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 Friday, May 13, 2022 at 09:40 AM.
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 pdf
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
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
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
data = model.generate({x}, 1000)
Fit pdf to data, save fit result¶
Perform fit and save result
r = model.fitTo(data, Save=True)
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")
Draw all canvases
from ROOT import gROOT
gROOT.GetListOfCanvases().Draw()
