rf607_fitresult.py

## \ingroup tutorial_roofit_main

## \notebook

## Likelihood and minimization: demonstration of options of the RooFitResult class

##

## \macro_image

## \macro_code

## \macro_output

##

## \date February 2018

## \authors Clemens Lange, Wouter Verkerke (C++ version)

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, PrintLevel=-1)

# 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")