def rooFit208(): print ">>> setup component pdfs..." t = RooRealVar("t", "t", -10, 30) ml = RooRealVar("ml", "mean landau", 5., -20, 20) sl = RooRealVar("sl", "sigma landau", 1, 0.1, 10) landau = RooLandau("lx", "lx", t, ml, sl) mg = RooRealVar("mg", "mg", 0) sg = RooRealVar("sg", "sg", 2, 0.1, 10) gauss = RooGaussian("gauss", "gauss", t, mg, sg) print ">>> construct convolution pdf..." # Set #bins to be used for FFT sampling to 10000 t.setBins(10000, "cache") # Construct landau (x) gauss convolution = RooFFTConvPdf("lxg", "landau (X) gauss", t, landau, gauss) print ">>> sample, fit and plot convoluted pdf..." data = convolution.generate(RooArgSet(t), 10000) # RooDataSet convolution.fitTo(data) print "\n>>> plot everything..." frame1 = t.frame(Title("landau #otimes gauss convolution")) # RooPlot data.plotOn(frame1, Binning(50), Name("data")) convolution.plotOn(frame1, Name("lxg")) gauss.plotOn(frame1, LineStyle(kDashed), LineColor(kRed), Name("gauss")) landau.plotOn(frame1, LineStyle(kDashed), LineColor(kGreen), Name("landau")) print "\n>>> draw pfds and fits on canvas..." canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600) legend = TLegend(0.6, 0.8, 0.8, 0.6) legend.SetTextSize(0.032) legend.SetBorderSize(0) legend.SetFillStyle(0) gPad.SetLeftMargin(0.15) gPad.SetRightMargin(0.02) frame1.GetYaxis().SetLabelOffset(0.008) frame1.GetYaxis().SetTitleOffset(1.5) frame1.GetYaxis().SetTitleSize(0.045) frame1.GetXaxis().SetTitleSize(0.045) frame1.Draw() legend.AddEntry("data", "data", 'LEP') legend.AddEntry("lxg", "convolution", 'L') legend.AddEntry("landau", "landau", 'L') legend.AddEntry("gauss", "gauss", 'L') legend.Draw() canvas.SaveAs("rooFit208.png")
def rooFit503(): print ">>> open and workspace file, list its contents and get workspace object..." file = TFile("rooFit502_workspace.root") file.ls() workspace = file.Get("workspace") # RooWorkspace print "\n>>> retrieve pdf, data from workspace..." x = workspace.var("x") # RooRealVar model = workspace.pdf("model") # RooAbsPdf data = workspace.data("modelData") print ">>> print model tree:..." model.Print("t") print "\n>>> fit model to data..." model.fitTo(data) frame1 = x.frame(Title("Model and data read from workspace")) # RooPlot print ">>> plot everything..." data.plotOn(frame1, Name("data"), Binning(40)) model.plotOn(frame1, Name("model")) model.plotOn(frame1, Components("bkg"), LineStyle(kDashed), Name("bkg")) model.plotOn(frame1, Components("bkg,sig2"), LineStyle(kDotted), Name("bkgsig2")) print "\n>>> draw on canvas..." canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600) legend = TLegend(0.2, 0.8, 0.4, 0.6) legend.SetTextSize(0.032) legend.SetBorderSize(0) legend.SetFillStyle(0) gPad.SetLeftMargin(0.15) gPad.SetRightMargin(0.02) frame1.GetYaxis().SetLabelOffset(0.008) frame1.GetYaxis().SetTitleOffset(1.5) frame1.GetYaxis().SetTitleSize(0.045) frame1.GetXaxis().SetTitleSize(0.045) frame1.Draw() legend.AddEntry("data", "data", 'LEP') legend.AddEntry("model", "model", 'L') legend.AddEntry("bkg", "background only", 'L') legend.AddEntry("bkgsig2", "background + signal 2", 'L') legend.Draw() canvas.SaveAs("rooFit503.png")
def rooFit203(): print ">>> setup model..." x = RooRealVar("x", "x", -10, 10) mean = RooRealVar("mean", "mean of gaussian", 0, -10, 10) gauss = RooGaussian("gauss", "gaussian PDF", x, mean, RooConst(1)) # Construct px = 1 (flat in x) px = RooPolynomial("px", "px", x) # Construct model = f*gx + (1-f)px f = RooRealVar("f", "f", 0., 1.) model = RooAddPdf("model", "model", RooArgList(gauss, px), RooArgList(f)) data = model.generate(RooArgSet(x), 10000) # RooDataSet print ">>> fit to full data range..." result_full = model.fitTo(data, Save(kTRUE)) # RooFitResult print "\n>>> fit \"signal\" range..." # Define "signal" range in x as [-3,3] x.setRange("signal", -3, 3) result_sig = model.fitTo(data, Save(kTRUE), Range("signal")) # RooFitResult print "\n>>> plot and print results..." # Make plot frame in x and add data and fitted model frame1 = x.frame(Title("Fitting a sub range")) # RooPlot data.plotOn(frame1, Name("data")) model.plotOn(frame1, Range("Full"), LineColor(kBlue), Name("model")) # Add shape in full ranged dashed model.plotOn(frame1, LineStyle(kDashed), LineColor(kRed), Name("model2")) # By default only fitted range is shown print "\n>>> result of fit on all data:" result_full.Print() print "\n>>> result of fit in in signal region (note increased error on signal fraction):" result_sig.Print() print ">>> draw on canvas..." canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600) legend = TLegend(0.2, 0.85, 0.4, 0.65) legend.SetTextSize(0.032) legend.SetBorderSize(0) legend.SetFillStyle(0) gPad.SetLeftMargin(0.14) gPad.SetRightMargin(0.02) frame1.GetYaxis().SetLabelOffset(0.008) frame1.GetYaxis().SetTitleOffset(1.4) frame1.GetYaxis().SetTitleSize(0.045) frame1.GetXaxis().SetTitleSize(0.045) frame1.Draw() legend.AddEntry("data", "data", 'LEP') legend.AddEntry("model", "fit (full range)", 'L') legend.AddEntry("model2", "fit (signal range)", 'L') legend.Draw() canvas.SaveAs("rooFit203.png")
