doc/v628/rf605__profilell_8py.html

import ROOT


# Create model and dataset

# -----------------------------------------------


# Observable

x = ROOT.RooRealVar("x", "x", -20, 20)


# Model (intentional strong correlations)

mean = ROOT.RooRealVar("mean", "mean of g1 and g2", 0, -10, 10)

sigma_g1 = ROOT.RooRealVar("sigma_g1", "width of g1", 3)

g1 = ROOT.RooGaussian("g1", "g1", x, mean, sigma_g1)


sigma_g2 = ROOT.RooRealVar("sigma_g2", "width of g2", 4, 3.0, 6.0)

g2 = ROOT.RooGaussian("g2", "g2", x, mean, sigma_g2)


frac = ROOT.RooRealVar("frac", "frac", 0.5, 0.0, 1.0)

model = ROOT.RooAddPdf("model", "model", [g1, g2], [frac])


# Generate 1000 events

data = model.generate({x}, 1000)


# Construct plain likelihood

# ---------------------------------------------------


# Construct unbinned likelihood

nll = model.createNLL(data, NumCPU=2)


# Minimize likelihood w.r.t all parameters before making plots

ROOT.RooMinimizer(nll).migrad()


# Plot likelihood scan frac

frame1 = frac.frame(Bins=10, Range=(0.01, 0.95), Title="LL and profileLL in frac")

nll.plotOn(frame1, ShiftToZero=True)


# Plot likelihood scan in sigma_g2

frame2 = sigma_g2.frame(Bins=10, Range=(3.3, 5.0), Title="LL and profileLL in sigma_g2")

nll.plotOn(frame2, ShiftToZero=True)


# Construct profile likelihood in frac

# -----------------------------------------------------------------------


# The profile likelihood estimator on nll for frac will minimize nll w.r.t

# all floating parameters except frac for each evaluation


pll_frac = nll.createProfile({frac})


# Plot the profile likelihood in frac

pll_frac.plotOn(frame1, LineColor="r")


# Adjust frame maximum for visual clarity

frame1.SetMinimum(0)

frame1.SetMaximum(3)


# Construct profile likelihood in sigma_g2

# -------------------------------------------------------------------------------


# The profile likelihood estimator on nll for sigma_g2 will minimize nll

# w.r.t all floating parameters except sigma_g2 for each evaluation

pll_sigmag2 = nll.createProfile({sigma_g2})


# Plot the profile likelihood in sigma_g2

pll_sigmag2.plotOn(frame2, LineColor="r")


# Adjust frame maximum for visual clarity

frame2.SetMinimum(0)

frame2.SetMaximum(3)


# Make canvas and draw ROOT.RooPlots

c = ROOT.TCanvas("rf605_profilell", "rf605_profilell", 800, 400)

c.Divide(2)

c.cd(1)

ROOT.gPad.SetLeftMargin(0.15)

frame1.GetYaxis().SetTitleOffset(1.4)

frame1.Draw()

c.cd(2)

ROOT.gPad.SetLeftMargin(0.15)

frame2.GetYaxis().SetTitleOffset(1.4)

frame2.Draw()


c.SaveAs("rf605_profilell.png")

migrad
Int_t migrad()