[u/mrichter/AliRoot.git] / ITS / AliITSPident.cxx

//////////////////////////////////////////////////////////////////////////
// USER Class for PID in the ITS                                          //
//The PID is based on the likelihood of all the four ITS' layers,         //
//without using the truncated mean for the dE/dx. The response            //
//functions for each layer are convoluted Landau-Gaussian functions.      // 
// Origin: Elena Bruna bruna@to.infn.it,, Massimo Masera masera@to.infn.it//
//////////////////////////////////////////////////////////////////////////
#include "AliESDtrack.h"
#include "AliITSPident.h"
#include "AliITSSteerPid.h"
#include <Riostream.h>

ClassImp(AliITSPident)
  //_______________________________________________________________________
AliITSPident::AliITSPident():
fMom(0),
fPBayesp(0),
fPBayesk(0),
fPBayespi(0),
fPPriorip(0),
fPPriorik(0),
fPPrioripi(0),
fPPriorie(0)
{
  // default constructor
  for (Int_t i=0;i<8;i++){
    fCondFunProLay[i]=0;
    fCondFunKLay[i]=0;
    fCondFunPiLay[i]=0;
  }
  for (Int_t i=0;i<4;i++)fNcls[i]=0;
}
//_______________________________________________________________________
AliITSPident::~AliITSPident(){
  // destructor
}
//______________________________________________________________________
AliITSPident::AliITSPident(const AliITSPident &ob) :TObject(ob),
fMom(ob.fMom),
fPBayesp(ob.fPBayesp),
fPBayesk(ob.fPBayesk),
fPBayespi(ob.fPBayespi),
fPPriorip(ob.fPPriorip),
fPPriorik(ob.fPPriorik),
fPPrioripi(ob.fPPrioripi),
fPPriorie(ob.fPPriorie)
{
  // Copy constructor
}

//______________________________________________________________________
AliITSPident& AliITSPident::operator=(const AliITSPident&  ob){
  // Assignment operator
  this->~AliITSPident();
  new(this) AliITSPident(ob);
  return *this;
}


//_______________________________________________________________________
AliITSPident::AliITSPident(Double_t mom,AliITSSteerPid *sp,Float_t *Qlay,Float_t *nlay,Float_t priorip,Float_t priorik,Float_t prioripi,Float_t priorie):
fMom(mom),
fPBayesp(0),
fPBayesk(0),
fPBayespi(0),
fPPriorip(priorip),
fPPriorik(priorik),
fPPrioripi(prioripi),
fPPriorie(priorie)
{
  //
  for (Int_t i=0;i<8;i++){
    fCondFunProLay[i]=-1;
    fCondFunKLay[i]=-1;
    fCondFunPiLay[i]=-1;
  }
  for(Int_t la=0;la<4;la++){//loop on layers
    Double_t parp[3];Double_t park[3];Double_t parpi[3];
    fNcls[la]=0;
    Double_t range[6];
    range[0]=0.3*parp[1];
    range[1]=2.*parp[1];

    range[2]=0.3*park[1];
    range[3]=2.*park[1];
    
    range[4]=0.3*parpi[1];
    range[5]=2.*parpi[1];
    Int_t layer=la+2;
    for(Int_t ii=0;ii<8;ii++){
      if(nlay[ii]==layer){
	fNcls[la]++;
	if(Qlay[ii]>0){
	  sp->GetParFitLayer(la,fMom,parp,park,parpi);
	  CookFunItsLay(ii,0,parp,Qlay[ii],fMom,range[0],range[1],"fPro");
	  CookFunItsLay(ii,1,park,Qlay[ii],fMom,range[2],range[3],"fKao");
	  CookFunItsLay(ii,2,parpi,Qlay[ii],fMom,range[4],range[5],"fPi");
	}  
      }
    }
    
  }
 
  Float_t prior[4];Double_t condFun[8][3];

  prior[0]=fPPriorip;
  prior[1]=fPPriorik;
  prior[2]=fPPrioripi;
  prior[3]=fPPriorie;
  for(Int_t la=0;la<8;la++){
    condFun[la][0]= fCondFunProLay[la];
    condFun[la][1]= fCondFunKLay[la]; 
    condFun[la][2]= fCondFunPiLay[la];
    
  }

  fPBayesp=CookCombinedBayes(condFun,prior,0);
  fPBayesk=CookCombinedBayes(condFun,prior,1); 
  fPBayespi=CookCombinedBayes(condFun,prior,2); 
}

