rf208_convolution.C

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/// \file
/// \ingroup tutorial_roofit
/// \notebook -js
/// Addition and convolution: one-dimensional numeric convolution
///
/// pdf = landau(t) (x) gauss(t)
///
/// This tutorial requires FFT3 to be enabled.
///
/// \macro_image
/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke

#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooLandau.h"
#include "RooFFTConvPdf.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TH1.h"
using namespace RooFit;

void rf208_convolution()
{
   // S e t u p   c o m p o n e n t   p d f s
   // ---------------------------------------

   // Construct observable
   RooRealVar t("t", "t", -10, 30);

   // Construct landau(t,ml,sl) ;
   RooRealVar ml("ml", "mean landau", 5., -20, 20);
   RooRealVar sl("sl", "sigma landau", 1, 0.1, 10);
   RooLandau landau("lx", "lx", t, ml, sl);

   // Construct gauss(t,mg,sg)
   RooRealVar mg("mg", "mg", 0);
   RooRealVar sg("sg", "sg", 2, 0.1, 10);
   RooGaussian gauss("gauss", "gauss", t, mg, sg);

   // C o n s t r u c t   c o n v o l u t i o n   p d f
   // ---------------------------------------

   // Set #bins to be used for FFT sampling to 10000
   t.setBins(10000, "cache");

   // Construct landau (x) gauss
   RooFFTConvPdf lxg("lxg", "landau (X) gauss", t, landau, gauss);

   // S a m p l e ,   f i t   a n d   p l o t   c o n v o l u t e d   p d f
   // ----------------------------------------------------------------------

   // Sample 1000 events in x from gxlx
   RooDataSet *data = lxg.generate(t, 10000);

   // Fit gxlx to data
   lxg.fitTo(*data);

   // Plot data, landau pdf, landau (X) gauss pdf
   RooPlot *frame = t.frame(Title("landau (x) gauss convolution"));
   data->plotOn(frame);
   lxg.plotOn(frame);
   landau.plotOn(frame, LineStyle(kDashed));

   // Draw frame on canvas
   new TCanvas("rf208_convolution", "rf208_convolution", 600, 600);
   gPad->SetLeftMargin(0.15);
   frame->GetYaxis()->SetTitleOffset(1.4);
   frame->Draw();
}