Rf 6 0 7_Fitresult

Likelihood and minimization: demonstration of options of the RooFitResult class

Author: Clemens Lange, Wouter Verkerke (C++ version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Wednesday, January 19, 2022 at 10:28 AM.

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from __future__ import print_function
import ROOT

Create pdf, data

Declare observable x

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x = ROOT.RooRealVar("x", "x", 0, 10)

Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters

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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 pdf

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a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0.0, 1.0)
a1 = ROOT.RooRealVar("a1", "a1", -0.2)
bkg = ROOT.RooChebychev("bkg", "Background", x, [a0, a1])

Sum the signal components into a composite signal pdf

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sig1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0.0, 1.0)
sig = ROOT.RooAddPdf("sig", "Signal", [sig1, sig2], [sig1frac])

Sum the composite signal and background

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bkgfrac = ROOT.RooRealVar("bkgfrac", "fraction of background", 0.5, 0.0, 1.0)
model = ROOT.RooAddPdf("model", "g1+g2+a", [bkg, sig], [bkgfrac])

Generate 1000 events

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data = model.generate({x}, 1000)

Fit pdf to data, save fit result

Perform fit and save result

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r = model.fitTo(data, Save=True)

Summary printing: Basic info plus final values of floating fit parameters

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r.Print()

Verbose printing: Basic info, of constant parameters, and final values of floating parameters, correlations

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r.Print("v")

Visualize correlation matrix

Construct 2D color plot of correlation matrix

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ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetPalette(1)
hcorr = r.correlationHist()

Visualize ellipse corresponding to single correlation matrix element

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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

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print("EDM = ", r.edm())
print("-log(L) minimum = ", r.minNll())

Access list of final fit parameter values

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print("final value of floating parameters")
r.floatParsFinal().Print("s")

Access correlation matrix elements

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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

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cor = r.correlationMatrix()
cov = r.covarianceMatrix()

Print correlation, matrix

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print("correlation matrix")
cor.Print()
print("covariance matrix")
cov.Print()

Persist fit result in root file

Open ROOT file save save result

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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")

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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")

Draw all canvases

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from ROOT import gROOT 
gROOT.GetListOfCanvases().Draw()