//__________________________________________________________________________________________
AliITSPident::AliITSPident(AliESDtrack *track,AliITSSteerPid *sp,Float_t *Qlay,Float_t *nlay,Float_t priorip,Float_t priorik,Float_t prioripi,Float_t priorie):
fMom(0),
fPBayesp(0),
fPBayesk(0),
fPBayespi(0),
fPPriorip(priorip),
fPPriorik(priorik),
fPPrioripi(prioripi),
fPPriorie(priorie)
{
  //
  for (Int_t i=0;i<8;i++){
    fCondFunProLay[i]=-1;
    fCondFunKLay[i]=-1;
    fCondFunPiLay[i]=-1;
  }
  Double_t xr;
  Double_t par[5];
  track->GetExternalParameters(xr,par);
  if (par[4]!=0) {
    Float_t lamb=TMath::ATan(par[3]);
    fMom=1/(TMath::Abs(par[4])*TMath::Cos(lamb));
  }
 
 
  for(Int_t la=0;la<4;la++){//loop on layers
    Double_t parp[3];Double_t park[3];Double_t parpi[3];
    fNcls[la]=0;
    Double_t range[8];
    range[0]=0.3*parp[1];
    range[1]=2.*parp[1];

    range[2]=0.3*park[1];
    range[3]=2.*park[1];

    range[4]=0.3*parpi[1];
    range[5]=2.*parpi[1];

    Int_t layer=la+2;
    for(Int_t ii=0;ii<8;ii++){
      if(nlay[ii]==layer){
	fNcls[la]++;
	if(Qlay[ii]>0){
	  sp->GetParFitLayer(la,fMom,parp,park,parpi);
	  CookFunItsLay(ii,0,parp,Qlay[ii],fMom,range[0],range[1],"fPro");
	  CookFunItsLay(ii,1,park,Qlay[ii],fMom,range[2],range[3],"fKao");
	  CookFunItsLay(ii,2,parpi,Qlay[ii],fMom,range[4],range[5],"fPi");
	}  
      }
    }
    
  }

  Float_t prior[4];Double_t condFun[8][3];

  prior[0]=fPPriorip;
  prior[1]=fPPriorik;
  prior[2]=fPPrioripi;
  prior[3]=fPPriorie;
  for(Int_t la=0;la<8;la++){
    condFun[la][0]= fCondFunProLay[la];
    condFun[la][1]= fCondFunKLay[la]; 
    condFun[la][2]= fCondFunPiLay[la];
    
  }

  fPBayesp=CookCombinedBayes(condFun,prior,0);
  fPBayesk=CookCombinedBayes(condFun,prior,1); 
  fPBayespi=CookCombinedBayes(condFun,prior,2); 

}
//_______________________________________________________________________
void AliITSPident::GetNclsPerLayer(Int_t *ncls) const{
  //number of clusters for each layer (sdd1,sdd2,ssd1,ssd2)
 for(Int_t la=0;la<4;la++){
   ncls[la]=fNcls[la];
 }

}//_______________________________________________________________________
Double_t AliITSPident::GetProdCondFunPro() const {
  //Product of conditional probability functions for protons
    Double_t rv=1.;
    for(Int_t i=0;i<8;i++){
      Double_t fun=GetCondFunPro(i);
      if(fun>=0)rv*=fun;
    }
    return rv;
}//_______________________________________________________________________
Double_t AliITSPident::GetProdCondFunK() const {
  //Product of conditional probability functions for kaons
    Double_t rv=1.; 
    for(Int_t i=0;i<8;i++){
      Double_t fun=GetCondFunK(i);
      if(fun>=0)rv*=fun;
    }
    return rv;
}
//_______________________________________________________________________
Double_t AliITSPident::GetProdCondFunPi() const {
  //Product of conditional probability functions for pions
    Double_t rv=1.; 
    for(Int_t i=0;i<8;i++){
      Double_t fun=GetCondFunPi(i);
      if(fun>=0)rv*=fun;
    }
    return rv;
}
//_______________________________________________________________________
void AliITSPident::PrintParameters() const{
 //print parameters
  cout<<"___________________________\n";
  cout<<"Track Local Momentum = "<<"  "<<fMom<<endl;
}

//_______________________________________________________________________
Double_t AliITSPident::Langaufun(Double_t *x, Double_t *par) {

  //Fit parameters:
  //par[0]=Width (scale) parameter of Landau density
  //par[1]=Most Probable (MP, location) parameter of Landau density
  //par[2]=Total area (integral -inf to inf, normalization constant)
  //par[3]=Width (sigma) of convoluted Gaussian function
  //
  //In the Landau distribution (represented by the CERNLIB approximation), 
  //the maximum is located at x=-0.22278298 with the location parameter=0.
  //This shift is corrected within this function, so that the actual
  //maximum is identical to the MP parameter.

