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1 | ||
2 | #ifndef ALICFUNFOLDING_H | |
3 | #define ALICFUNFOLDING_H | |
4 | ||
5 | //--------------------------------------------------------------------// | |
6 | // // | |
7 | // AliCFUnfolding Class // | |
8 | // Class to handle general unfolding procedure using bayesian method // | |
9 | // // | |
10 | // Author : renaud.vernet@cern.ch // | |
11 | //--------------------------------------------------------------------// | |
12 | ||
13 | #include "TNamed.h" | |
14 | #include "THnSparse.h" | |
15 | ||
16 | class TF1; | |
17 | class TProfile; | |
18 | class TRandom3; | |
19 | ||
20 | class 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 = 10) {fMaxNumIterations=n; fNRandomIterations=n; } | |
31 | ||
32 | /* | |
33 | The following is for correct error estimation | |
34 | thanks to Marta Verweij | |
35 | */ | |
36 | void SetUseCorrelatedErrors (Double_t maxConvergence = 1.e-06 , UInt_t randomSeed = 0) { | |
37 | fUseCorrelatedErrors = kTRUE ; | |
38 | fRandomSeed = randomSeed ; | |
39 | SetMaxConvergencePerDOF(maxConvergence) ; | |
40 | } | |
41 | void UnsetCorrelatedErrors() {fUseCorrelatedErrors=kFALSE;} | |
42 | ||
43 | ||
44 | void UseSmoothing(TF1* fcn=0x0, Option_t* opt="iremn") { // if fcn=0x0 then smooth using neighbouring bins | |
45 | fUseSmoothing=kTRUE; // this function must NOT be used if fNVariables > 3 | |
46 | fSmoothFunction=fcn; // the option "opt" is used if "fcn" is specified | |
47 | fSmoothOption=opt; | |
48 | } | |
49 | ||
50 | void Unfold(); | |
51 | ||
52 | THnSparse* GetResponse() const {return fResponse;} | |
53 | THnSparse* GetInverseResponse() const {return fInverseResponse;} | |
54 | THnSparse* GetPrior() const {return fPrior;} | |
55 | THnSparse* GetOriginalPrior() const {return fOriginalPrior;} | |
56 | THnSparse* GetEfficiency() const {return fEfficiency;} | |
57 | THnSparse* GetUnfolded() const {return fUnfolded;} | |
58 | THnSparse* GetEstMeasured() const {return fMeasuredEstimate;} | |
59 | THnSparse* GetMeasured() const {return fMeasured;} | |
60 | THnSparse* GetConditional() const {return fConditional;} | |
61 | TF1* GetSmoothFunction() const {return fSmoothFunction;} | |
62 | TProfile* GetDeltaUnfoldedProfile() const {return fDeltaUnfoldedP;} | |
63 | Int_t GetDOF(); // Returns number of degrees of freedom | |
64 | ||
65 | static Short_t SmoothUsingNeighbours(THnSparse*); // smoothes the unfolded spectrum using the neighbouring cells | |
66 | ||
67 | private : | |
68 | ||
69 | // user-related settings | |
70 | THnSparse *fResponse; // Response matrix : dimensions must be 2N = 2 x (number of variables) | |
71 | // dimensions 0 -> N-1 must be filled with reconstructed values | |
72 | // dimensions N -> 2N-1 must be filled with generated values | |
73 | THnSparse *fPrior; // This is the assumed generated distribution : dimensions must be N = number of variables | |
74 | // it will be used at the first step | |
75 | // then will be updated automatically at each iteration | |
76 | THnSparse *fEfficiency; // Efficiency map : dimensions must be N = number of variables | |
77 | // this map must be filled only with "true" values of the variables (should not include resolution effects) | |
78 | THnSparse *fMeasured; // Measured spectrum to be unfolded : dimensions must be N = number of variables (modified) | |
79 | THnSparse *fMeasuredOrig; // Measured spectrum to be unfolded : dimensions must be N = number of variables (not modified) | |
80 | Int_t fMaxNumIterations; // Maximum number of iterations to be performed | |
