rf101_basics.C File Reference

Detailed Description

Basic functionality: fitting, plotting, toy data generation on one-dimensional PDFs.

pdf = gauss(x,m,s)

 
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "TAxis.h"
using namespace RooFit;
 
void rf101_basics()
{
   // S e t u p   m o d e l
   // ---------------------
 
   // Declare variables x,mean,sigma with associated name, title, initial value and allowed range
   RooRealVar x("x", "x", -10, 10);
   RooRealVar mean("mean", "mean of gaussian", 1, -10, 10);
   RooRealVar sigma("sigma", "width of gaussian", 1, 0.1, 10);
 
   // Build gaussian pdf in terms of x,mean and sigma
   RooGaussian gauss("gauss", "gaussian PDF", x, mean, sigma);
 
   // Construct plot frame in 'x'
   RooPlot *xframe = x.frame(Title("Gaussian pdf."));
 
   // P l o t   m o d e l   a n d   c h a n g e   p a r a m e t e r   v a l u e s
   // ---------------------------------------------------------------------------
 
   // 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(kRed));
 
   // G e n e r a t e   e v e n t s
   // -----------------------------
 
   // Generate a dataset of 1000 events in x from gauss
   std::unique_ptr<RooDataSet> data{gauss.generate(x, 10000)};
 
   // Make a second plot frame in x and draw both the
   // data and the pdf in the frame
   RooPlot *xframe2 = x.frame(Title("Gaussian pdf with data"));
   data->plotOn(xframe2);
   gauss.plotOn(xframe2);
 
   // F i t   m o d e l   t o   d a t a
   // -----------------------------
 
   // 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
   TCanvas *c = new TCanvas("rf101_basics", "rf101_basics", 800, 400); c->Divide(2);
   c->cd(1);
   gPad->SetLeftMargin(0.15);
   xframe->GetYaxis()->SetTitleOffset(1.6);
   xframe->Draw();
   c->cd(2);
   gPad->SetLeftMargin(0.15);
   xframe2->GetYaxis()->SetTitleOffset(1.6);
   xframe2->Draw();
}

[#1] INFO:Fitting -- RooAbsPdf::fitTo(gauss_over_gauss_Int[x]) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- using generic CPU library compiled with no vectorizations
[#1] INFO:Fitting -- Creation of NLL object took 772.513 μs
[#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 -- [fitFCN] No discrete parameters, performing continuous minimization only
[#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

July 2008

Author

Wouter Verkerke

Definition in file rf101_basics.C.