  // Numeric constants
  Double_t invsq2pi = 0.3989422804014;   // (2 pi)^(-1/2)
  Double_t mpshift  = -0.22278298;       // Landau maximum location

  // Control constants
  Double_t np = 100.0;      // number of convolution steps
  Double_t sc =   5.0;      // convolution extends to +-sc Gaussian sigmas

  // Variables
  Double_t xx;
  Double_t mpc;
  Double_t fland;
  Double_t sum = 0.0;
  Double_t xlow,xupp;
  Double_t step;
  Double_t i;


  // MP shift correction
  mpc = par[1] - mpshift * par[0]; 

  // Range of convolution integral
  xlow = x[0] - sc * par[3];
  xupp = x[0] + sc * par[3];

  step = (xupp-xlow) / np;

  // Convolution integral of Landau and Gaussian by sum
  for(i=1.0; i<=np/2; i++) {
    xx = xlow + (i-.5) * step;
    fland = TMath::Landau(xx,mpc,par[0]) / par[0];
    sum += fland * TMath::Gaus(x[0],xx,par[3]);

    xx = xupp - (i-.5) * step;
    fland = TMath::Landau(xx,mpc,par[0]) / par[0];
    sum += fland * TMath::Gaus(x[0],xx,par[3]);
  }

  return (par[2] * step * sum * invsq2pi / par[3]);
}

//_______________________________________________________________________
Double_t AliITSPident::Langaufun2(Double_t *x, Double_t *par){
  //normalized langaufun
  return 1/par[4]*Langaufun(x,par);
}
//_______________________________________________________________________
Double_t AliITSPident::Langaufunnorm(Double_t *x, Double_t *par){
   //Fit parameters:
  //par[0]=Width (scale) parameter of Landau density
  //par[1]=Most Probable (MP, location) parameter of Landau density
  
  //par[2]=Width (sigma) of convoluted Gaussian function
  //
  //In the Landau distribution (represented by the CERNLIB approximation), 
  //the maximum is located at x=-0.22278298 with the location parameter=0.
  //This shift is corrected within this function, so that the actual
  //maximum is identical to the MP parameter.

  // Numeric constants
  Double_t invsq2pi = 0.3989422804014;   // (2 pi)^(-1/2)
  Double_t mpshift  = -0.22278298;       // Landau maximum location

  // Control constants
  Double_t np = 100.0;      // number of convolution steps
  Double_t sc =   5.0;      // convolution extends to +-sc Gaussian sigmas

  // Variables
  Double_t xx;
  Double_t mpc;
  Double_t fland;
  Double_t sum = 0.0;
  Double_t xlow,xupp;
  Double_t step;
  Double_t i;


  // MP shift correction
  mpc = par[1] - mpshift * par[0]; 

  // Range of convolution integral
  xlow = x[0] - sc * par[2];
  xupp = x[0] + sc * par[2];

  step = (xupp-xlow) / np;

  // Convolution integral of Landau and Gaussian by sum
  for(i=1.0; i<=np/2; i++) {
    xx = xlow + (i-.5) * step;
    fland = TMath::Landau(xx,mpc,par[0]) / par[0];
    sum += fland * TMath::Gaus(x[0],xx,par[2]);

    xx = xupp - (i-.5) * step;
    fland = TMath::Landau(xx,mpc,par[0]) / par[0];
    sum += fland * TMath::Gaus(x[0],xx,par[2]);
  }