def rooFit510(): print ">>> create and fill workspace..." workspace = RooWorkspace("workspace") # RooWorkspace fillWorkspace(workspace) print ">>>\n>>> retrieve model from workspace..." # Exploit convention encoded in named set "parameters" and "observables" # to use workspace contents w/o need for introspected model = workspace.pdf("model") # RooAbsPdf print ">>> generate and fit data in given observables" data = model.generate(workspace.set("observables"), 1000) # RooDataSet model.fitTo(data) print ">>> plot model and data of first observables..." frame1 = workspace.set("observables").first().frame() # RooPlot data.plotOn(frame1, Name("data"), Binning(50)) model.plotOn(frame1, Name("model")) print ">>> overlay plots with reference parameters as stored in snapshots..." workspace.loadSnapshot("reference_fit") model.plotOn(frame1, LineColor(kRed), Name("model_ref")) workspace.loadSnapshot("reference_fit_bkgonly") model.plotOn(frame1, LineColor(kRed), LineStyle(kDashed), Name("bkg_ref")) print "\n>>> draw on canvas..." canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600) legend = TLegend(0.2, 0.8, 0.4, 0.6) legend.SetTextSize(0.032) legend.SetBorderSize(0) legend.SetFillStyle(0) gPad.SetLeftMargin(0.15) gPad.SetRightMargin(0.02) frame1.GetYaxis().SetLabelOffset(0.008) frame1.GetYaxis().SetTitleOffset(1.5) frame1.GetYaxis().SetTitleSize(0.045) frame1.GetXaxis().SetTitleSize(0.045) frame1.Draw() legend.AddEntry("data", "data", 'LEP') legend.AddEntry("model", "model", 'L') legend.AddEntry("model_ref", "model fit", 'L') legend.AddEntry("bkg_ref", "background only fit", 'L') legend.Draw() canvas.SaveAs("rooFit510.png") # Print workspace contents workspace.Print() # Workspace will remain in memory after macro finishes gDirectory.Add(workspace)
def rooFit107(): print ">>> setup model..." x = RooRealVar("x","x",-3,3) mean = RooRealVar("mean","mean of gaussian",1,-10,10) sigma = RooRealVar("sigma","width of gaussian",1,0.1,10) gauss = RooGaussian("gauss","gauss",x,mean,sigma) data = gauss.generate(RooArgSet(x),1000) # RooDataSet gauss.fitTo(data) print ">>> plot pdf and data..." frame1 = x.frame(Name("frame1"),Title("Red Curve / SumW2 Histo errors"),Bins(20)) # RooPlot frame2 = x.frame(Name("frame2"),Title("Dashed Curve / No XError bars"),Bins(20)) # RooPlot frame3 = x.frame(Name("frame3"),Title("Filled Curve / Blue Histo"),Bins(20)) # RooPlot frame4 = x.frame(Name("frame4"),Title("Partial Range / Filled Bar chart"),Bins(20)) # RooPlot print ">>> data plotting styles..." data.plotOn(frame1,DataError(RooAbsData.SumW2)) data.plotOn(frame2,XErrorSize(0)) # Remove horizontal error bars data.plotOn(frame3,MarkerColor(kBlue),LineColor(kBlue)) # Blue markers and error bors data.plotOn(frame4,DrawOption("B"),DataError(RooAbsData.None), XErrorSize(0),FillColor(kGray)) # Filled bar chart print ">>> data plotting styles..." gauss.plotOn(frame1,LineColor(kRed)) gauss.plotOn(frame2,LineStyle(kDashed)) # Change line style to dashed gauss.plotOn(frame3,DrawOption("F"),FillColor(kOrange),MoveToBack()) # Filled shapes in green color gauss.plotOn(frame4,Range(-8,3),LineColor(kMagenta)) print ">>> draw pfds and fits on canvas..." canvas = TCanvas("canvas","canvas",100,100,1000,1200) canvas.Divide(2,2) for i, frame in enumerate([frame1,frame2,frame3,frame4],1): canvas.cd(i) gPad.SetLeftMargin(0.15); gPad.SetRightMargin(0.05) frame.GetYaxis().SetTitleOffset(1.6) frame.GetYaxis().SetLabelOffset(0.010) frame.GetYaxis().SetTitleSize(0.045) frame.GetYaxis().SetLabelSize(0.042) frame.GetXaxis().SetTitleSize(0.045) frame.GetXaxis().SetLabelSize(0.042) frame.Draw() canvas.SaveAs("rooFit107.png")
