import ROOT
def fillWorkspace(w):
x = ROOT.RooRealVar("x", "x", 0, 10)
mean = ROOT.RooRealVar("mean", "mean of gaussians", 5, 0, 10)
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])
bkgfrac = ROOT.RooRealVar("bkgfrac", "fraction of background", 0.5, 0.0, 1.0)
model = ROOT.RooAddPdf("model", "g1+g2+a", [bkg, sig], [bkgfrac])
w.Import(model)
params = model.getParameters({x})
w.defineSet("parameters", params)
w.defineSet("observables", {x})
refData = model.generate({x}, 10000)
model.fitTo(refData, PrintLevel=-1)
w.saveSnapshot("reference_fit", params, True)
bkgfrac.setVal(1)
bkgfrac.setConstant(True)
bkgfrac.removeError()
model.fitTo(refData, PrintLevel=-1)
w.saveSnapshot("reference_fit_bkgonly", params, True)
w = ROOT.RooWorkspace("w")
fillWorkspace(w)
model = w["model"]
data = model.generate(w.set("observables"), 1000)
model.fitTo(data)
frame = (w.set(
"observables").
first()).frame()
data.plotOn(frame)
model.plotOn(frame)
w.loadSnapshot("reference_fit")
model.plotOn(frame, LineColor="r")
w.loadSnapshot("reference_fit_bkgonly")
model.plotOn(frame, LineColor="r", LineStyle="--")
c = ROOT.TCanvas("rf510_wsnamedsets", "rf503_wsnamedsets", 600, 600)
ROOT.gPad.SetLeftMargin(0.15)
frame.GetYaxis().SetTitleOffset(1.4)
frame.Draw()
c.SaveAs("rf510_wsnamedsets.png")
w.Print()
ROOT.gDirectory.Add(w)