  return (step * sum * invsq2pi / par[2]);
}
//_______________________________________________________________________
Double_t AliITSPident::Gaus2(Double_t *x, Double_t *par){
  //normalized gaussian function
  return 1/(sqrt(2*TMath::Pi())*par[1])*TMath::Gaus(x[0],par[0],par[1]);
}
//_______________________________________________________________________
void AliITSPident::CookFunItsLay(Int_t lay,Int_t opt,Double_t *par,Double_t dedx,Double_t mom,Double_t rangei,Double_t rangef,TString comment){
  //it gives the response functions
 TF1 funLay(comment,Langaufunnorm,rangei,rangef,3);
 funLay.SetParameters(par);
 Double_t condFun=funLay.Eval(dedx);
  if(opt==0){
    fCondFunProLay[lay]=condFun;
    if(mom<0.4 && dedx<100)fCondFunProLay[lay]=0.00001;
    if(mom<0.4 &&dedx<50)fCondFunProLay[lay]=0.0000001;
  }
  if(opt==1){
    fCondFunKLay[lay]=condFun; 
    if(mom<0.25 && dedx<100)fCondFunKLay[lay]=0.00001;
    if(mom<0.4 &&dedx<30)fCondFunKLay[lay]=0.0000001;   
  }
  if(opt==2){
    fCondFunPiLay[lay]=condFun;
    if(mom<0.6 &&dedx<20)fCondFunPiLay[lay]=0.001;
  }

}
//_______________________________________________________________________
Float_t AliITSPident::CookCombinedBayes(Double_t condfun[][3],Float_t *prior,Int_t part)const {
  //Bayesian combined PID in the ITS
  Int_t test=0; 
  Float_t bayes;
  Float_t pprior[4]={0,0,0,0};
  for(Int_t j=0;j<4;j++)pprior[j]=prior[j];
  pprior[2]+=pprior[3];//prior for electrons summed to priors for pions
  for(Int_t i=0;i<8;i++){//layer
    if (condfun[i][0]>0 || condfun[i][1]>0 ||condfun[i][2]>0) test++; 
  }
  if(test>0){
    if ((pprior[0]!=0 || pprior[1]!=0 ||pprior[2]!=0)&&CookSum(condfun,pprior)!=0){

      bayes=pprior[part]*CookProd(condfun,part)*1/CookSum(condfun,pprior);
  
    }
    else bayes=-100;
  }
  else bayes=-100;
  return bayes;
}
//_______________________________________________________________________
Float_t AliITSPident::CookProd(Double_t condfun[][3],Int_t part)const{
  //
  Float_t p=1;
  for(Int_t lay=0;lay<8;lay++){
    if(condfun[lay][part]>=0)p=p*condfun[lay][part];
  }

  return p;
}
//_______________________________________________________________________
Float_t AliITSPident::CookSum(Double_t condfun[][3],Float_t *prior)const{
  //
  Float_t sum=0;
  Float_t pprior[4]={0,0,0,0};
  for(Int_t j=0;j<4;j++)pprior[j]=prior[j];
  pprior[2]+=pprior[3];//prior for electrons summed to priors for pions
  for(Int_t i=0;i<3;i++){//sum over the particles--electrons excluded
    sum+=pprior[i]*CookProd(condfun,i);
  }
  return sum;

