doc/v636/rf601__intminuit_8py.html

import ROOT


# Setup pdf and likelihood

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


# Observable

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


# Model (intentional strong correlations)

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

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 unbinned likelihood of model w.r.t. data

nll = model.createNLL(data)


# Interactive minimization, error analysis

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


# Create MINUIT interface object

m = ROOT.RooMinimizer(nll)


# Activate verbose logging of MINUIT parameter space stepping

m.setVerbose(True)


# Call MIGRAD to minimize the likelihood

m.migrad()


# Print values of all parameters, reflect values (and error estimates)

# that are back propagated from MINUIT

model.getParameters({x}).Print("s")


# Disable verbose logging

m.setVerbose(False)


# Run HESSE to calculate errors from d2L/dp2

m.hesse()


# Print value (and error) of sigma_g2 parameter, reflects

# value and error back propagated from MINUIT

sigma_g2.Print()


# Run MINOS on sigma_g2 parameter only

m.minos({sigma_g2})


# Print value (and error) of sigma_g2 parameter, reflects

# value and error back propagated from MINUIT

sigma_g2.Print()


# Saving results, contour plots

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


# Save a snapshot of the fit result. ROOT.This object contains the initial

# fit parameters, final fit parameters, complete correlation

# matrix, EDM, minimized FCN , last MINUIT status code and

# the number of times the ROOT.RooFit function object has indicated evaluation

# problems (e.g. zero probabilities during likelihood evaluation)

r = m.save()


# Make contour plot of mx vs sx at 1,2, sigma

frame = m.contour(frac, sigma_g2, 1, 2, 3)

frame.SetTitle("Contour plot")


# Print the fit result snapshot

r.Print("v")


# Change parameter values, plotting

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


# At any moment you can manually change the value of a (constant)

# parameter

mean.setVal(0.3)


# Rerun MIGRAD,HESSE

m.migrad()

m.hesse()

frac.Print()


# Now fix sigma_g2

sigma_g2.setConstant(True)


# Rerun MIGRAD,HESSE

m.migrad()

m.hesse()

frac.Print()


c = ROOT.TCanvas("rf601_intminuit", "rf601_intminuit", 600, 600)

ROOT.gPad.SetLeftMargin(0.15)

frame.GetYaxis().SetTitleOffset(1.4)

frame.Draw()


c.SaveAs("rf601_intminuit.png")

TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358

Print
void Print(Option_t *option="") const override