support for efficiency and response error propagation
[u/mrichter/AliRoot.git] / CORRFW / AliCFUnfolding.h
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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 17class TF1;
769f5114 18class TRandom3;
85b6bda9 19
c0b10ad4 20class AliCFUnfolding : public TNamed {
21
22 public :
a9500e70 23
c0b10ad4 24 AliCFUnfolding();
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(const AliCFUnfolding& c);
30 AliCFUnfolding& operator= (const AliCFUnfolding& c);
31 ~AliCFUnfolding();
a9500e70 32 void UnsetCorrelatedErrors() {AliError("===================> DEPRECATED <=====================");}
33 void SetUseCorrelatedErrors() {AliError("===================> DEPRECATED <=====================");}
34 void SetMaxNumberOfIterations(Int_t n = 10) {
35 AliError("===================> DEPRECATED : should be set in constructor <=====================");
36 AliError("===================> DEPRECATED : will be removed soon <=====================");
37 fMaxNumIterations = n;
769f5114 38 }
769f5114 39
85b6bda9 40 void UseSmoothing(TF1* fcn=0x0, Option_t* opt="iremn") { // if fcn=0x0 then smooth using neighbouring bins
41 fUseSmoothing=kTRUE; // this function must NOT be used if fNVariables > 3
42 fSmoothFunction=fcn; // the option "opt" is used if "fcn" is specified
43 fSmoothOption=opt;
44 }
45
c0b10ad4 46 void Unfold();
47
5a575436 48 const THnSparse* GetResponse() const {return fResponseOrig;}
49 const THnSparse* GetEfficiency() const {return fEfficiencyOrig;}
50 const THnSparse* GetMeasured() const {return fMeasuredOrig;}
51 const THnSparse* GetOriginalPrior() const {return fPriorOrig;}
52 THnSparse* GetInverseResponse() const {return fInverseResponse;}
53 THnSparse* GetPrior() const {return fPrior;}
54 THnSparse* GetUnfolded() const {return fUnfoldedFinal;}
55 THnSparse* GetEstMeasured() const {return fMeasuredEstimate;}
56 THnSparse* GetConditional() const {return fConditional;}
57 TF1* GetSmoothFunction() const {return fSmoothFunction;}
58 THnSparse* GetDeltaUnfoldedProfile() const {return fDeltaUnfoldedP;}
59 Int_t GetDOF(); // Returns number of degrees of freedom
c0b10ad4 60
7036630f 61 static Short_t SmoothUsingNeighbours(THnSparse*); // smoothes the unfolded spectrum using the neighbouring cells
62
c0b10ad4 63 private :
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
112
c0b10ad4 113
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