def rooFit204(): print ">>> setup model signal components: gaussians..." x = RooRealVar("x","x",0,10) mean = RooRealVar("mean","mean of gaussian",5) sigma1 = RooRealVar("sigma1","width of gaussians",0.5) sigma2 = RooRealVar("sigma2","width of gaussians",1) sig1 = RooGaussian("sig1","Signal component 1",x,mean,sigma1) sig2 = RooGaussian("sig2","Signal component 2",x,mean,sigma2) sig1frac = RooRealVar("sig1frac","fraction of component 1 in signal",0.8,0.,1.) sig = RooAddPdf("sig","Signal",RooArgList(sig1,sig2),RooArgList(sig1frac)) print ">>> setup model background components: Chebychev polynomial..." a0 = RooRealVar("a0","a0",0.5,0.,1.) a1 = RooRealVar("a1","a1",-0.2,0.,1.) bkg = RooChebychev("bkg","Background",x,RooArgList(a0,a1)) print ">>> construct extended components with specified range..." # Define signal range in which events counts are to be defined x.setRange("signalRange",5,6) # Associated nsig/nbkg as expected number of events with sig/bkg _in_the_range_ "signalRange" nsig = RooRealVar("nsig","number of signal events in signalRange", 500,0.,10000) nbkg = RooRealVar("nbkg","number of background events in signalRange",500,0,10000) esig = RooExtendPdf("esig","extended signal pdf", sig,nsig,"signalRange") ebkg = RooExtendPdf("ebkg","extended background pdf",bkg,nbkg,"signalRange") print ">>> sum extended components..." # Construct sum of two extended p.d.f. (no coefficients required) model = RooAddPdf("model","(g1+g2)+a",RooArgList(ebkg,esig)) print ">>> sample data, fit model..." data = model.generate(RooArgSet(x),1000) # RooDataSet result = model.fitTo(data,Extended(kTRUE),Save()) # RooFitResult print "\n>>> fit result:" result.Print() print "\n>>> plot everything..." frame1 = x.frame(Title("Fitting a sub range")) # RooPlot data.plotOn(frame1,Binning(50),Name("data")) model.plotOn(frame1,LineColor(kBlue),Name("model")) argset1 = RooArgSet(bkg) model.plotOn(frame1,Components(argset1),LineStyle(kDashed),LineColor(kBlue),Name("bkg"),Normalization(1.0,RooAbsReal.RelativeExpected)) #model.plotOn(frame1,Components(argset1),LineStyle(kDashed),LineColor(kRed),Name("bkg2")) print ">>> draw on canvas..." canvas = TCanvas("canvas","canvas",100,100,800,600) legend = TLegend(0.2,0.85,0.4,0.7) legend.SetTextSize(0.032) legend.SetBorderSize(0) legend.SetFillStyle(0) gPad.SetLeftMargin(0.14); gPad.SetRightMargin(0.02) frame1.GetYaxis().SetLabelOffset(0.008) frame1.GetYaxis().SetTitleOffset(1.4) frame1.GetYaxis().SetTitleSize(0.045) frame1.GetXaxis().SetTitleSize(0.045) frame1.Draw() legend.AddEntry("data", "data", 'LEP') legend.AddEntry("model", "fit", 'L') legend.AddEntry("bkg", "background only",'L') #legend.AddEntry("bkg2", "background only (no extended norm)",'L') legend.Draw() canvas.SaveAs("rooFit204.png")
def rooFit202(): print ">>> setup model component: gaussian signals and Chebychev polynomial background..." x = RooRealVar("x", "x", 0, 10) mean = RooRealVar("mean", "mean of gaussian", 5) sigma1 = RooRealVar("sigma1", "width of gaussian", 0.5) sigma2 = RooRealVar("sigma2", "width of gaussian", 1.0) sig1 = RooGaussian("sig1", "Signal component 1", x, mean, sigma1) sig2 = RooGaussian("sig2", "Signal component 2", x, mean, sigma2) a0 = RooRealVar("a0", "a0", 0.5, 0., 1.) a1 = RooRealVar("a1", "a1", -0.2, 0., 1.) bkg = RooChebychev("bkg", "Background", x, RooArgList(a0, a1)) # Sum the signal components into a composite signal p.d.f. sig1frac = RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0., 1.) sig = RooAddPdf("sig", "Signal", RooArgList(sig1, sig2), RooArgList(sig1frac)) print ">>>\n>>> METHOD 1" print ">>> construct extended composite model..." # Sum the composite signal and background into an extended pdf nsig*sig+nbkg*bkg nsig = RooRealVar("nsig", "number of signal events", 500, 0., 10000) nbkg = RooRealVar("nbkg", "number of background events", 500, 0, 10000) model = RooAddPdf("model", "(g1+g2)+a", RooArgList(bkg, sig), RooArgList(nbkg, nsig)) print ">>> sample, fit and plot extended model...