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)
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))
a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0., 1.)
a1 = ROOT.RooRealVar("a1", "a1", -0.2, 0., 1.)
bkg1 = ROOT.RooChebychev("bkg1", "Background 1",
x, ROOT.RooArgList(a0, a1))
alpha = ROOT.RooRealVar("alpha", "alpha", -1)
bkg2 = ROOT.RooExponential("bkg2", "Background 2", x, alpha)
bkg1frac = ROOT.RooRealVar(
"sig1frac", "fraction of component 1 in background", 0.2, 0., 1.)
bkg = ROOT.RooAddPdf(
"bkg", "Signal", ROOT.RooArgList(bkg1, bkg2), ROOT.RooArgList(sig1frac))
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))
params = model.getParameters(ROOT.RooArgSet(x))
initParams = params.snapshot()
data = model.generate(ROOT.RooArgSet(x), 1000)
model.fitTo(data)
params.printLatex()
params.printLatex(ROOT.RooFit.Columns(2))
params.printLatex(ROOT.RooFit.Sibling(initParams))
params.printLatex(ROOT.RooFit.Sibling(initParams), ROOT.RooFit.Columns(2))
params.printLatex(ROOT.RooFit.Sibling(initParams),
ROOT.RooFit.OutputFile("rf407_latextables.tex"))