rf307_fullpereventerrors.C

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/// \file
/// \ingroup tutorial_roofit
/// \notebook
///  'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #307
///
///  Complete example with use of full p.d.f. with per-event errors
///
///
///
/// \macro_code
/// \author 07/2008 - Wouter Verkerke 
 
 
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooGaussModel.h"
#include "RooConstVar.h"
#include "RooDecay.h"
#include "RooLandau.h"
#include "RooProdPdf.h"
#include "RooHistPdf.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TH1.h"
using namespace RooFit ;
 
 
void rf307_fullpereventerrors()
{
   // B - p h y s i c s   p d f   w i t h   p e r - e v e n t  G a u s s i a n   r e s o l u t i o n
   // ----------------------------------------------------------------------------------------------
 
   // Observables
   RooRealVar dt("dt","dt",-10,10) ;
   RooRealVar dterr("dterr","per-event error on dt",0.01,10) ;
 
   // Build a gaussian resolution model scaled by the per-event error = gauss(dt,bias,sigma*dterr)
   RooRealVar bias("bias","bias",0,-10,10) ;
   RooRealVar sigma("sigma","per-event error scale factor",1,0.1,10) ;
   RooGaussModel gm("gm1","gauss model scaled bt per-event error",dt,bias,sigma,dterr) ;
 
   // Construct decay(dt) (x) gauss1(dt|dterr)
   RooRealVar tau("tau","tau",1.548) ;
   RooDecay decay_gm("decay_gm","decay",dt,tau,gm,RooDecay::DoubleSided) ;
 
 
 
   // C o n s t r u c t   e m p i r i c a l   p d f   f o r   p e r - e v e n t   e r r o r
   // -----------------------------------------------------------------
 
   // Use landau p.d.f to get empirical distribution with long tail
   RooLandau pdfDtErr("pdfDtErr","pdfDtErr",dterr,RooConst(1),RooConst(0.25)) ;
   RooDataSet* expDataDterr = pdfDtErr.generate(dterr,10000) ;
 
   // Construct a histogram pdf to describe the shape of the dtErr distribution
   RooDataHist* expHistDterr = expDataDterr->binnedClone() ;
   RooHistPdf pdfErr("pdfErr","pdfErr",dterr,*expHistDterr) ;
 
 
   // C o n s t r u c t   c o n d i t i o n a l   p r o d u c t   d e c a y _ d m ( d t | d t e r r ) * p d f ( d t e r r )
   // ----------------------------------------------------------------------------------------------------------------------
 
   // Construct production of conditional decay_dm(dt|dterr) with empirical pdfErr(dterr)
   RooProdPdf model("model","model",pdfErr,Conditional(decay_gm,dt)) ;
 
   // (Alternatively you could also use the landau shape pdfDtErr)
   //RooProdPdf model("model","model",pdfDtErr,Conditional(decay_gm,dt)) ;
 
 
 
   // S a m p l e,   f i t   a n d   p l o t   p r o d u c t   m o d e l 
   // ------------------------------------------------------------------
 
   // Specify external dataset with dterr values to use model_dm as conditional p.d.f.
   RooDataSet* data = model.generate(RooArgSet(dt,dterr),10000) ;
 
 
 
   // F i t   c o n d i t i o n a l   d e c a y _ d m ( d t | d t e r r )
   // ---------------------------------------------------------------------
 
   // Specify dterr as conditional observable
   model.fitTo(*data) ;
 
 
 
   // P l o t   c o n d i t i o n a l   d e c a y _ d m ( d t | d t e r r )
   // ---------------------------------------------------------------------
 
 
   // Make two-dimensional plot of conditional p.d.f in (dt,dterr)
   TH1* hh_model = model.createHistogram("hh_model",dt,Binning(50),YVar(dterr,Binning(50))) ;
   hh_model->SetLineColor(kBlue) ;
 
 
   // Make projection of data an dt
   RooPlot* frame = dt.frame(Title("Projection of model(dt|dterr) on dt")) ;
   data->plotOn(frame) ;
   model.plotOn(frame) ;
 
 
   // Draw all frames on canvas
   TCanvas* c = new TCanvas("rf307_fullpereventerrors","rf307_fullperventerrors",800, 400);
   c->Divide(2) ;
   c->cd(1) ; gPad->SetLeftMargin(0.20) ; hh_model->GetZaxis()->SetTitleOffset(2.5) ; hh_model->Draw("surf") ;
   c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
 
 
 
}