\n" # Generate a data sample of expected number events in x from model # nsig + nbkg = model.expectedEvents() # NOTE: since the model predicts a specific number events, one can # omit the requested number of events to be generated # Introduce Poisson fluctuation with Extended(kTRUE) data = model.generate(RooArgSet(x), Extended(kTRUE)) # RooDataSet # Fit model to data, extended ML term automatically included # NOTE: Composite extended pdfs can only be successfully fit if the extended likelihood # term -log(Poisson(Nobs,Nexp)) is included in the minimization because they have # one extra degree of freedom in their parameterization that is constrained by # this extended term. If a pdf is capable of calculating an extended term (i.e. # any extended RooAddPdf), the extended term is AUTOMATICALLY included in the # likelihood calculation. Override this behaviour with Extended(): # Extended(kTRUE) ADD extended likelihood term # Extended(kFALSE) DO NOT ADD extended likelihood #model.fitTo(data,Extended(kTRUE)) model.fitTo(data) print "\n>>> plot data, model and model components..." # Plot data and PDF overlaid, use expected number of events for pdf projection # normalization, rather than observed number of events, data.numEntries() frame1 = x.frame(Title("extended ML fit example")) # RooPlot data.plotOn(frame1, Binning(30), Name("data")) model.plotOn(frame1, Normalization(1.0, RooAbsReal.RelativeExpected), Name("model")) # Overlay the background components of model # NOTE: By default, the pdf is normalized to event count of the last dataset added # to the plot frame. Use "RelativeExpected" to normalize to the expected # event count of the pdf instead argset1 = RooArgSet(bkg) argset2 = RooArgSet(sig1) argset3 = RooArgSet(sig2) argset4 = RooArgSet(bkg, sig2) model.plotOn(frame1, Components(argset1), LineStyle(kDashed), LineColor(kBlue), Normalization(1.0, RooAbsReal.RelativeExpected), Name("bkg")) #model.plotOn(frame1,Components(argset1),LineStyle(kDashed),LineColor(kBlue), Name("bkg2")) model.plotOn(frame1, Components(argset2), LineStyle(kDotted), LineColor(kMagenta), Normalization(1.0, RooAbsReal.RelativeExpected), Name("sig1")) model.plotOn(frame1, Components(argset3), LineStyle(kDotted), LineColor(kPink), Normalization(1.0, RooAbsReal.RelativeExpected), Name("sig2")) model.plotOn(frame1, Components(argset4), LineStyle(kDashed), LineColor(kAzure - 4), Normalization(1.0, RooAbsReal.RelativeExpected), Name("bkgsig2")) print "\n>>> structure of composite pdf:" model.Print("t") # "tree" mode print "\n>>> parameters:" params = model.getVariables() # RooArgSet params.Print("v") params.Print() print "\n>>> params.find(\"...\").getVal():" print ">>> sigma1 = %.2f" % params.find("sigma1").getVal() print ">>> sigma2 = %.2f" % params.find("sigma2").getVal() print ">>> nsig = %6.2f, sig1frac = %5.2f" % ( params.find("nsig").getVal(), params.find("sig1frac").getVal()) print ">>> nbkg = %6.2f" % params.find("nbkg").getVal() print ">>>\n>>> components:" comps = model.getComponents() # RooArgSet sig = comps.find("sig") # RooAbsArg sigVars = sig.getVariables() # RooArgSet sigVars.Print() print ">>>\n>>> METHOD 2" print ">>> construct extended components first..." # Associated nsig/nbkg as expected number of events with sig/bkg nsig = RooRealVar("nsig", "number of signal events", 500, 0., 10000) nbkg = RooRealVar("nbkg", "number of background events", 500, 0, 10000) esig = RooExtendPdf("esig", "extended signal pdf", sig, nsig) ebkg = RooExtendPdf("ebkg", "extended background pdf", bkg, nbkg) print ">>> sum extended components without coefficients..." # Construct sum of two extended p.d.f. (no coefficients required) model2 = RooAddPdf("model2", "(g1+g2)+a", RooArgList(ebkg, esig)) # METHOD 2 is functionally completely equivalent to METHOD 1. # Its advantage is that the yield parameter is associated to the shape pdf # directly, while in METHOD 1 the association is made after constructing # a RooAddPdf. Also, class RooExtendPdf offers extra functionality to # interpret event counts in a different range. print ">>> plot model..." model2.plotOn(frame1, LineStyle(kDashed), LineColor(kRed), Normalization(1.0, RooAbsReal.RelativeExpected), Name("model2")) print ">>> draw on canvas..." canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600) legend = TLegend(0.2, 0.85, 0.4, 0.65) legend.SetTextSize(0.032) legend.SetBorderSize(0) legend.SetFillStyle(0) gPad.SetLeftMargin(0.14) gPad.SetRightMargin(0.02) frame1.GetYaxis().SetLabelOffset(0.008) frame1.GetYaxis().SetTitleOffset(1.4) frame1.GetYaxis().SetTitleSize(0.045) frame1.GetXaxis().SetTitleSize(0.045) frame1.Draw() legend.AddEntry("data", "data", 'LEP') legend.AddEntry("model", "composite model", 'L') legend.AddEntry("model2", "composite model (method 2)", 'L') legend.AddEntry("bkg", "background only", 'L') #legend.AddEntry("bkg2", "background only (no extended norm)", 'L') legend.AddEntry("sig1", "signal 1", 'L') legend.AddEntry("sig2", "signal 2", 'L') legend.AddEntry("bkgsig2", "background + signal 2", 'L') legend.Draw() canvas.SaveAs("rooFit202.png")
