[u/mrichter/AliRoot.git] / CORRFW / AliCFUnfolding.h


#ifndef ALICFUNFOLDING_H
#define ALICFUNFOLDING_H

//--------------------------------------------------------------------//
//                                                                    //
// AliCFUnfolding Class                                               //
// Class to handle general unfolding procedure using bayesian method  //
//                                                                    //
// Author : renaud.vernet@cern.ch                                     //
//--------------------------------------------------------------------//

#include "TNamed.h"
#include "THnSparse.h"
#include "AliLog.h"

class TF1;
class TRandom3;

class AliCFUnfolding : public TNamed {

 public :

  AliCFUnfolding();
  AliCFUnfolding(const Char_t* name, const Char_t* title, const Int_t nVar, 
		 const THnSparse* response, const THnSparse* efficiency, const THnSparse* measured, const THnSparse* prior=0x0, 
		 Double_t maxConvergencePerDOF = 1.e-06, UInt_t randomSeed = 0,
		 Int_t maxNumIterations = 10);
  ~AliCFUnfolding();
  void UnsetCorrelatedErrors()  {AliError("===================> DEPRECATED <=====================");}
  void SetUseCorrelatedErrors() {AliError("===================> DEPRECATED <=====================");}
  void SetMaxNumberOfIterations(Int_t n = 10)  {
    AliError("===================> DEPRECATED : should be set in constructor <=====================");
    AliError("===================> DEPRECATED : will be removed soon         <=====================");
    fMaxNumIterations = n;
  }

  void UseSmoothing(TF1* fcn=0x0, Option_t* opt="iremn") { // if fcn=0x0 then smooth using neighbouring bins 
    fUseSmoothing=kTRUE;                                   // this function must NOT be used if fNVariables > 3
    fSmoothFunction=fcn;                                   // the option "opt" is used if "fcn" is specified
    fSmoothOption=opt;
  } 
                                                                                                
  void Unfold();

  const THnSparse* GetResponse()             const {return fResponseOrig;}
  const THnSparse* GetEfficiency()           const {return fEfficiencyOrig;}
  const THnSparse* GetMeasured()             const {return fMeasuredOrig;}
  const THnSparse* GetOriginalPrior()        const {return fPriorOrig;}
        THnSparse* GetInverseResponse()      const {return fInverseResponse;}
        THnSparse* GetPrior()                const {return fPrior;}
	THnSparse* GetUnfolded()             const {return fUnfoldedFinal;}
        THnSparse* GetEstMeasured()          const {return fMeasuredEstimate;}
	THnSparse* GetConditional()          const {return fConditional;}
	TF1*       GetSmoothFunction()       const {return fSmoothFunction;}
	THnSparse* GetDeltaUnfoldedProfile() const {return fDeltaUnfoldedP;}
	Int_t      GetDOF();                 // Returns number of degrees of freedom

  static Short_t  SmoothUsingNeighbours(THnSparse*); // smoothes the unfolded spectrum using the neighbouring cells

 private :
  AliCFUnfolding(const AliCFUnfolding& c);
  AliCFUnfolding& operator= (const AliCFUnfolding& c);
  
  //
  // user-related settings
  //
  const THnSparse     *fResponseOrig;     // Response matrix : dimensions must be 2N = 2 x (number of variables)
                                          // dimensions 0 ->  N-1 must be filled with reconstructed values
                                          // dimensions N -> 2N-1 must be filled with generated values
  const THnSparse     *fPriorOrig;        // This is the assumed generated distribution : dimensions must be N = number of variables
                                          // it will be used at the first step 
                                          // then will be updated automatically at each iteration
  const THnSparse     *fEfficiencyOrig;   // Efficiency map : dimensions must be N = number of variables (modified)
                                          // this map must be filled only with "true" values of the variables (should not do "bin smearing")
  const THnSparse     *fMeasuredOrig;     // Measured spectrum to be unfolded : dimensions must be N = number of variables (modified)