}
Commit	Line	Data
e62c1aea	1	//////////////////////////////////////////////////////////////////////////
	2	// USER Class for PID in the ITS //
	3	//The PID is based on the likelihood of all the four ITS' layers, //
	4	//without using the truncated mean for the dE/dx. The response //
	5	//functions for each layer are convoluted Landau-Gaussian functions. //
	6	// Origin: Elena Bruna bruna@to.infn.it,, Massimo Masera masera@to.infn.it//
	7	//////////////////////////////////////////////////////////////////////////
e96abd41	8	#include "AliESDtrack.h"
e62c1aea	9	#include "AliITSPident.h"
b68da4a2	10	#include "AliITSSteerPid.h"
b68da4a2	11	#include <Riostream.h>
e62c1aea	12
	13	ClassImp(AliITSPident)
	14	//_______________________________________________________________________
94631b2f	15	AliITSPident::AliITSPident():
94631b2f	16	fMom(0),
94631b2f	17	fPBayesp(0),
	18	fPBayesk(0),
	19	fPBayespi(0),
	20	fPPriorip(0),
	21	fPPriorik(0),
	22	fPPrioripi(0),
b68da4a2	23	fPPriorie(0)
94631b2f	24	{
e62c1aea	25	// default constructor
b68da4a2	26	for (Int_t i=0;i<8;i++){
e62c1aea	27	fCondFunProLay[i]=0;
	28	fCondFunKLay[i]=0;
	29	fCondFunPiLay[i]=0;
	30	}
b68da4a2	31	for (Int_t i=0;i<4;i++)fNcls[i]=0;
e62c1aea	32	}
	33	//_______________________________________________________________________
	34	AliITSPident::~AliITSPident(){
	35	// destructor
	36	}
	37	//______________________________________________________________________
94631b2f	38	AliITSPident::AliITSPident(const AliITSPident &ob) :TObject(ob),
94631b2f	39	fMom(ob.fMom),
94631b2f	40	fPBayesp(ob.fPBayesp),
	41	fPBayesk(ob.fPBayesk),
	42	fPBayespi(ob.fPBayespi),
	43	fPPriorip(ob.fPPriorip),
	44	fPPriorik(ob.fPPriorik),
	45	fPPrioripi(ob.fPPrioripi),
b68da4a2	46	fPPriorie(ob.fPPriorie)
94631b2f	47	{
e62c1aea	48	// Copy constructor
e62c1aea	49	}
	50
	51	//______________________________________________________________________
94631b2f	52	AliITSPident& AliITSPident::operator=(const AliITSPident& ob){
e62c1aea	53	// Assignment operator
94631b2f	54	this->~AliITSPident();
94631b2f	55	new(this) AliITSPident(ob);
e62c1aea	56	return *this;
	57	}
	58
	59
	60	//_______________________________________________________________________
e96abd41	61	AliITSPident::AliITSPident(Double_t mom,AliITSSteerPid sp,Float_t Qlay,Float_t *nlay,Float_t priorip,Float_t priorik,Float_t prioripi,Float_t priorie):
94631b2f	62	fMom(mom),
94631b2f	63	fPBayesp(0),
	64	fPBayesk(0),
	65	fPBayespi(0),
	66	fPPriorip(priorip),
	67	fPPriorik(priorik),
	68	fPPrioripi(prioripi),
b68da4a2	69	fPPriorie(priorie)
	70	{
	71	//
	72	for (Int_t i=0;i<8;i++){
	73	fCondFunProLay[i]=-1;
	74	fCondFunKLay[i]=-1;
	75	fCondFunPiLay[i]=-1;
	76	}
e62c1aea	77	for(Int_t la=0;la<4;la++){//loop on layers
e62c1aea	78	Double_t parp[3];Double_t park[3];Double_t parpi[3];
b68da4a2	79	fNcls[la]=0;
e62c1aea	80	Double_t range[6];
	81	range[0]=0.3*parp[1];
	82	range[1]=2.*parp[1];
	83
	84	range[2]=0.3*park[1];
	85	range[3]=2.*park[1];
	86
	87	range[4]=0.3*parpi[1];
	88	range[5]=2.*parpi[1];
b68da4a2	89	Int_t layer=la+2;
	90	for(Int_t ii=0;ii<8;ii++){
	91	if(nlay[ii]==layer){
	92	fNcls[la]++;
	93	if(Qlay[ii]>0){
	94	sp->GetParFitLayer(la,fMom,parp,park,parpi);
	95	CookFunItsLay(ii,0,parp,Qlay[ii],fMom,range[0],range[1],"fPro");
	96	CookFunItsLay(ii,1,park,Qlay[ii],fMom,range[2],range[3],"fKao");
	97	CookFunItsLay(ii,2,parpi,Qlay[ii],fMom,range[4],range[5],"fPi");
	98	}
	99	}
	100	}
e62c1aea	101
e62c1aea	102	}
b68da4a2	103
b68da4a2	104	Float_t prior[4];Double_t condFun[8][3];
e62c1aea	105
	106	prior[0]=fPPriorip;
	107	prior[1]=fPPriorik;
	108	prior[2]=fPPrioripi;