mean.setConstant(False) gamma.setConstant(False) sigma_1.setConstant(False) sigma_2.setConstant(False) rPhifit = tot.fitTo(splotData,Range(phimin,phimax),RooFit.NumCPU(args.ncpu),RooFit.Verbose(False)) nfits = nfits + 1 c = TCanvas("canvas","canvas",1200,900) phiFrame = masskk.frame(Range(phimin,phimax),Normalization((nSig.getValV() + nBkg.getValV())), Title("#phi Mass")) splotData.plotOn(phiFrame) ratio = 1.0/float(nfits) tot.plotOn(phiFrame,Normalization(ratio)) bFrac = (nBkg.getValV())/(nSig.getValV() + nBkg.getValV()) bkg.plotOn(phiFrame,LineColor(kRed),Normalization(bFrac),LineStyle(kDashed)) signal.plotOn(phiFrame,LineColor(kGreen),Normalization(1.0-bFrac)) a0.setConstant(True) a1.setConstant(True) a2.setConstant(True) a3.setConstant(True) a4.setConstant(True) nBkg.setConstant(True) tot.paramOn(phiFrame,RooFit.Layout(0.57,0.99,0.65)) phiFrame.Draw() sidesigma = np.sqrt((rFrac.getValV())*sigma_1.getValV()**2 + (1.0 - (rFrac.getValV()))*sigma_2.getValV()**2)
#Plotting fits canvs = [] fitPads = [] pullPads = [] pulls = [] for i in range(n_regions) : region = regions[i] canvs.append(TCanvas(region+"_canv", region+" D0 Mass Region Fit")) frame = deltam.frame() datahist.plotOn(frame, Cut("regionIndex==regionIndex::"+region)) regionIndexSet = RooArgSet(regionIndex) total_pdf.plotOn(frame, Slice(regionIndex, region), ProjWData(regionIndexSet, datahist)) pulls.append(frame.pullHist()) total_pdf.plotOn(frame, Slice(regionIndex, region), ProjWData(regionIndexSet, datahist), Components("bg_pdf"), LineColor(kGreen+2), LineStyle(2)) total_pdf.plotOn(frame, Slice(regionIndex, region), ProjWData(regionIndexSet, datahist), Components("signal_pdf_"+region), LineColor(kRed+2), LineStyle(3)) #Creating pad to plot fit to data fitPads.append(TPad("fitPad_{}".format(i), "fitPad_{}".format(i), 0., 0.3, 1., 1.)) fitPads[i].SetMargin(0.08, 0.05, 0.05, 0.1) fitPads[i].Draw() fitPads[i].cd() frame.Draw() #Creating pad to plot pull ( (actual - fit) / uncertainty ) canvs[i].cd() pullPads.append(TPad("pullPad_{}".format(i), "pullPad_{}".format(i), 0., 0., 1., 0.3)) pullPads[i].SetMargin(0.08, 0.05, 0.1, 0.05) pullPads[i].Draw()
masslist = RooArgList(mass) dh = RooDataHist("dh", "dh", masslist, hist) numEvts = dh.sum(False) print numEvts # In[10]: tot.fitTo(dh) # In[11]: massFrame = mass.frame() massFrame.SetTitle("Phi signal") dh.plotOn(massFrame) tot.plotOn(massFrame) gauss.plotOn(massFrame, LineColor(kGreen), LineStyle(kDashed), Normalization((sFrac.getValV() * numEvts) / (numEvts))) cheb.plotOn(massFrame, LineColor(kMagenta), LineStyle(kDotted), Normalization(((1.0 - sFrac.getValV()) * numEvts) / (numEvts))) tot.paramOn(massFrame, Layout(0.60, 0.99, 0.75)) massFrame.Draw() # In[12]: plotmax = hist.GetMaximum() * 1.05 sidesigma = sigma.getValV() leftlowside = -7. * sidesigma + mean.getValV() leftupside = -5. * sidesigma + mean.getValV() rightlowside = +5. * sidesigma + mean.getValV() rightupside = +7. * sidesigma + mean.getValV()
def rooFit205(): print ">>> setup model signal components: gaussians..." x = RooRealVar("x", "x", 0, 10) mean = RooRealVar("mean", "mean of gaussians", 5) sigma1 = RooRealVar("sigma1", "width of gaussians", 0.5) sigma2 = RooRealVar("sigma2", "width of gaussians", 1) sig1 = RooGaussian("sig1", "Signal component 1", x, mean, sigma1) sig2 = RooGaussian("sig2", "Signal component 2", x, mean, sigma2) sig1frac = RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0., 1.) sig = RooAddPdf("sig", "Signal", RooArgList(sig1, sig2), RooArgList(sig1frac)) print ">>> setup model background components: Chebychev polynomial plus exponential..." a0 = RooRealVar("a0", "a0", 0.5, 0., 1.) a1 = RooRealVar("a1", "a1", -0.2, 0., 1.) bkg1 = RooChebychev("bkg1", "Background 1", x, RooArgList(a0, a1)) alpha = RooRealVar("alpha", "alpha", -1) bkg2 = RooExponential("bkg2", "Background 2", x, alpha) bkg1frac = RooRealVar("bkg1frac", "fraction of component 1 in background", 0.2, 0., 1.) bkg = RooAddPdf("bkg", "Signal", RooArgList(bkg1, bkg2), RooArgList(bkg1frac)) print ">>> sum signal and background component..." bkgfrac = RooRealVar("bkgfrac", "fraction of background", 0.5, 0., 1.) model = RooAddPdf("model", "g1+g2+a", RooArgList(bkg, sig), RooArgList(bkgfrac)) print ">>> setup basic plot with data and full pdf..." data = model.generate(RooArgSet(x), 1000) # RooDataSet frame1 = x.frame( Title("Component plotting of pdf=(sig1+sig2)+(bkg1+bkg2)")) # RooPlot data.plotOn(frame1, Name("data")) model.plotOn(frame1, Name("model")) print ">>> clone frame for reuse..." frame2 = frame1.Clone("frame2") # RooPlot frame2.SetTitle("Get components with regular expressions") print ">>> make omponent by object reference..." # Plot multiple background components specified by object reference # Note that specified components may occur at any level in object tree # (e.g bkg is component of 'model' and 'sig2' is component 'sig') argset1 = RooArgSet(bkg) argset2 = RooArgSet(bkg2) argset3 = RooArgSet(bkg, sig2) model.plotOn(frame1, Components(argset1), LineColor(kRed), Name("bkgs1")) model.plotOn(frame1, Components(argset2), LineStyle(kDashed), LineColor(kRed), Name("bkg2")) model.plotOn(frame1, Components(argset3), LineStyle(kDotted), Name("bkgssig21")) print "\n>>> make component by name / regular expressions..." model.plotOn(frame2, Components("bkg"), LineColor(kAzure - 4), Name("bkgs2")) # by name model.plotOn(frame2, Components("bkg1,sig2"), LineColor(kAzure - 4), LineStyle(kDotted), Name("bkg1sig22")) # by name model.plotOn(frame2, Components("sig*"), LineColor(kAzure - 4), LineStyle(kDashed), Name("sigs2")) # with regexp (wildcard *) model.plotOn(frame2, Components("bkg1,sig*"), LineColor(kYellow), LineStyle(kDashed), Name("bkg1sigs2")) # with regexp (,) #Invisible() print "\n>>> draw pfds and fits on canvas..." canvas = TCanvas("canvas", "canvas", 100, 100, 1400, 600) legend1 = TLegend(0.22, 0.85, 0.4, 0.65) legend2 = TLegend(0.22, 0.85, 0.4, 0.65) for legend in [legend1, legend2]: legend.SetTextSize(0.032) legend.SetBorderSize(0) legend.SetFillStyle(0) canvas.Divide(2) canvas.cd(1) gPad.SetLeftMargin(0.15) gPad.SetRightMargin(0.02) frame1.GetYaxis().SetLabelOffset(0.008) frame1.GetYaxis().SetTitleOffset(1.6) frame1.GetYaxis().SetTitleSize(0.045) frame1.GetXaxis().SetTitleSize(0.045) frame1.Draw() legend1.AddEntry("data", "data", 'LEP') legend1.AddEntry("model", "model", 'L') legend1.AddEntry("bkgs1", "bkg", 'L') legend1.AddEntry("bkg2", "bkg2", 'L') legend1.AddEntry("bkgssig21", "bkg,sig2", 'L') legend1.Draw() canvas.cd(2) gPad.SetLeftMargin(0.15) gPad.SetRightMargin(0.02) frame2.GetYaxis().SetLabelOffset(0.008) frame2.GetYaxis().SetTitleOffset(1.6) frame2.GetYaxis().SetTitleSize(0.045) frame2.GetXaxis().SetTitleSize(0.045) frame2.Draw() legend2.AddEntry("data", "data", 'LEP') legend2.AddEntry("model", "model", 'L') legend2.AddEntry("bkgs2", "\"bkg\"", 'L') legend2.AddEntry("bkg1sig22", "\"bkg1,sig2\"", 'L') legend2.AddEntry("sigs2", "\"sig*\"", 'L') legend2.AddEntry("bkg1sigs2", "\"bkg1,sig*\"", 'L') legend2.Draw() canvas.SaveAs("rooFit205.png")
def rooFit501(): print ">>> setup model for physics sample..." x = RooRealVar("x", "x", -8, 8) mean = RooRealVar("mean", "mean", 0, -8, 8) sigma = RooRealVar("sigma", "sigma", 0.3, 0.1, 10) gauss = RooGaussian("gx", "gx", x, mean, sigma) a0 = RooRealVar("a0", "a0", -0.1, -1, 1) a1 = RooRealVar("a1", "a1", 0.004, -1, 1) px = RooChebychev("px", "px", x, RooArgList(a0, a1)) f = RooRealVar("f", "f", 0.2, 0., 1.) model = RooAddPdf("model", "model", RooArgList(gauss, px), RooArgList(f)) print ">>> setup model for control sample..." # NOTE: sigma is shared with the signal sample model mean_ctrl = RooRealVar("mean_ctrl", "mean_ctrl", -3, -8, 8) gauss_ctrl = RooGaussian("gauss_ctrl", "gauss_ctrl", x, mean_ctrl, sigma) a0_ctrl = RooRealVar("a0_ctrl", "a0_ctrl", -0.1, -1, 1) a1_ctrl = RooRealVar("a1_ctrl", "a1_ctrl", 0.5, -0.1, 1) px_ctrl = RooChebychev("px_ctrl", "px_ctrl", x, RooArgList(a0_ctrl, a1_ctrl)) f_ctrl = RooRealVar("f_ctrl", "f_ctrl", 0.5, 0., 1.) model_ctrl = RooAddPdf("model_ctrl", "model_ctrl", RooArgList(gauss_ctrl, px_ctrl), RooArgList(f_ctrl)) print ">>> generate events for both samples..." data = model.generate(RooArgSet(x), 100) # RooDataSet data_ctrl = model_ctrl.generate(RooArgSet(x), 2000) # RooDataSet print ">>> create index category and join samples..." # Define category to distinguish physics and control samples events sample = RooCategory("sample", "sample") sample.defineType("physics") sample.defineType("control") print ">>> construct combined dataset in (x,sample)..." combData = RooDataSet("combData", "combined data", RooArgSet(x), Index(sample), Import("physics", data), Import("control", data_ctrl)) print ">>> construct a simultaneous pdf in (x,sample)..." # Construct a simultaneous pdf using category sample as index simPdf = RooSimultaneous("simPdf", "simultaneous pdf", sample) # Associate model with the physics state and model_ctrl