        Int_t          fMaxNumIterations; // Maximum  number of iterations to be performed
	Int_t          fNVariables;       // Number of variables used in analysis spectra (pt, y, ...)
        Bool_t         fUseSmoothing;     // Smooth the unfolded sectrum at each iteration; default is kFALSE
	TF1           *fSmoothFunction;   // Function used to smooth the unfolded spectrum
	Option_t      *fSmoothOption;     // Option to use during the fit (with fSmoothFunction) ; default is "iremn"

  //
  // internal settings
  //
	Double_t       fMaxConvergence;    // Convergence criterion in case of correlated error calculation
        Int_t          fNRandomIterations; // Number of random distributed measured spectra
	THnSparse     *fResponse;          // Copy of the original response matrix    (modified)
	THnSparse     *fPrior;             // Copy of the original prior spectrum     (modified)
	THnSparse     *fEfficiency;        // Copy of original efficiency             (modified)
	THnSparse     *fMeasured;          // Copy of the original measureed spectrum (modified)
        THnSparse     *fInverseResponse;   // Inverse response matrix
        THnSparse     *fMeasuredEstimate;  // Estimation of the measured (M) spectrum given the a priori (T) distribution
	THnSparse     *fConditional;       // Matrix holding the conditional probabilities P(M|T)
        THnSparse     *fUnfolded;          // Unfolded spectrum (modified before and during error calculation)
	THnSparse     *fUnfoldedFinal;     // Final unfolded spectrum
        Int_t         *fCoordinates2N;     // Coordinates in 2N (measured,true) space
	Int_t         *fCoordinatesN_M;    // Coordinates in measured space
	Int_t         *fCoordinatesN_T;    // Coordinates in true space


  /* correlated error calculation */
  THnSparse     *fRandomResponse;    // Randomized distribution for each bin of the response matrix     to calculate correlated errors
  THnSparse     *fRandomEfficiency;  // Randomized distribution for each bin of the efficiency spectrum to calculate correlated errors
  THnSparse     *fRandomMeasured;    // Randomized distribution for each bin of the measured   spectrum to calculate correlated errors
  TRandom3      *fRandom3;           // Object to get random number following Poisson distribution
  THnSparse     *fDeltaUnfoldedP;    // Profile of the delta-unfolded distribution
  THnSparse     *fDeltaUnfoldedN;    // Entries of the delta-unfolded distribution (count for each bin)
  Short_t        fNCalcCorrErrors;   // Book-keeping to prevend infinite loop
  UInt_t         fRandomSeed;        // Random seed


  // functions
  void     Init();                  // initialisation of the internal settings
  void     GetCoordinates();        // gets a cell coordinates in Measured and True space
  void     CreateConditional();     // creates the conditional matrix from the response matrix
  void     CreateEstMeasured();     // creates the measured spectrum estimation from the conditional matrix and the prior distribution
  void     CreateInvResponse();     // creates the inverse response function (Bayes Theorem) from the conditional matrix and the prior distribution
  void     CreateUnfolded();        // creates the unfolded spectrum from the inverse response matrix and the measured distribution
  void     CreateFlatPrior();       // creates a flat a priori distribution in case the one given in the constructor is null
  Double_t GetChi2();               // returns the chi2 between unfolded and prior spectra
  Short_t  Smooth();                // function calling smoothing methods
  Short_t  SmoothUsingFunction();   // smoothes the unfolded spectrum using a fit function

  /* correlated error calculation */
  Double_t GetConvergence();            // Returns convergence criterion
  void     CalculateCorrelatedErrors(); // Calculates correlated errors for the final unfolded spectrum
  void     CreateRandomizedDist();      // Create randomized dist from measured distribution
  void     FillDeltaUnfoldedProfile();  // Fills the fDeltaUnfoldedP profile
  void     SetMaxConvergencePerDOF (Double_t val);

  ClassDef(AliCFUnfolding,1);
};