	109	prior[3]=fPPriorie;
b68da4a2	110	for(Int_t la=0;la<8;la++){
e62c1aea	111	condFun[la][0]= fCondFunProLay[la];
	112	condFun[la][1]= fCondFunKLay[la];
	113	condFun[la][2]= fCondFunPiLay[la];
	114
	115	}
	116
e62c1aea	117	fPBayesp=CookCombinedBayes(condFun,prior,0);
e62c1aea	118	fPBayesk=CookCombinedBayes(condFun,prior,1);
e62c1aea	119	fPBayespi=CookCombinedBayes(condFun,prior,2);
b68da4a2	120	}
e62c1aea	121
b68da4a2	122	//__________________________________________________________________________________________
e96abd41	123	AliITSPident::AliITSPident(AliESDtrack track,AliITSSteerPid sp,Float_t Qlay,Float_t nlay,Float_t priorip,Float_t priorik,Float_t prioripi,Float_t priorie):
94631b2f	124	fMom(0),
94631b2f	125	fPBayesp(0),
	126	fPBayesk(0),
	127	fPBayespi(0),
	128	fPPriorip(priorip),
	129	fPPriorik(priorik),
	130	fPPrioripi(prioripi),
b68da4a2	131	fPPriorie(priorie)
94631b2f	132	{
e62c1aea	133	//
b68da4a2	134	for (Int_t i=0;i<8;i++){
	135	fCondFunProLay[i]=-1;
	136	fCondFunKLay[i]=-1;
	137	fCondFunPiLay[i]=-1;
	138	}
e62c1aea	139	Double_t xr;
e62c1aea	140	Double_t par[5];
e96abd41	141	track->GetExternalParameters(xr,par);
e62c1aea	142	if (par[4]!=0) {
	143	Float_t lamb=TMath::ATan(par[3]);
	144	fMom=1/(TMath::Abs(par[4])*TMath::Cos(lamb));
	145	}
	146
e62c1aea	147
	148	for(Int_t la=0;la<4;la++){//loop on layers
	149	Double_t parp[3];Double_t park[3];Double_t parpi[3];
b68da4a2	150	fNcls[la]=0;
e62c1aea	151	Double_t range[8];
	152	range[0]=0.3*parp[1];
	153	range[1]=2.*parp[1];
	154
	155	range[2]=0.3*park[1];
	156	range[3]=2.*park[1];
b68da4a2	157
e62c1aea	158	range[4]=0.3*parpi[1];
e62c1aea	159	range[5]=2.*parpi[1];
b68da4a2	160
	161	Int_t layer=la+2;
	162	for(Int_t ii=0;ii<8;ii++){
	163	if(nlay[ii]==layer){
	164	fNcls[la]++;
	165	if(Qlay[ii]>0){
	166	sp->GetParFitLayer(la,fMom,parp,park,parpi);
	167	CookFunItsLay(ii,0,parp,Qlay[ii],fMom,range[0],range[1],"fPro");
	168	CookFunItsLay(ii,1,park,Qlay[ii],fMom,range[2],range[3],"fKao");
	169	CookFunItsLay(ii,2,parpi,Qlay[ii],fMom,range[4],range[5],"fPi");
	170	}
	171	}
	172	}
	173
e62c1aea	174	}
e62c1aea	175
b68da4a2	176	Float_t prior[4];Double_t condFun[8][3];
e62c1aea	177
	178	prior[0]=fPPriorip;
	179	prior[1]=fPPriorik;
	180	prior[2]=fPPrioripi;
	181	prior[3]=fPPriorie;
b68da4a2	182	for(Int_t la=0;la<8;la++){
e62c1aea	183	condFun[la][0]= fCondFunProLay[la];
	184	condFun[la][1]= fCondFunKLay[la];
	185	condFun[la][2]= fCondFunPiLay[la];
	186
	187	}
	188
e62c1aea	189	fPBayesp=CookCombinedBayes(condFun,prior,0);
e62c1aea	190	fPBayesk=CookCombinedBayes(condFun,prior,1);
e62c1aea	191	fPBayespi=CookCombinedBayes(condFun,prior,2);
e62c1aea	192
	193	}
	194	//_______________________________________________________________________
b68da4a2	195	void AliITSPident::GetNclsPerLayer(Int_t *ncls) const{
	196	//number of clusters for each layer (sdd1,sdd2,ssd1,ssd2)
	197	for(Int_t la=0;la<4;la++){
	198	ncls[la]=fNcls[la];
	199	}
	200
	201	}//_______________________________________________________________________
	202	Double_t AliITSPident::GetProdCondFunPro() const {
	203	//Product of conditional probability functions for protons
	204	Double_t rv=1.;
	205	for(Int_t i=0;i<8;i++){
	206	Double_t fun=GetCondFunPro(i);
	207	if(fun>=0)rv*=fun;
	208	}
	209	return rv;
	210	}//_______________________________________________________________________
	211	Double_t AliITSPident::GetProdCondFunK() const {
	212	//Product of conditional probability functions for kaons
	213	Double_t rv=1.;
	214	for(Int_t i=0;i<8;i++){
	215	Double_t fun=GetCondFunK(i);
	216	if(fun>=0)rv*=fun;
	217	}
	218	return rv;
