import ROOT
x = ROOT.RooRealVar("x", "x", 0, 10)
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)
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])
sig1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0.0, 1.0)
sig = ROOT.RooAddPdf("sig", "Signal", [sig1, sig2], [sig1frac])
x.setRange("signalRange", 4, 6)
nsig = ROOT.RooRealVar("nsig", "number of signal events in signalRange", 500, 0.0, 10000)
nbkg = ROOT.RooRealVar("nbkg", "number of background events in signalRange", 500, 0, 10000)
esig = ROOT.RooExtendPdf("esig", "extended signal pdf", sig, nsig, "signalRange")
ebkg = ROOT.RooExtendPdf("ebkg", "extended background pdf", bkg, nbkg, "signalRange")
model = ROOT.RooAddPdf("model", "(g1+g2)+a", [ebkg, esig])
data = model.generate({x}, 1000)
r = model.fitTo(data, Extended=True, Save=True)
r.Print()