def rooFit602():
print ">>> setup model..."
x = RooRealVar("x","x",0,10)
mean = RooRealVar("mean","mean of gaussian",5)
sigma1 = RooRealVar("sigma1","width of gaussian",0.5)
sigma2 = RooRealVar("sigma2","width of gaussian",1)
sig1 = RooGaussian("sig1","Signal component 1",x,mean,sigma1)
sig2 = RooGaussian("sig2","Signal component 2",x,mean,sigma2)
a0 = RooRealVar("a0","a0",0.5,0.,1.)
a1 = RooRealVar("a1","a1",-0.2,0.,1.)
bkg = RooChebychev("bkg","Background",x,RooArgSet(a0,a1))
sig1frac = RooRealVar("sig1frac","fraction of component 1 in signal",0.8,0.,1.)
sig = RooAddPdf("sig","Signal",RooArgList(sig1,sig2),sig1frac)
bkgfrac = RooRealVar("bkgfrac","fraction of background",0.5,0.,1.)
model = RooAddPdf("model","g1+g2+a",RooArgList(bkg,sig),bkgfrac)
print ">>> create binned dataset..."
data = model.generate(RooArgSet(x),10000) # RooDataSet
hist = data.binnedClone() # RooDataHist
# Construct a chi^2 of the data and the model.
# When a p.d.f. is used in a chi^2 fit, the probability density scaled
# by the number of events in the dataset to obtain the fit function
# If model is an extended p.d.f, the expected number events is used
# instead of the observed number of events.
model.chi2FitTo(hist)
# NB: It is also possible to fit a RooAbsReal function to a RooDataHist
# using chi2FitTo().
# Note that entries with zero bins are _not_ allowed
# for a proper chi^2 calculation and will give error
# messages
data_small = date.reduce(EventRange(1,100)) # RooDataSet
hist_small = data_small.binnedClone() # RooDataHist
chi2_lowstat("chi2_lowstat","chi2",model,hist_small)
print ">>> chi2_lowstat.getVal() = %s" % chi2_lowstat.getVal()