#endif
Commit	Line	Data
c0b10ad4	1
	2	#ifndef ALICFUNFOLDING_H
	3	#define ALICFUNFOLDING_H
	4
	5	//--------------------------------------------------------------------//
	6	// //
	7	// AliCFUnfolding Class //
fb494025	8	// Class to handle general unfolding procedure using bayesian method //
c0b10ad4	9	// //
	10	// Author : renaud.vernet@cern.ch //
	11	//--------------------------------------------------------------------//
	12
	13	#include "TNamed.h"
	14	#include "THnSparse.h"
a9500e70	15	#include "AliLog.h"
c0b10ad4	16
85b6bda9	17	class TF1;
769f5114	18	class TRandom3;
85b6bda9	19
c0b10ad4	20	class AliCFUnfolding : public TNamed {
	21
	22	public :
a9500e70	23
c0b10ad4	24	AliCFUnfolding();
c0b10ad4	25	AliCFUnfolding(const Char_t* name, const Char_t* title, const Int_t nVar,
a9500e70	26	const THnSparse* response, const THnSparse* efficiency, const THnSparse* measured, const THnSparse* prior=0x0,
	27	Double_t maxConvergencePerDOF = 1.e-06, UInt_t randomSeed = 0,
	28	Int_t maxNumIterations = 10);
c0b10ad4	29	~AliCFUnfolding();
a9500e70	30	void UnsetCorrelatedErrors() {AliError("===================> DEPRECATED <=====================");}
	31	void SetUseCorrelatedErrors() {AliError("===================> DEPRECATED <=====================");}
	32	void SetMaxNumberOfIterations(Int_t n = 10) {
	33	AliError("===================> DEPRECATED : should be set in constructor <=====================");
	34	AliError("===================> DEPRECATED : will be removed soon <=====================");
	35	fMaxNumIterations = n;
769f5114	36	}
769f5114	37
85b6bda9	38	void UseSmoothing(TF1* fcn=0x0, Option_t* opt="iremn") { // if fcn=0x0 then smooth using neighbouring bins
	39	fUseSmoothing=kTRUE; // this function must NOT be used if fNVariables > 3
	40	fSmoothFunction=fcn; // the option "opt" is used if "fcn" is specified
	41	fSmoothOption=opt;
	42	}
	43
c0b10ad4	44	void Unfold();
c0b10ad4	45
5a575436	46	const THnSparse* GetResponse() const {return fResponseOrig;}
	47	const THnSparse* GetEfficiency() const {return fEfficiencyOrig;}
	48	const THnSparse* GetMeasured() const {return fMeasuredOrig;}
	49	const THnSparse* GetOriginalPrior() const {return fPriorOrig;}
	50	THnSparse* GetInverseResponse() const {return fInverseResponse;}
	51	THnSparse* GetPrior() const {return fPrior;}
	52	THnSparse* GetUnfolded() const {return fUnfoldedFinal;}
	53	THnSparse* GetEstMeasured() const {return fMeasuredEstimate;}
	54	THnSparse* GetConditional() const {return fConditional;}
	55	TF1* GetSmoothFunction() const {return fSmoothFunction;}
	56	THnSparse* GetDeltaUnfoldedProfile() const {return fDeltaUnfoldedP;}
	57	Int_t GetDOF(); // Returns number of degrees of freedom
c0b10ad4	58
7036630f	59	static Short_t SmoothUsingNeighbours(THnSparse*); // smoothes the unfolded spectrum using the neighbouring cells
7036630f	60
c0b10ad4	61	private :
67f40e08	62	AliCFUnfolding(const AliCFUnfolding& c);
67f40e08	63	AliCFUnfolding& operator= (const AliCFUnfolding& c);
c0b10ad4	64
5a575436	65	//
c0b10ad4	66	// user-related settings
5a575436	67	//
	68	const THnSparse *fResponseOrig; // Response matrix : dimensions must be 2N = 2 x (number of variables)
	69	// dimensions 0 -> N-1 must be filled with reconstructed values
	70	// dimensions N -> 2N-1 must be filled with generated values
	71	const THnSparse *fPriorOrig; // This is the assumed generated distribution : dimensions must be N = number of variables
	72	// it will be used at the first step
	73	// then will be updated automatically at each iteration
	74	const THnSparse *fEfficiencyOrig; // Efficiency map : dimensions must be N = number of variables (modified)
	75	// this map must be filled only with "true" values of the variables (should not do "bin smearing")
	76	const THnSparse *fMeasuredOrig; // Measured spectrum to be unfolded : dimensions must be N = number of variables (modified)
	77
	78	Int_t fMaxNumIterations; // Maximum number of iterations to be performed
