rf603_multicpu.C

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/// \file
/// \ingroup tutorial_roofit
/// \notebook -js
///  'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #603
///
///  Setting up a multi-core parallelized unbinned maximum likelihood fit
///
/// \macro_image
/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke 
 
 
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooPolynomial.h"
#include "RooAddPdf.h"
#include "RooProdPdf.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit ;
 
 
void rf603_multicpu()
{
 
   // C r e a t e   3 D   p d f   a n d   d a t a 
   // -------------------------------------------
 
   // Create observables
   RooRealVar x("x","x",-5,5) ;
   RooRealVar y("y","y",-5,5) ;
   RooRealVar z("z","z",-5,5) ;
 
   // Create signal pdf gauss(x)*gauss(y)*gauss(z) 
   RooGaussian gx("gx","gx",x,RooConst(0),RooConst(1)) ;
   RooGaussian gy("gy","gy",y,RooConst(0),RooConst(1)) ;
   RooGaussian gz("gz","gz",z,RooConst(0),RooConst(1)) ;
   RooProdPdf sig("sig","sig",RooArgSet(gx,gy,gz)) ;
 
   // Create background pdf poly(x)*poly(y)*poly(z) 
   RooPolynomial px("px","px",x,RooArgSet(RooConst(-0.1),RooConst(0.004))) ;
   RooPolynomial py("py","py",y,RooArgSet(RooConst(0.1),RooConst(-0.004))) ;
   RooPolynomial pz("pz","pz",z) ;
   RooProdPdf bkg("bkg","bkg",RooArgSet(px,py,pz)) ;
 
   // Create composite pdf sig+bkg
   RooRealVar fsig("fsig","signal fraction",0.1,0.,1.) ;
   RooAddPdf model("model","model",RooArgList(sig,bkg),fsig) ;
 
   // Generate large dataset
   RooDataSet* data = model.generate(RooArgSet(x,y,z),200000) ;
 
 
 
   // P a r a l l e l   f i t t i n g 
   // -------------------------------
 
   // In parallel mode the likelihood calculation is split in N pieces,
   // that are calculated in parallel and added a posteriori before passing
   // it back to MINUIT.
 
   // Use four processes and time results both in wall time and CPU time
   model.fitTo(*data,NumCPU(4),Timer(kTRUE)) ;
 
 
 
   // P a r a l l e l   M C   p r o j e c t i o n s 
   // ----------------------------------------------
 
   // Construct signal, total likelihood projection on (y,z) observables and likelihood ratio
   RooAbsPdf* sigyz = sig.createProjection(x) ;
   RooAbsPdf* totyz = model.createProjection(x) ;
   RooFormulaVar llratio_func("llratio","log10(@0)-log10(@1)",RooArgList(*sigyz,*totyz)) ;
 
   // Calculate likelihood ratio for each event, define subset of events with high signal likelihood
   data->addColumn(llratio_func) ;
   RooDataSet* dataSel = (RooDataSet*) data->reduce(Cut("llratio>0.7")) ;
 
   // Make plot frame and plot data
   RooPlot* frame = x.frame(Title("Projection on X with LLratio(y,z)>0.7"),Bins(40)) ;
   dataSel->plotOn(frame) ;
 
   // Perform parallel projection using MC integration of pdf using given input dataSet. 
   // In this mode the data-weighted average of the pdf is calculated by splitting the
   // input dataset in N equal pieces and calculating in parallel the weighted average
   // one each subset. The N results of those calculations are then weighted into the
   // final result
   
   // Use four processes
   model.plotOn(frame,ProjWData(*dataSel),NumCPU(4)) ;
 
 
   new TCanvas("rf603_multicpu","rf603_multicpu",600,600) ;
   gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
 
}