+ smoothing using a fit function
[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 //
8// Class to handle general unfolding procedure //
9// For the moment only bayesian unfolding is supported //
10// The next steps are to add chi2 minimisation and weighting methods //
11// //
12// Author : renaud.vernet@cern.ch //
13//--------------------------------------------------------------------//
14
15#include "TNamed.h"
16#include "THnSparse.h"
17
85b6bda9 18class TF1;
19
c0b10ad4 20class AliCFUnfolding : public TNamed {
21
22 public :
23 AliCFUnfolding();
24 AliCFUnfolding(const Char_t* name, const Char_t* title, const Int_t nVar,
25 const THnSparse* response, const THnSparse* efficiency, const THnSparse* measured, const THnSparse* prior=0x0);
26 AliCFUnfolding(const AliCFUnfolding& c);
27 AliCFUnfolding& operator= (const AliCFUnfolding& c);
28 ~AliCFUnfolding();
29
30 void SetMaxNumberOfIterations(Int_t n) {fMaxNumIterations=n;}
31 void SetMaxChi2(Double_t val) {fMaxChi2=val;}
32 void SetMaxChi2PerDOF(Double_t val);
85b6bda9 33 void UseSmoothing(TF1* fcn=0x0, Option_t* opt="iremn") { // if fcn=0x0 then smooth using neighbouring bins
34 fUseSmoothing=kTRUE; // this function must NOT be used if fNVariables > 3
35 fSmoothFunction=fcn; // the option "opt" is used if "fcn" is specified
36 fSmoothOption=opt;
37 }
38
c0b10ad4 39 void Unfold();
40
41 THnSparse* GetResponse() const {return fResponse;}
42 THnSparse* GetInverseResponse() const {return fInverseResponse;}
43 THnSparse* GetPrior() const {return fPrior;}
44 THnSparse* GetOriginalPrior() const {return fOriginalPrior;}
45 THnSparse* GetEfficiency() const {return fEfficiency;}
46 THnSparse* GetUnfolded() const {return fUnfolded;}
85b6bda9 47 THnSparse* GetEstMeasured() const {return fMeasuredEstimate;}
48 THnSparse* GetConditional() const {return fConditional;}
49 TF1* GetSmoothFunction() const {return fSmoothFunction;}
c0b10ad4 50
51 private :
52
53 // user-related settings
54 THnSparse *fResponse; // Response matrix : dimensions must be 2N = 2 x (number of variables)
85b6bda9 55 // dimensions 0 -> N-1 must be filled with reconstructed values
56 // dimensions N -> 2N-1 must be filled with generated values
c0b10ad4 57 THnSparse *fPrior; // This is the assumed generated distribution : dimensions must be N = number of variables
58 // it will be used at the first step
59 // then will be updated automatically at each iteration
c0b10ad4 60 THnSparse *fEfficiency; // Efficiency map : dimensions must be N = number of variables
61 // this map must be filled only with "true" values of the variables (should not include resolution effects)
62 THnSparse *fMeasured; // Measured spectrum to be unfolded : dimensions must be N = number of variables
63 Int_t fMaxNumIterations; // Maximum number of iterations to be performed
64 Int_t fNVariables; // Number of variables used in analysis spectra (pt, y, ...)
65 Double_t fMaxChi2; // Maximum Chi2 between unfolded and prior distributions.
66 Bool_t fUseSmoothing; // Smooth the unfolded sectrum at each iteration
85b6bda9 67 TF1 *fSmoothFunction; // Function used to smooth the unfolded spectrum
68 Option_t *fSmoothOption; // Option to use during the fit (with fSmoothFunction) ; default is "iremn"
c0b10ad4 69
70 // internal settings
85b6bda9 71 THnSparse *fOriginalPrior; // This is the original prior distribution : will not be modified
c0b10ad4 72 THnSparse *fInverseResponse; // Inverse response matrix
73 THnSparse *fMeasuredEstimate; // Estimation of the measured (M) spectrum given the a priori (T) distribution
74 THnSparse *fConditional; // Matrix holding the conditional probabilities P(M|T)
75 THnSparse *fProjResponseInT; // Projection of the response matrix on TRUE axis
76 THnSparse *fUnfolded; // Unfolded spectrum
77 Int_t *fCoordinates2N; // Coordinates in 2N (measured,true) space
78 Int_t *fCoordinatesN_M; // Coordinates in measured space
79 Int_t *fCoordinatesN_T; // Coordinates in true space
80
81
82 // functions
85b6bda9 83 void Init(); // initialisation of the internal settings
84 void GetCoordinates(); // gets a cell coordinates in Measured and True space
85 void CreateConditional(); // creates the conditional matrix from the response matrix
86 void CreateEstMeasured(); // creates the measured spectrum estimation from the conditional matrix and the prior distribution
87 void CreateInvResponse(); // creates the inverse response function (Bayes Theorem) from the conditional matrix and the prior distribution
88 void CreateUnfolded(); // creates the unfolded spectrum from the inverse response matrix and the measured distribution
89 void CreateFlatPrior(); // creates a flat a priori distribution in case the one given in the constructor is null
90 Double_t GetChi2(); // returns the chi2 between unfolded and prior spectra
91 Short_t Smooth(); // function calling smoothing methods
92 Short_t SmoothUsingNeighbours(); // smoothes the unfolded spectrum using the neighbouring cells
93 Short_t SmoothUsingFunction(); // smoothes the unfolded spectrum using a fit function
c0b10ad4 94
95 ClassDef(AliCFUnfolding,0);
96};
97
98#endif