/////////////////////////////////////////////////////////////////////////
//
// 'SPECIAL PDFS' RooFit tutorial macro #701
// 
// Unbinned maximum likelihood fit of an efficiency eff(x) function to 
// a dataset D(x,cut), where cut is a category encoding a selection, of which
// the efficiency as function of x should be described by eff(x)
//
// 07/2008 - Wouter Verkerke 
//
/////////////////////////////////////////////////////////////////////////

#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooFormulaVar.h"
#include "RooProdPdf.h"
#include "RooEfficiency.h"
#include "RooPolynomial.h"
#include "RooCategory.h"
#include "TCanvas.h"
#include "TLegend.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit ;


void rf701_efficiencyfit()
{
  // C o n s t r u c t   e f f i c i e n c y   f u n c t i o n   e ( x ) 
  // -------------------------------------------------------------------

  // Declare variables x,mean,sigma with associated name, title, initial value and allowed range
  RooRealVar x("x","x",-10,10) ;

  // Efficiency function eff(x;a,b) 
  RooRealVar a("a","a",0.4,0,1) ;
  RooRealVar b("b","b",5) ;
  RooRealVar c("c","c",-1,-10,10) ;
  RooFormulaVar effFunc("effFunc","(1-a)+a*cos((x-c)/b)",RooArgList(a,b,c,x)) ;



  // C o n s t r u c t   c o n d i t i o n a l    e f f i c i e n c y   p d f   E ( c u t | x ) 
  // ------------------------------------------------------------------------------------------

  // Acceptance state cut (1 or 0)
  RooCategory cut("cut","cutr") ;
  cut.defineType("accept",1) ;
  cut.defineType("reject",0) ;

  // Construct efficiency p.d.f eff(cut|x)
  RooEfficiency effPdf("effPdf","effPdf",effFunc,cut,"accept") ;

  // G e n e r a t e   d a t a   ( x ,   c u t )   f r o m   a   t o y   m o d e l 
  // -----------------------------------------------------------------------------

  // Construct global shape p.d.f shape(x) and product model(x,cut) = eff(cut|x)*shape(x) 
  // (These are _only_ needed to generate some toy MC here to be used later)
  RooPolynomial shapePdf("shapePdf","shapePdf",x,RooConst(-0.095)) ;
  RooProdPdf model("model","model",shapePdf,Conditional(effPdf,cut)) ;

  // Generate some toy data from model
  RooDataSet* data = model.generate(RooArgSet(x,cut),10000) ;

  // F i t   c o n d i t i o n a l   e f f i c i e n c y   p d f   t o   d a t a 
  // --------------------------------------------------------------------------

  // Fit conditional efficiency p.d.f to data
  effPdf.fitTo(*data,ConditionalObservables(x)) ;



  // P l o t   f i t t e d ,   d a t a   e f f i c i e n c y  
  // --------------------------------------------------------

  // Plot distribution of all events and accepted fraction of events on frame
  RooPlot* frame_1 = NULL;

  frame1 = x.frame(Bins(20),Title("Data (all, accepted)")) ;
  RooPlot *frame_a = frame1->emptyClone("x");
  data->plotOn(frame_a) ;

  RooPlot *frame_b = frame1->emptyClone("x");
  data->plotOn(frame_b,Cut("cut==cut::accept"),MarkerColor(kRed),LineColor(kRed)) ;

  TLegend* tleg = new TLegend(0.6,0.6,0.8,0.85); 
  tleg->AddEntry(frame_a,"All Data", "lp"); 
  tleg->AddEntry(frame_b,"Accepted Data", "lp"); 

  // Draw all frames on a canvas
  TCanvas* ca = new TCanvas("rf701_efficiency","rf701_efficiency",800,400) ;
  ca->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame_a->Draw() ;frame_b->Draw("same") ;tleg->Draw("same");
  
 
}