with the control state simPdf.addPdf(model, "physics") simPdf.addPdf(model_ctrl, "control") print ">>> perform a simultaneous fit..." # Perform simultaneous fit of model to data and model_ctrl to data_ctrl simPdf.fitTo(combData) print "\n>>> plot model slices on data slices..." frame1 = x.frame(Bins(30), Title("Physics sample")) # RooPlot combData.plotOn(frame1, Cut("sample==sample::physics")) # Plot "physics" slice of simultaneous pdf. # NBL You _must_ project the sample index category with data using ProjWData # as a RooSimultaneous makes no prediction on the shape in the index category # and can thus not be integrated simPdf.plotOn(frame1, Slice(sample, "physics"), ProjWData(RooArgSet(sample), combData)) simPdf.plotOn(frame1, Slice(sample, "physics"), Components("px"), ProjWData(RooArgSet(sample), combData), LineStyle(kDashed)) print "\n>>> plot control sample slices..." frame2 = x.frame(Bins(30), Title("Control sample")) # RooPlot combData.plotOn(frame2, Cut("sample==sample::control")) simPdf.plotOn(frame2, Slice(sample, "control"), ProjWData(RooArgSet(sample), combData)) simPdf.plotOn(frame2, Slice(sample, "control"), Components("px_ctrl"), ProjWData(RooArgSet(sample), combData), LineStyle(kDashed)) print "\n>>> draw on canvas..." canvas = TCanvas("canvas", "canvas", 100, 100, 1400, 600) canvas.Divide(2) canvas.cd(1) gPad.SetLeftMargin(0.15) gPad.SetRightMargin(0.02) frame1.GetYaxis().SetLabelOffset(0.008) frame1.GetYaxis().SetTitleOffset(1.6) frame1.GetYaxis().SetTitleSize(0.045) frame1.GetXaxis().SetTitleSize(0.045) frame1.Draw() canvas.cd(2) gPad.SetLeftMargin(0.15) gPad.SetRightMargin(0.02) frame2.GetYaxis().SetLabelOffset(0.008) frame2.GetYaxis().SetTitleOffset(1.6) frame2.GetYaxis().SetTitleSize(0.045) frame2.GetXaxis().SetTitleSize(0.045) frame2.Draw() canvas.SaveAs("rooFit501.png")
def rooFit201(): print ">>> setup model component: gaussian signals and Chebychev polynomial background..." x = RooRealVar("x","x",0,11) mean = RooRealVar("mean","mean of gaussians",5) sigma1 = RooRealVar("sigma1","width of gaussians",0.5) sigma2 = RooRealVar("sigma2","width of gaussians",1) sig1 = RooGaussian("sig1","Signal component 1",x,mean,sigma1) sig2 = RooGaussian("sig2","Signal component 2",x,mean,sigma2) a0 = RooRealVar("a0","a0",0.5,0.,1.) a1 = RooRealVar("a1","a1",-0.2,0.,1.) bkg = RooChebychev("bkg","Background",x,RooArgList(a0,a1)) print ">>>\n>>> METHOD 1 - Two RooAddPdfs" print ">>> add signal components..." # Sum the signal components into a composite signal p.d.f. sig1frac = RooRealVar("sig1frac","fraction of component 1 in signal",0.8,0.,1.) sig = RooAddPdf("sig","Signal",RooArgList(sig1,sig2),RooArgList(sig1frac)) print ">>> add signal and background..." # Sum the composite signal and background bkgfrac = RooRealVar("bkgfrac","fraction of background",0.5,0.,1.) model = RooAddPdf("model","g1+g2+a",RooArgList(bkg,sig),RooArgList(bkgfrac)) print ">>> sample, fit and plot model..." data = model.generate(RooArgSet(x),1000) # RooDataSet model.fitTo(data) frame1 = x.frame(Title("Example of composite pdf=(sig1+sig2)+bkg")) # RooPlot data.plotOn(frame1,Binning(50),Name("data")) model.plotOn(frame1,Name("model")) # Overlay the background component of model with a dashed line argset1 = RooArgSet(bkg) model.plotOn(frame1,Components(argset1),LineWidth(2),Name("bkg")) #,LineStyle(kDashed) # Overlay the background+sig2 components of model with a dotted line argset2 = RooArgSet(bkg,sig2) model.plotOn(frame1,Components(argset2),LineWidth(2),LineStyle(kDashed),LineColor(kAzure-4),Name("bkgsig2")) #,LineStyle(kDotted) print "\n>>> structure of composite pdf:" model.Print("t") # "tree" mode print "\n>>> parameters:" params = model.getVariables() # RooArgSet params.Print("v") params.Print() print "\n>>> params.find(\"...