistPdf(pdfname, pdfname, ROOT.RooArgSet(mmgMass), bdata, 2) pdf.Print() # w.Import(pdf)
m1gOplusM2g.SetTitle('sqrt(mmgMass^2-mmMass^2)') isrData = dataset.get(tree=chains['z'], variable=mmMass, weight=weight, cuts=[ '!isFSR', 'mmgMass < 200', 'mmMass < 200', 'phoPt > 15', 'Entry$ < 500000' ]) mmgMassIsrData = dataset.get(variable=mmgMass) m1gOplusM2gIsrData = dataset.get(variable=m1gOplusM2g) isrData.merge(mmgMassIsrData, m1gOplusM2gIsrData) ## Fit the model to the data mmMassPdf.fitTo(isrData, roofit.Range(60, 120)) ## Plot the data and the fit mmPlot = mmMass.frame(roofit.Range(60, 120)) isrData.plotOn(mmPlot) mmMassPdf.plotOn(mmPlot) canvases.next('mmMass') mmPlot.Draw() ## Model for the reconstructed mmg mass of the ISR through transformation # mmgMassIsrPdf = ROOT.RooFFTConvPdf('mmgMassIsrPdf', 'mmgMassIsrPdf', mmMassFunc, # mmMass, zmmGenShape, mmMassRes) mmgMassPdf = w.factory('Voigtian::mmgMassPdf(mmMassFunc, mmMean, GZ, mmRes)') ## Plot the mmg mass data and model overlaid without fitting (!) mmgPlot = mmgMass.frame(roofit.Range(60, 200))
phoRes, data, rho=1.5) ## Build the model for fT(t|s,r) = fT1(t1) * fT2(t2|s,r) t.setRange(5, 10) t.setBins(1000, "cache") tpdf = ROOT.RooFFTConvPdf('tpdf', 'tpdf', t, t1pdf, t2pdf) tpdf.setBufferFraction(0.1) ##------------------------------------------------------------------------------ ## Plot fT1(t1) with training data. canvases.next('t1pdf').SetGrid() t.setRange(5, 10) t.SetTitle('log(m_{#mu#mu#gamma,E_{gen}^{#gamma}}^{2} - m_{#mu#mu}^{2})') plot = t.frame(roo.Range(6, 9)) t1data.plotOn(plot) t1pdf.shape.plotOn(plot) t1pdf.plotOn(plot, roo.LineColor(ROOT.kRed), roo.LineStyle(ROOT.kDashed)) plot.Draw() Latex([ 's_{shape}: %.3f' % t1pdf.shapemode, 's_{fit}: %.3f #pm %.3f' % (t1mode.getVal(), t1mode.getError()), 's_{fit} - s_{shape}: %.4f #pm %.4f' % (t1mode.getVal() - t1pdf.shapemode, t1mode.getError()), 'r_{shape}: %.3f' % t1pdf.shapewidth, 'r_{fit}: %.3f #pm %.3f' % (t1width.getVal(), t1width.getError()), 'r_{fit} - r_{shape}: %.4f #pm %.4f' % (t1width.getVal() - t1pdf.shapewidth, t1width.getError()), 'r_{fit}/r_{shape}: %.4f #pm %.4f' % (t1width.getVal() / t1pdf.shapewidth,
def __init__(self, name, title, mass, phos, phor, data, workspace, phostarget, phortargets, rho=1.5, mirror=ROOT.RooKeysPdf.NoMirror, mrangetrain=(40, 140), mrangenorm=(50, 130), mrangefit=(60, 120)): '''PhosphorModel4(str name, str title, RooRealVar mass, RooRealVar phos, RooRealVar phor, RooDataSet data, float phostarget, [float] phortargets, float rho=1.5, int mirror=ROOT.RooKeysPdf.NoMirror) name - PDF name title - PDF title mass - mumugamma invariant mass (GeV), observable phos - photon energy scale (%), parameter phor - photon energy resolution (%), paramter data - (mmMass, mmgMass, phoERes = 100*(phoEreco/phoEgen - 1), dataset on which the shapes of reference PDFs are trained. phostarget - target reference value of the photon energy scale at which the model is being trained phortargetss - a list of target photon energy resolution values for the moment morphing rho - passed to trained RooKeysPdfs mirror - passed to trained RooKeysPdfs ''' ## Attach args. self._name = name self._title = title self._mass = mass self._phos = phos self._phor = phor self._data = data self._phostarget = phostarget self._phortargets = phortargets[:] self._rho = rho self._mirror = mirror ## Attach other attributes. self._massrange = (mass.getMin(), mass.getMax()) self._sdata_list = [] self._phostrue_list = [] self._phortrue_list = [] self._dm_dphos_list = [] self._msubs_list = [] self._keys_pdfs = [] self._keys_modes = [] self._keys_effsigmas = [] self._keys_fitresults = [] self._pdfs = [] self._custs = [] # self._pdfrefs = [] self._mrefs = [] self._phormorphs = [] self._phorhists = [] self._workspace = workspace ## self._workspace = ROOT.RooWorkspace(name + '_workspace', ## title + ' workspace') w = self._workspace self.w = w ## Import important args in workspace. # w.Import(ROOT.RooArgSet(mass, phos, phor)) w.Import(mass) w.Import(phos) w.Import(phor) w.Import(self._data) w.factory('ConstVar::{name}_phostarget({value})'.format( name=name, value=self._phostarget)) ## Define the morphing parameter. This an identity with the ## photon resolution for now. mpar = self._mpar = w.factory( 'expr::{name}_mpar("{phor}", {{{phor}}})'.format( ## 'expr::{name}_mpar("2 + sqrt(0.5^2 + 0.05 * {phor}^2)", {{{phor}}})'.format( ## 'expr::{name}_mpar("2.325+sqrt(0.4571 + (0.1608*{phor})^2)", {{{phor}}})'.format( name=name, phor=phor.GetName())) ## Define the formula for dlog(m)/dphos. self._dm_dphos_func = w.factory(''' expr::dm_dphos_func("0.5 * (1 - mmMass^2 / mmgMass^2) * mmgMass", {mmMass, mmgMass}) ''') ## Get the calibrator. self._calibrator = MonteCarloCalibrator(self._data) ## Loop over target reference photon energy resolutions in phortargets. for index, phortarget in enumerate(self._phortargets): ## Store the target photon resolution value. w.factory('ConstVar::{name}_phortarget_{index}({value})'.format( name=name, index=index, value=phortarget)) ## Get the corresponding smeared RooDataSet sdata named ## {name}_sdata_{index} and attach it to self._sdata sdata = self._calibrator.get_smeared_data( self._phostarget, phortarget, name + '_sdata_%d' % index, title + ' sdata %d' % index, ## Get the true scale and resolution with errors. (This can be ## added to the calibrator and snapshots of ## self._calibrator.s and self._calibrator.r stored as ## {name}_sdata_{index}_sr in self._calibrator.w) dofit=True) self._sdata_list.append(sdata) w.Import(sdata) phostrue = ROOT.RooRealVar(self._calibrator.s, name + '_phostrue_%d' % index) phortrue = ROOT.RooRealVar(self._calibrator.r, name + '_phortrue_%d' % index) phostrue.setConstant(True) phortrue.setConstant(True) self._phostrue_list.append(phostrue) self._phortrue_list.append(phortrue) w.Import(phostrue) w.Import(phortrue) ## Calculate the dlogm/dphos {name}_dm_dphos_{index} ## for the smeared dataset. sdata.addColumn(self._dm_dphos_func) dm_dphos = w.factory(''' {name}_dm_dphos_{index}[{mean}, 0, 1] '''.format(name=name, index=index, mean=sdata.mean(sdata.get()['dm_dphos_func']))) dm_dphos.setConstant(True) self._dm_dphos_list.append(dm_dphos) ## Define the mass scaling {name}_msubs{i} introducing ## the dependence on phos. This needs self._calibrator.s and ## dm_dphos. ## msubs = w.factory( ## ''' ## LinearVar::{msubs}( ## {mass}, 1, ## expr::{offset}( ## "- 0.01 * {dm_dphos} * ({phos} - {phostrue})", ## {{ {dm_dphos}, {phos}, {phostrue} }} ## ) ## ) ## '''.format(msubs = name + '_msubs_%d' % index, ## offset = name + '_msubs_offset_%d' % index, ## mass = self._mass.GetName(), ## dm_dphos = dm_dphos.GetName(), ## phos = self._phos.GetName(), ## phostrue = phostrue.GetName()) ## ) ## LinearVar cannot be persisted. msubs = w.factory(''' expr::{msubs}( "{mass} - 0.01 * {dm_dphos} * ({phos} - {phostrue})", {{ {mass}, {dm_dphos}, {phos}, {phostrue} }} ) '''.format(msubs=name + '_msubs_%d' % index, mass=self._mass.GetName(), dm_dphos=dm_dphos.GetName(), phos=self._phos.GetName(), phostrue=phostrue.GetName())) self._msubs_list.append(msubs) ## Build the corresponding parametrized KEYS PDF {name}_kyes_{index} ## with {name}_keys_mode_{index} and ## {name}_keys_effsigma_{index}. keys_mode = w.factory( '{name}_keys_mode_{index}[91.2, 60, 120]'.format(name=name, index=index)) keys_effsigma = w.factory( '{name}_keys_effsigma_{index}[3, 0.1, 60]'.format(name=name, index=index)) mass.setRange(*mrangetrain) keys_pdf = ParametrizedKeysPdf(name + '_keys_pdf_%d' % index, name + '_keys_pdf_%d' % index, mass, keys_mode, keys_effsigma, sdata, rho=self._rho) self._keys_modes.append(keys_mode) self._keys_effsigmas.append(keys_effsigma) self._keys_pdfs.append(keys_pdf) ## Fit the KEYS PDF to the training data and save the result ## {name}_keys_fitresult_{index} and parameter snapshots ## {name}_keys_mctrue_{index}. mass.setRange(*mrangenorm) keys_fitresult = keys_pdf.fitTo(sdata, roo.Range(*mrangefit), roo.Strategy(2), roo.NumCPU(8), roo.Save(True)) self._keys_fitresults.append(keys_fitresult) w.Import(keys_fitresult, name + '_keys_fitresult_%d' % index) w.saveSnapshot( name + '_mctrue_%d' % index, ','.join([ phostrue.GetName(), phortrue.GetName(), keys_mode.GetName(), keys_effsigma.GetName() ])) ## Sample the fitted KEYS PDF to a histogram {name}_hist_{index}. mass.setRange(*mrangetrain) hist = keys_pdf.createHistogram(name + '_hist_%d' % index, mass, roo.Binning('cache')) ## Build a RooDataHist {name}_dhist{index} of the sampled histogram. dhist = ROOT.RooDataHist(name + '_dhist_%d' % index, name + '_dhist_%d' % index, ROOT.RooArgList(mass), hist) w.Import(dhist) ## Build a RooHistPdf {name}_pdf_{index} using the dhist and msubs. ## pdf = w.factory( pdf = w.factory('HistPdf::{name}({{{mass}}}, {dhist}, 1)'.format( name=name + '_pdf_%d' % index, msubs=msubs.GetName(), mass=mass.GetName(), dhist=dhist.GetName())) self._pdfs.append(pdf) mass.setRange(*self._massrange) ## Calculate morphing parameter reference values float mref[index]. phorval = phor.getVal() phor.setVal(phortrue.getVal()) self._mrefs.append(mpar.getVal()) phor.setVal(phorval) ## End of loop over target phortargets ## Make the reference and cache binnings in phor self._check_phor_ranges() partitions = self._partition_binning(self._phor, 'cache', 'reference') ## Loop over phor reference bins and define pairwise RooMomentMorphs. for ilo in range(len(self._mrefs) - 1): ihi = ilo + 1 mlo = self._mrefs[ilo] mhi = self._mrefs[ihi] pdflo = self._pdfs[ilo] pdfhi = self._pdfs[ihi] phormorph = w.factory(''' MomentMorph::{name}_phormorph_{ilo}to{ihi}( {mpar}, {{{mass}}}, {{{pdfs}}}, {{{mrefs}}} ) '''.format(name=name, ilo=ilo, ihi=ihi, mpar=mpar.GetName(), mass=mass.GetName(), pdfs='%s, %s' % (pdflo.GetName(), pdfhi.GetName()), mrefs='%f, %f' % (mlo, mhi))) self._phormorphs.append(phormorph) ## Sample the morph in a 2D histogram in mass and phor. phorhist = phormorph.createHistogram( name + '_phorhist_%dto%d' % (ilo, ihi), mass, roo.Binning('cache'), roo.YVar(phor, roo.Binning(partitions[ilo]))) self._phorhists.append(phorhist) ## End of loop over phor reference bins. self._stitch_phorhists() self._phor_dhist = phor_dhist = ROOT.RooDataHist( name + '_phor_dhist', name + '_phor_dhist', ROOT.RooArgList(mass, phor), self._phorhist) average_index = (len(self._msubs_list) + 1) / 2 msubs = self._msubs_list[average_index] ## self._model = model = ROOT.RooHistPdf( ## name + '_phor_histpdf', name + '_phor_histpdf', ## ROOT.RooArgList(msubs, phor), ROOT.RooArgList(mass, phor), phor_dhist, 2 ## ) ## self._model = model = ROOT.RooPhosphorPdf( ## name + '_phor_histpdf', name + '_phor_histpdf', mass, msubs, phor, phor_dhist, 2 ## ) ## ## Quick hack to make things work. ## self._customizer = customizer = ROOT.RooCustomizer(model, 'msub') ## customizer.replaceArg(mass, msubs) ## model = customizer.build() # ROOT.RooHistPdf.__init__(self, model) ROOT.RooPhosphorPdf.__init__(self, name, title, mass, msubs, phos, phor, phor_dhist, 2) self.SetName(name) self.SetTitle(title)