81 | Int_t fNVariables; // Number of variables used in analysis spectra (pt, y, ...) | |
82 | /* Double_t fMaxChi2; // Maximum Chi2 between unfolded and prior distributions. */ | |
83 | Bool_t fUseSmoothing; // Smooth the unfolded sectrum at each iteration; default is kFALSE | |
84 | TF1 *fSmoothFunction; // Function used to smooth the unfolded spectrum | |
85 | Option_t *fSmoothOption; // Option to use during the fit (with fSmoothFunction) ; default is "iremn" | |
86 | ||
87 | /* correlated error calculation */ | |
88 | Double_t fMaxConvergence; // Convergence criterion in case of correlated error calculation | |
89 | Bool_t fUseCorrelatedErrors;// Calculate correlated errors for the final unfolded spectrum; default is kTRUE | |
90 | Int_t fNRandomIterations; // Number of random distributed measured spectra | |
91 | ||
92 | // internal settings | |
93 | THnSparse *fOriginalPrior; // This is the original prior distribution : will not be modified | |
94 | THnSparse *fInverseResponse; // Inverse response matrix | |
95 | THnSparse *fMeasuredEstimate; // Estimation of the measured (M) spectrum given the a priori (T) distribution | |
96 | THnSparse *fConditional; // Matrix holding the conditional probabilities P(M|T) | |
97 | THnSparse *fProjResponseInT; // Projection of the response matrix on TRUE axis | |
98 | THnSparse *fUnfolded; // Unfolded spectrum | |
99 | Int_t *fCoordinates2N; // Coordinates in 2N (measured,true) space | |
100 | Int_t *fCoordinatesN_M; // Coordinates in measured space | |
101 | Int_t *fCoordinatesN_T; // Coordinates in true space | |
102 | ||
103 | ||
104 | /* correlated error calculation */ | |
105 | THnSparse *fRandomizedDist; // Randomized distribution for each bin of the measured spectrum to calculate correlated errors | |
106 | TRandom3 *fRandom3; // Object to get random number following Poisson distribution | |
107 | THnSparse *fRandomUnfolded; | |
108 | TProfile *fDeltaUnfoldedP; // Profile of the delta-unfolded distribution | |
109 | Int_t fNCalcCorrErrors; // book keeping to prevend infinite loop | |
110 | UInt_t fRandomSeed; // Random seed | |
111 | ||
112 | ||
113 | // functions | |
114 | void Init(); // initialisation of the internal settings | |
115 | void GetCoordinates(); // gets a cell coordinates in Measured and True space | |
116 | void CreateConditional(); // creates the conditional matrix from the response matrix | |
117 | void CreateEstMeasured(); // creates the measured spectrum estimation from the conditional matrix and the prior distribution | |
118 | void CreateInvResponse(); // creates the inverse response function (Bayes Theorem) from the conditional matrix and the prior distribution | |
119 | void CreateUnfolded(); // creates the unfolded spectrum from the inverse response matrix and the measured distribution | |
120 | void CreateFlatPrior(); // creates a flat a priori distribution in case the one given in the constructor is null | |
121 | Double_t GetChi2(); // returns the chi2 between unfolded and prior spectra | |
122 | Short_t Smooth(); // function calling smoothing methods | |
123 | Short_t SmoothUsingFunction(); // smoothes the unfolded spectrum using a fit function | |
124 | ||
125 | /* correlated error calculation */ | |
126 | Double_t GetConvergence(); // Returns convergence criterion | |
127 | void CalculateCorrelatedErrors(); // Calculates correlated errors for the final unfolded spectrum | |
128 | void InitDeltaUnfoldedProfile(); // Initializes the fDeltaUnfoldedP Profiles with spread option | |
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); | |
134 | }; | |
135 | ||
136 | #endif |