doc/master/rf602__chi2fit_8py.html

import ROOT


# Set up model

# ---------------------


# 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)

sigma1 = ROOT.RooRealVar("sigma1", "width of gaussians", 0.5)

sigma2 = ROOT.RooRealVar("sigma2", "width of gaussians", 1)


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, 0.0, 1.0)

a1 = ROOT.RooRealVar("a1", "a1", 0.2, 0.0, 1.0)

bkg = ROOT.RooChebychev("bkg", "Background", x, [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, 0.0, 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])


# Create biuned dataset

# -----------------------------------------


d = model.generate({x}, 10000)

dh = d.binnedClone()


# Construct a chi^2 of the data and the model.

# When a p.d.f. is used in a chi^2 fit, probability density scaled

# by the number of events in the dataset to obtain the fit function

# If model is an extended p.d.f, expected number events is used

# instead of the observed number of events.

ll = ROOT.RooLinkedList()

model.chi2FitTo(dh, ll)


# NB: It is also possible to fit a ROOT.RooAbsReal function to a ROOT.RooDataHist

# using chi2FitTo().


# Note that entries with zero bins are _not_ allowed

# for a proper chi^2 calculation and will give error

# messages

dsmall = d.reduce(ROOT.RooFit.EventRange(1, 100))

dhsmall = dsmall.binnedClone()

chi2_lowstat = model.createChi2(dhsmall)

print(chi2_lowstat.getVal())