ROOT   Reference Guide
rf607_fitresult.py File Reference

Namespaces

namespace  rf607_fitresult

Detailed Description

Likelihood and minimization: demonstration of options of the RooFitResult class

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", "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", "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)