doc/backup_latest-stable/rf207__comptools_8py.html

import ROOT


# Set up composite pdf dataset

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


# Declare observable x

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


# Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and

# their parameters

mean = ROOT.RooRealVar("mean", "mean of gaussians", 5)

sigma = ROOT.RooRealVar("sigma", "width of gaussians", 0.5)

sig = ROOT.RooGaussian("sig", "Signal component 1", x, mean, sigma)


# Build Chebychev polynomial p.d.f.

a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0.0, 1.0)

a1 = ROOT.RooRealVar("a1", "a1", 0.2, 0.0, 1.0)

bkg1 = ROOT.RooChebychev("bkg1", "Background 1", x, [a0, a1])


# Build expontential pdf

alpha = ROOT.RooRealVar("alpha", "alpha", -1)

bkg2 = ROOT.RooExponential("bkg2", "Background 2", x, alpha)


# Sum the background components into a composite background p.d.f.

bkg1frac = ROOT.RooRealVar("bkg1frac", "fraction of component 1 in background", 0.2, 0.0, 1.0)

bkg = ROOT.RooAddPdf("bkg", "Signal", [bkg1, bkg2], [bkg1frac])


# Sum the composite signal and background

bkgfrac = ROOT.RooRealVar("bkgfrac", "fraction of background", 0.5, 0.0, 1.0)

model = ROOT.RooAddPdf("model", "g1+g2+a", [bkg, sig], [bkgfrac])


# Create dummy dataset that has more observables than the above pdf

y = ROOT.RooRealVar("y", "y", -10, 10)

data = ROOT.RooDataSet("data", "data", {x, y})


# Basic information requests

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


# Get list of observables

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


# Get list of observables of pdf in context of a dataset

#

# Observables are define each context as the variables

# shared between a model and a dataset. In self case

# that is the variable 'x'


model_obs = model.getObservables(data)

ROOT.SetOwnership(model_obs, True)

model_obs.Print("v")


# Get list of parameters

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


# Get list of parameters, list of observables

model_params = model.getParameters({x})

ROOT.SetOwnership(model_params, True)

model_params.Print("v")


# Get list of parameters, a dataset

# (Gives identical results to operation above)

model_params2 = model.getParameters(data)

ROOT.SetOwnership(model_params2, True)

model_params2.Print()


# Get list of components

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


# Get list of component objects, top-level node

model_comps = model.getComponents()

ROOT.SetOwnership(model_comps, True)

model_comps.Print("v")


# Modifications to structure of composites

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


# Create a second Gaussian

sigma2 = ROOT.RooRealVar("sigma2", "width of gaussians", 1)

sig2 = ROOT.RooGaussian("sig2", "Signal component 1", x, mean, sigma2)


# Create a sum of the original Gaussian plus the second Gaussian

sig1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0.0, 1.0)

sigsum = ROOT.RooAddPdf("sigsum", "sig+sig2", [sig, sig2], [sig1frac])


# Construct a customizer utility to customize model

cust = ROOT.RooCustomizer(model, "cust")


# Instruct the customizer to replace node 'sig' with node 'sigsum'

cust.replaceArg(sig, sigsum)


# Build a clone of the input pdf according to the above customization

# instructions. Each node that requires modified is clone so that the

# original pdf remained untouched. The name of each cloned node is that

# of the original node suffixed by the name of the customizer object

#

# The returned head node own all nodes that were cloned as part of

# the build process so when cust_clone is deleted so will all other

# nodes that were created in the process.

cust_clone = cust.build(True)

ROOT.SetOwnership(cust_clone, True)


# Print structure of clone of model with sig.sigsum replacement.

cust_clone.Print("t")

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