ROOT   Reference Guide
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rf101_basics.py File Reference

## Detailed Description

This tutorial illustrates the basic features of RooFit.

import ROOT
# Set up model
# ---------------------
# Declare variables x,mean,sigma with associated name, title, initial
# value and allowed range
x = ROOT.RooRealVar("x", "x", -10, 10)
mean = ROOT.RooRealVar("mean", "mean of gaussian", 1, -10, 10)
sigma = ROOT.RooRealVar("sigma", "width of gaussian", 1, 0.1, 10)
# Build gaussian pdf in terms of x,mean and sigma
gauss = ROOT.RooGaussian("gauss", "gaussian PDF", x, mean, sigma)
# Construct plot frame in 'x'
xframe = x.frame(Title="Gaussian pdf") # RooPlot
# Plot model and change parameter values
# ---------------------------------------------------------------------------
# Plot gauss in frame (i.e. in x)
gauss.plotOn(xframe)
# Change the value of sigma to 3
sigma.setVal(3)
# Plot gauss in frame (i.e. in x) and draw frame on canvas
gauss.plotOn(xframe, LineColor="r")
# Generate events
# -----------------------------
# Generate a dataset of 1000 events in x from gauss
data = gauss.generate({x}, 10000) # ROOT.RooDataSet
# Make a second plot frame in x and draw both the
# data and the pdf in the frame
xframe2 = x.frame(Title="Gaussian pdf with data") # RooPlot
data.plotOn(xframe2)
gauss.plotOn(xframe2)
# Fit model to data
# -----------------------------
# Fit pdf to data
gauss.fitTo(data, PrintLevel=-1)
# Print values of mean and sigma (that now reflect fitted values and
# errors)
mean.Print()
sigma.Print()
# Draw all frames on a canvas
c = ROOT.TCanvas("rf101_basics", "rf101_basics", 800, 400)
c.Divide(2)
c.cd(1)
xframe.GetYaxis().SetTitleOffset(1.6)
xframe.Draw()
c.cd(2)
xframe2.GetYaxis().SetTitleOffset(1.6)
xframe2.Draw()
c.SaveAs("rf101_basics.png")
[#1] INFO:Fitting -- RooAbsPdf::fitTo(gauss_over_gauss_Int[x]) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- using CPU computation library compiled with -mavx2
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_gauss_over_gauss_Int[x]_gaussData) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
RooRealVar::mean = 1.01746 +/- 0.0300144 L(-10 - 10)
RooRealVar::sigma = 2.9787 +/- 0.0219217 L(0.1 - 10)
Date
February 2018

Definition in file rf101_basics.py.