doc/v626/rf502__wspacewrite_8py.html

import ROOT


# Create model and 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, 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)


# Build Chebychev polynomial pdf

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])


# Sum the signal components into a composite signal pdf

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

sig = ROOT.RooAddPdf("sig", "Signal", [sig1, sig2], [sig1frac])


# 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])


# Generate a data sample of 1000 events in x from model

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


# Create workspace, import data and model

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


# Create a empty workspace

w = ROOT.RooWorkspace("w", "workspace")


# Import model and all its components into the workspace

w.Import(model)


# Import data into the workspace

w.Import(data)


# Print workspace contents

w.Print()


# Save workspace in file

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


# Save the workspace into a ROOT file

w.writeToFile("rf502_workspace_py.root")