///////////////////////////////////////////////////////////////////////// // // 'ADDITION AND CONVOLUTION' RooFit tutorial macro #208 // // One-dimensional numeric convolution // (require ROOT to be compiled with --enable-fftw3) // // pdf = landau(t) (x) gauss(t) // // // 07/2008 - Wouter Verkerke // ///////////////////////////////////////////////////////////////////////// #ifndef __CINT__ #include "RooGlobalFunc.h" #endif #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() ; }