doc/v626/rf902__numgenconfig_8py.html

import ROOT


# Adjust global MC sampling strategy

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


# Example pdf for use below

x = ROOT.RooRealVar("x", "x", 0, 10)

model = ROOT.RooChebychev("model", "model", x, [0.0, 0.5, -0.1])


# Change global strategy for 1D sampling problems without conditional observable

# (1st kFALSE) and without discrete observable (2nd kFALSE) from ROOT.RooFoamGenerator,

# ( an interface to the ROOT.TFoam MC generator with adaptive subdivisioning strategy ) to ROOT.RooAcceptReject,

# a plain accept/reject sampling algorithm [ ROOT.RooFit default before

# ROOT 5.23/04 ]

ROOT.RooAbsPdf.defaultGeneratorConfig().method1D(False, False).setLabel("RooAcceptReject")


# Generate 10Kevt using ROOT.RooAcceptReject

data_ar = model.generate({x}, 10000, Verbose=True)

data_ar.Print()


# Adjusting default config for a specific pdf

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


# Another possibility: associate custom MC sampling configuration as default for object 'model'

# The kTRUE argument will install a clone of the default configuration as specialized configuration

# for self model if none existed so far

model.specialGeneratorConfig(True).method1D(False, False).setLabel("RooFoamGenerator")


# Adjusting parameters of a specific technique

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


# Adjust maximum number of steps of ROOT.RooIntegrator1D in the global

# default configuration

ROOT.RooAbsPdf.defaultGeneratorConfig().getConfigSection("RooAcceptReject").setRealValue("nTrial1D", 2000)


# Example of how to change the parameters of a numeric integrator

# (Each config section is a ROOT.RooArgSet with ROOT.RooRealVars holding real-valued parameters

#  and ROOT.RooCategories holding parameters with a finite set of options)

model.specialGeneratorConfig().getConfigSection("RooFoamGenerator").setRealValue("chatLevel", 1)


# Generate 10Kevt using ROOT.RooFoamGenerator (FOAM verbosity increased

# with above chatLevel adjustment for illustration purposes)

data_foam = model.generate({x}, 10000, ROOT.RooFit.Verbose())

data_foam.Print()