rf602_chi2fit.py

#####################################
#
# 'LIKELIHOOD AND MINIMIZATION' ROOT.RooFit tutorial macro #602
#
# Setting up a chi^2 fit to a binned dataset
#
#
#
# 07/2008 - Wouter Verkerke
#
# /


import ROOT


def rf602_chi2fit():

    # S e t u p   m o d e l
    # ---------------------

    # 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., 1.)
    a1 = ROOT.RooRealVar("a1", "a1", -0.2, 0., 1.)
    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 = ROOT.RooAddPdf(
        "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 = ROOT.RooAddPdf(
        "model", "g1+g2+a", ROOT.RooArgList(bkg, sig), ROOT.RooArgList(bkgfrac))

    # C r e a t e   b i n n e d   d a t a s e t
    # -----------------------------------------

    d = model.generate(ROOT.RooArgSet(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 = ROOT.RooChi2Var("chi2_lowstat", "chi2", model, dhsmall)
    print chi2_lowstat.getVal()


if __name__ == "__main__":
    rf602_chi2fit()