## \file ## \ingroup tutorial_roofit ## \notebook ## ## Likelihood and minimization: demonstration of options of the RooFitResult class ## ## \macro_code ## ## \date February 2018 ## \authors Clemens Lange, Wouter Verkerke (C++ version) from __future__ import print_function import ROOT # Create pdf, data # -------------------------------- # Declare observable x x = ROOT.RooRealVar("x", "x", 0, 10) # Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and # their parameters mean = ROOT.RooRealVar("mean", "mean of gaussians", 5, -10, 10) sigma1 = ROOT.RooRealVar("sigma1", "width of gaussians", 0.5, 0.1, 10) sigma2 = ROOT.RooRealVar("sigma2", "width of gaussians", 1, 0.1, 10) sig1 = ROOT.RooGaussian("sig1", "Signal component 1", x, mean, sigma1) sig2 = ROOT.RooGaussian("sig2", "Signal component 2", x, mean, sigma2) # Build Chebychev polynomial p.d.f. a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0., 1.) a1 = ROOT.RooRealVar("a1", "a1", -0.2) bkg = ROOT.RooChebychev("bkg", "Background", x, ROOT.RooArgList(a0, a1)) # Sum the signal components into a composite signal p.d.f. sig1frac = ROOT.RooRealVar( "sig1frac", "fraction of component 1 in signal", 0.8, 0., 1.) sig = ROOT.RooAddPdf( "sig", "Signal", ROOT.RooArgList(sig1, sig2), ROOT.RooArgList(sig1frac)) # Sum the composite signal and background bkgfrac = ROOT.RooRealVar("bkgfrac", "fraction of background", 0.5, 0., 1.) model = ROOT.RooAddPdf( "model", "g1+g2+a", ROOT.RooArgList(bkg, sig), ROOT.RooArgList(bkgfrac)) # Generate 1000 events data = model.generate(ROOT.RooArgSet(x), 1000) # Fit pdf to data, save fit result # ------------------------------------------------------------- # Perform fit and save result r = model.fitTo(data, ROOT.RooFit.Save()) # Print fit results # --------------------------------- # Summary printing: Basic info plus final values of floating fit parameters r.Print() # Verbose printing: Basic info, of constant parameters, and # final values of floating parameters, correlations r.Print("v") # Visualize correlation matrix # ------------------------------------------------------- # Construct 2D color plot of correlation matrix ROOT.gStyle.SetOptStat(0) ROOT.gStyle.SetPalette(1) hcorr = r.correlationHist() # Visualize ellipse corresponding to single correlation matrix element frame = ROOT.RooPlot(sigma1, sig1frac, 0.45, 0.60, 0.65, 0.90) frame.SetTitle("Covariance between sigma1 and sig1frac") r.plotOn(frame, sigma1, sig1frac, "ME12ABHV") # Access fit result information # --------------------------------------------------------- # Access basic information print("EDM = ", r.edm()) print("-log(L) minimum = ", r.minNll()) # Access list of final fit parameter values print("final value of floating parameters") r.floatParsFinal().Print("s") # Access correlation matrix elements print("correlation between sig1frac and a0 is ", r.correlation( sig1frac, a0)) print("correlation between bkgfrac and mean is ", r.correlation( "bkgfrac", "mean")) # Extract covariance and correlation matrix as ROOT.TMatrixDSym cor = r.correlationMatrix() cov = r.covarianceMatrix() # Print correlation, matrix print("correlation matrix") cor.Print() print("covariance matrix") cov.Print() # Persist fit result in root file # ------------------------------------------------------------- # Open ROOT file save save result f = ROOT.TFile("rf607_fitresult.root", "RECREATE") r.Write("rf607") f.Close() # In a clean ROOT session retrieve the persisted fit result as follows: # r = gDirectory.Get("rf607") c = ROOT.TCanvas("rf607_fitresult", "rf607_fitresult", 800, 400) c.Divide(2) c.cd(1) ROOT.gPad.SetLeftMargin(0.15) hcorr.GetYaxis().SetTitleOffset(1.4) hcorr.Draw("colz") c.cd(2) ROOT.gPad.SetLeftMargin(0.15) frame.GetYaxis().SetTitleOffset(1.6) frame.Draw() c.SaveAs("rf607_fitresult.png")