	219	}
	220	//_______________________________________________________________________
	221	Double_t AliITSPident::GetProdCondFunPi() const {
	222	//Product of conditional probability functions for pions
	223	Double_t rv=1.;
	224	for(Int_t i=0;i<8;i++){
	225	Double_t fun=GetCondFunPi(i);
	226	if(fun>=0)rv*=fun;
	227	}
	228	return rv;
	229	}
	230	//_______________________________________________________________________
e62c1aea	231	void AliITSPident::PrintParameters() const{
	232	//print parameters
	233	cout<<"___________________________\n";
	234	cout<<"Track Local Momentum = "<<" "<<fMom<<endl;
e62c1aea	235	}
	236
	237	//_______________________________________________________________________
	238	Double_t AliITSPident::Langaufun(Double_t x, Double_t par) {
	239
	240	//Fit parameters:
	241	//par[0]=Width (scale) parameter of Landau density
	242	//par[1]=Most Probable (MP, location) parameter of Landau density
	243	//par[2]=Total area (integral -inf to inf, normalization constant)
	244	//par[3]=Width (sigma) of convoluted Gaussian function
	245	//
	246	//In the Landau distribution (represented by the CERNLIB approximation),
	247	//the maximum is located at x=-0.22278298 with the location parameter=0.
	248	//This shift is corrected within this function, so that the actual
	249	//maximum is identical to the MP parameter.
	250
	251	// Numeric constants
	252	Double_t invsq2pi = 0.3989422804014; // (2 pi)^(-1/2)
	253	Double_t mpshift = -0.22278298; // Landau maximum location
	254
	255	// Control constants
	256	Double_t np = 100.0; // number of convolution steps
	257	Double_t sc = 5.0; // convolution extends to +-sc Gaussian sigmas
	258
	259	// Variables
	260	Double_t xx;
	261	Double_t mpc;
	262	Double_t fland;
	263	Double_t sum = 0.0;
	264	Double_t xlow,xupp;
	265	Double_t step;
	266	Double_t i;
	267
	268
	269	// MP shift correction
	270	mpc = par[1] - mpshift * par[0];
	271
	272	// Range of convolution integral
	273	xlow = x[0] - sc * par[3];
	274	xupp = x[0] + sc * par[3];
	275
	276	step = (xupp-xlow) / np;
	277
	278	// Convolution integral of Landau and Gaussian by sum
	279	for(i=1.0; i<=np/2; i++) {
	280	xx = xlow + (i-.5) * step;
	281	fland = TMath::Landau(xx,mpc,par[0]) / par[0];
	282	sum += fland * TMath::Gaus(x[0],xx,par[3]);
	283
	284	xx = xupp - (i-.5) * step;
	285	fland = TMath::Landau(xx,mpc,par[0]) / par[0];
	286	sum += fland * TMath::Gaus(x[0],xx,par[3]);
	287	}
	288
	289	return (par[2] * step * sum * invsq2pi / par[3]);
	290	}
	291
	292	//_______________________________________________________________________
	293	Double_t AliITSPident::Langaufun2(Double_t x, Double_t par){
	294	//normalized langaufun
	295	return 1/par[4]*Langaufun(x,par);
	296	}
	297	//_______________________________________________________________________
	298	Double_t AliITSPident::Langaufunnorm(Double_t x, Double_t par){
299	//Fit parameters:
300	//par[0]=Width (scale) parameter of Landau density
301	//par[1]=Most Probable (MP, location) parameter of Landau density
302
303	//par[2]=Width (sigma) of convoluted Gaussian function
304	//
305	//In the Landau distribution (represented by the CERNLIB approximation),
306	//the maximum is located at x=-0.22278298 with the location parameter=0.
307	//This shift is corrected within this function, so that the actual
308	//maximum is identical to the MP parameter.
309
310	// Numeric constants
311	Double_t invsq2pi = 0.3989422804014; // (2 pi)^(-1/2)
312	Double_t mpshift = -0.22278298; // Landau maximum location
313
314	// Control constants
315	Double_t np = 100.0; // number of convolution steps