	79	Int_t fNVariables; // Number of variables used in analysis spectra (pt, y, ...)
	80	Bool_t fUseSmoothing; // Smooth the unfolded sectrum at each iteration; default is kFALSE
	81	TF1 *fSmoothFunction; // Function used to smooth the unfolded spectrum
	82	Option_t *fSmoothOption; // Option to use during the fit (with fSmoothFunction) ; default is "iremn"
	83
	84	//
c0b10ad4	85	// internal settings
5a575436	86	//
	87	Double_t fMaxConvergence; // Convergence criterion in case of correlated error calculation
	88	Int_t fNRandomIterations; // Number of random distributed measured spectra
	89	THnSparse *fResponse; // Copy of the original response matrix (modified)
	90	THnSparse *fPrior; // Copy of the original prior spectrum (modified)
	91	THnSparse *fEfficiency; // Copy of original efficiency (modified)
	92	THnSparse *fMeasured; // Copy of the original measureed spectrum (modified)
	93	THnSparse *fInverseResponse; // Inverse response matrix
	94	THnSparse *fMeasuredEstimate; // Estimation of the measured (M) spectrum given the a priori (T) distribution
	95	THnSparse *fConditional; // Matrix holding the conditional probabilities P(M\|T)
	96	THnSparse *fUnfolded; // Unfolded spectrum (modified before and during error calculation)
	97	THnSparse *fUnfoldedFinal; // Final unfolded spectrum
	98	Int_t *fCoordinates2N; // Coordinates in 2N (measured,true) space
	99	Int_t *fCoordinatesN_M; // Coordinates in measured space
	100	Int_t *fCoordinatesN_T; // Coordinates in true space
769f5114	101
	102
	103	/* correlated error calculation */
5a575436	104	THnSparse *fRandomResponse; // Randomized distribution for each bin of the response matrix to calculate correlated errors
	105	THnSparse *fRandomEfficiency; // Randomized distribution for each bin of the efficiency spectrum to calculate correlated errors
	106	THnSparse *fRandomMeasured; // Randomized distribution for each bin of the measured spectrum to calculate correlated errors
769f5114	107	TRandom3 *fRandom3; // Object to get random number following Poisson distribution
a9500e70	108	THnSparse *fDeltaUnfoldedP; // Profile of the delta-unfolded distribution
	109	THnSparse *fDeltaUnfoldedN; // Entries of the delta-unfolded distribution (count for each bin)
	110	Short_t fNCalcCorrErrors; // Book-keeping to prevend infinite loop
769f5114	111	UInt_t fRandomSeed; // Random seed
769f5114	112
c0b10ad4	113
c0b10ad4	114	// functions
85b6bda9	115	void Init(); // initialisation of the internal settings
	116	void GetCoordinates(); // gets a cell coordinates in Measured and True space
	117	void CreateConditional(); // creates the conditional matrix from the response matrix
	118	void CreateEstMeasured(); // creates the measured spectrum estimation from the conditional matrix and the prior distribution
	119	void CreateInvResponse(); // creates the inverse response function (Bayes Theorem) from the conditional matrix and the prior distribution
	120	void CreateUnfolded(); // creates the unfolded spectrum from the inverse response matrix and the measured distribution
	121	void CreateFlatPrior(); // creates a flat a priori distribution in case the one given in the constructor is null
	122	Double_t GetChi2(); // returns the chi2 between unfolded and prior spectra
	123	Short_t Smooth(); // function calling smoothing methods
85b6bda9	124	Short_t SmoothUsingFunction(); // smoothes the unfolded spectrum using a fit function
c0b10ad4	125
769f5114	126	/* correlated error calculation */
	127	Double_t GetConvergence(); // Returns convergence criterion
	128	void CalculateCorrelatedErrors(); // Calculates correlated errors for the final unfolded spectrum
769f5114	129	void CreateRandomizedDist(); // Create randomized dist from measured distribution
	130	void FillDeltaUnfoldedProfile(); // Fills the fDeltaUnfoldedP profile
	131	void SetMaxConvergencePerDOF (Double_t val);
	132
	133	ClassDef(AliCFUnfolding,1);
c0b10ad4	134	};
	135
	136	#endif