\").getVal():" print ">>> sigma1 = %.2f" % params.find("sigma1").getVal() print ">>> sigma2 = %.2f" % params.find("sigma2").getVal() print ">>> bkgfrac = %5.2f" % params.find("bkgfrac").getVal() print ">>> sig1frac = %5.2f" % params.find("sig1frac").getVal() print ">>>\n>>> components:" comps = model.getComponents() # RooArgSet sig = comps.find("sig") # RooAbsArg sigVars = sig.getVariables() # RooArgSet sigVars.Print() print ">>>\n>>> METHOD 2 - One RooAddPdf with recursive fractions" # Construct sum of models on one go using recursive fraction interpretations # model2 = bkg + (sig1 + sig2) model2 = RooAddPdf("model","g1+g2+a",RooArgList(bkg,sig1,sig2),RooArgList(bkgfrac,sig1frac),kTRUE) # NB: Each coefficient is interpreted as the fraction of the # left-hand component of the i-th recursive sum, i.e. # sum4 = A + ( B + ( C + D ) ) # with fraction fA, fB and fC expands to # sum4 = fA*A + (1-fA)*(fB*B + (1-fB)*(fC*C + (1-fC)*D)) print ">>> plot recursive addition model..." argset3 = RooArgSet(bkg,sig2) model2.plotOn(frame1,LineColor(kRed),LineStyle(kDashDotted),LineWidth(3),Name("model2")) model2.plotOn(frame1,Components(argset3),LineColor(kMagenta),LineStyle(kDashDotted),LineWidth(3),Name("bkgsig22")) model2.Print("t") print ">>> draw pdfs and fits on canvas..." canvas = TCanvas("canvas","canvas",100,100,800,600) legend = TLegend(0.57,0.87,0.95,0.65) legend.SetTextSize(0.030) legend.SetBorderSize(0) legend.SetFillStyle(0) gPad.SetLeftMargin(0.14); gPad.SetRightMargin(0.02) frame1.GetYaxis().SetLabelOffset(0.008) frame1.GetYaxis().SetTitleOffset(1.4) frame1.GetYaxis().SetTitleSize(0.045) frame1.GetXaxis().SetTitleSize(0.045) frame1.Draw() legend.AddEntry("data", "data", 'LEP') legend.AddEntry("model", "composite model", 'L') legend.AddEntry("model2", "composite model (method 2)", 'L') legend.AddEntry("bkg", "background only", 'L') legend.AddEntry("bkgsig2", "background + signal 2", 'L') legend.AddEntry("bkgsig22","background + signal 2 (method 2)",'L') legend.Draw() canvas.SaveAs("rooFit201.png")
def rooFit206(): print ">>> setup model signal components: gaussians..." x = RooRealVar("x", "x", 0, 10) mean = RooRealVar("mean", "mean of gaussians", 5) sigma1 = RooRealVar("sigma1", "width of gaussians", 0.5) sigma2 = RooRealVar("sigma2", "width of gaussians", 1) sig1 = RooGaussian("sig1", "Signal component 1", x, mean, sigma1) sig2 = RooGaussian("sig2", "Signal component 2", x, mean, sigma2) sig1frac = RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0., 1.) sig = RooAddPdf("sig", "Signal", RooArgList(sig1, sig2), RooArgList(sig1frac)) print ">>> setup model background components: Chebychev polynomial plus exponential..." a0 = RooRealVar("a0", "a0", 0.5, 0., 1.) a1 = RooRealVar("a1", "a1", -0.2, 0., 1.) bkg1 = RooChebychev("bkg1", "Background 1", x, RooArgList(a0, a1)) alpha = RooRealVar("alpha", "alpha", -1) bkg2 = RooExponential("bkg2", "Background 2", x, alpha) bkg1frac = RooRealVar("bkg1frac", "fraction of component 1 in background", 0.2, 0., 1.) bkg = RooAddPdf("bkg", "Signal", RooArgList(bkg1, bkg2), RooArgList(bkg1frac)) print ">>> sum signal and background component..." bkgfrac = RooRealVar("bkgfrac", "fraction of background", 0.5, 0., 1.) model = RooAddPdf("model", "g1+g2+a", RooArgList(bkg, sig), RooArgList(bkgfrac)) print ">>> composite tree in ASCII:" model.Print("t") print "\n>>> write to txt file" model.printCompactTree("", "rooFit206_asciitree.txt") print ">>> draw composite tree graphically..." # Print GraphViz DOT file with representation of tree model.graphVizTree("rooFit206_model.dot") # Make graphic output file with one of the GraphViz tools # (freely available from www.graphviz.org) # # 'Top-to-bottom graph' # unix> dot -Tgif -o rooFit206_model_dot.gif rooFit206_model.dot # # 'Spring-model graph' # unix> fdp -Tgif -o rooFit206_model_fdp.gif rooFit206_model.dot print ">>> plot everything..." data = model.generate(RooArgSet(x), 1000) # RooDataSet frame1 = x.frame( Title("Component plotting of pdf=(sig1+sig2)+(bkg1+bkg2)")) # RooPlot data.plotOn(frame1, Name("data"), Binning(40)) model.plotOn(frame1, Name("model")) argset1 = RooArgSet(bkg) argset2 = RooArgSet(bkg2) argset3 = RooArgSet(bkg, sig2) model.plotOn(frame1, Components(argset1), LineColor(kRed), Name("bkg")) model.plotOn(frame1, Components(argset2), LineStyle(kDashed), LineColor(kRed), Name("bkg2")) model.plotOn(frame1, Components(argset3), LineStyle(kDotted), Name("bkgsig2")) print "\n>>> draw pfds and fits on canvas..." canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600) legend = TLegend(0.22, 0.85, 0.4, 0.65) legend.SetTextSize(0.032) legend.SetBorderSize(0) legend.SetFillStyle(0) gPad.SetLeftMargin(0.15) gPad.SetRightMargin(0.02) frame1.GetYaxis().SetLabelOffset(0.008) frame1.GetYaxis().SetTitleOffset(1.6) frame1.GetYaxis().SetTitleSize(0.045) frame1.GetXaxis().SetTitleSize(0.045) frame1.Draw() legend.AddEntry("data", "data", 'LEP') legend.AddEntry("model", "model", 'L') legend.AddEntry("bkg", "bkg", 'L') legend.AddEntry("bkg2", "bkg2", 'L') legend.AddEntry("bkgsig2", "bkg,sig2", 'L') legend.Draw() canvas.SaveAs("rooFit206.png")