316	Double_t sc = 5.0; // convolution extends to +-sc Gaussian sigmas
317
318	// Variables
319	Double_t xx;
320	Double_t mpc;
321	Double_t fland;
322	Double_t sum = 0.0;
323	Double_t xlow,xupp;
324	Double_t step;
325	Double_t i;
326
327
328	// MP shift correction
329	mpc = par[1] - mpshift * par[0];
330
331	// Range of convolution integral
332	xlow = x[0] - sc * par[2];
333	xupp = x[0] + sc * par[2];
334
335	step = (xupp-xlow) / np;
336
337	// Convolution integral of Landau and Gaussian by sum
338	for(i=1.0; i<=np/2; i++) {
339	xx = xlow + (i-.5) * step;
340	fland = TMath::Landau(xx,mpc,par[0]) / par[0];
341	sum += fland * TMath::Gaus(x[0],xx,par[2]);
342
343	xx = xupp - (i-.5) * step;
344	fland = TMath::Landau(xx,mpc,par[0]) / par[0];
345	sum += fland * TMath::Gaus(x[0],xx,par[2]);
346	}
347
348	return (step * sum * invsq2pi / par[2]);
349	}
350	//_______________________________________________________________________
351	Double_t AliITSPident::Gaus2(Double_t x, Double_t par){
352	//normalized gaussian function
353	return 1/(sqrt(2TMath::Pi())par[1])*TMath::Gaus(x[0],par[0],par[1]);
354	}
355	//_______________________________________________________________________
356	void AliITSPident::CookFunItsLay(Int_t lay,Int_t opt,Double_t *par,Double_t dedx,Double_t mom,Double_t rangei,Double_t rangef,TString comment){
357	//it gives the response functions
358	TF1 funLay(comment,Langaufunnorm,rangei,rangef,3);
359	funLay.SetParameters(par);
360	Double_t condFun=funLay.Eval(dedx);
361	if(opt==0){
362	fCondFunProLay[lay]=condFun;
363	if(mom<0.4 && dedx<100)fCondFunProLay[lay]=0.00001;
364	if(mom<0.4 &&dedx<50)fCondFunProLay[lay]=0.0000001;
365	}
366	if(opt==1){
367	fCondFunKLay[lay]=condFun;
368	if(mom<0.25 && dedx<100)fCondFunKLay[lay]=0.00001;
369	if(mom<0.4 &&dedx<30)fCondFunKLay[lay]=0.0000001;
370	}
371	if(opt==2){
372	fCondFunPiLay[lay]=condFun;
373	if(mom<0.6 &&dedx<20)fCondFunPiLay[lay]=0.001;
374	}
375
376	}
377	//_______________________________________________________________________
378	Float_t AliITSPident::CookCombinedBayes(Double_t condfun[][3],Float_t *prior,Int_t part)const {
379	//Bayesian combined PID in the ITS
380	Int_t test=0;
381	Float_t bayes;
382	Float_t pprior[4]={0,0,0,0};
383	for(Int_t j=0;j<4;j++)pprior[j]=prior[j];
384	pprior[2]+=pprior[3];//prior for electrons summed to priors for pions
b68da4a2	385	for(Int_t i=0;i<8;i++){//layer
e62c1aea	386	if (condfun[i][0]>0 \|\| condfun[i][1]>0 \|\|condfun[i][2]>0) test++;
e62c1aea	387	}
b68da4a2	388	if(test>0){
e62c1aea	389	if ((pprior[0]!=0 \|\| pprior[1]!=0 \|\|pprior[2]!=0)&&CookSum(condfun,pprior)!=0){
	390
	391	bayes=pprior[part]CookProd(condfun,part)1/CookSum(condfun,pprior);
	392
	393	}
	394	else bayes=-100;
	395	}
	396	else bayes=-100;
	397	return bayes;
	398	}
	399	//_______________________________________________________________________
	400	Float_t AliITSPident::CookProd(Double_t condfun[][3],Int_t part)const{
	401	//
	402	Float_t p=1;
b68da4a2	403	for(Int_t lay=0;lay<8;lay++){
b68da4a2	404	if(condfun[lay][part]>=0)p=p*condfun[lay][part];
e62c1aea	405	}
	406
	407	return p;
	408	}
	409	//_______________________________________________________________________
	410	Float_t AliITSPident::CookSum(Double_t condfun[][3],Float_t *prior)const{
	411	//
	412	Float_t sum=0;
	413	Float_t pprior[4]={0,0,0,0};
	414	for(Int_t j=0;j<4;j++)pprior[j]=prior[j];
	415	pprior[2]+=pprior[3];//prior for electrons summed to priors for pions
	416	for(Int_t i=0;i<3;i++){//sum over the particles--electrons excluded
	417	sum+=pprior[i]*CookProd(condfun,i);
	418	}
	419	return sum;
	420
	421	}