//--------------------------------------------------------------------//
// //
// AliCFUnfolding Class //
-// Class to handle general unfolding procedure //
-// For the moment only bayesian unfolding is supported //
-// The next steps are to add chi2 minimisation and weighting methods //
+// Class to handle general unfolding procedure using bayesian method //
// //
// Author : renaud.vernet@cern.ch //
//--------------------------------------------------------------------//
#include "TNamed.h"
#include "THnSparse.h"
+class TF1;
+
class AliCFUnfolding : public TNamed {
public :
void SetMaxNumberOfIterations(Int_t n) {fMaxNumIterations=n;}
void SetMaxChi2(Double_t val) {fMaxChi2=val;}
void SetMaxChi2PerDOF(Double_t val);
- void UseSmoothing(Bool_t b=kTRUE) {fUseSmoothing=b;}
+ 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();
THnSparse* GetResponse() const {return fResponse;}
THnSparse* GetOriginalPrior() const {return fOriginalPrior;}
THnSparse* GetEfficiency() const {return fEfficiency;}
THnSparse* GetUnfolded() const {return fUnfolded;}
+ THnSparse* GetEstMeasured() const {return fMeasuredEstimate;}
+ THnSparse* GetMeasured() const {return fMeasured;}
+ THnSparse* GetConditional() const {return fConditional;}
+ TF1* GetSmoothFunction() const {return fSmoothFunction;}
private :
// user-related settings
THnSparse *fResponse; // Response matrix : dimensions must be 2N = 2 x (number of variables)
- // first N dimensions must be filled with reconstructed values
- // last N dimensions must be filled with generated values
+ // dimensions 0 -> N-1 must be filled with reconstructed values
+ // dimensions N -> 2N-1 must be filled with generated values
THnSparse *fPrior; // 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
- THnSparse *fOriginalPrior; // This is the original prior distribution : will not be modified
THnSparse *fEfficiency; // Efficiency map : dimensions must be N = number of variables
// this map must be filled only with "true" values of the variables (should not include resolution effects)
THnSparse *fMeasured; // Measured spectrum to be unfolded : dimensions must be N = number of variables
Int_t fNVariables; // Number of variables used in analysis spectra (pt, y, ...)
Double_t fMaxChi2; // Maximum Chi2 between unfolded and prior distributions.
Bool_t fUseSmoothing; // Smooth the unfolded sectrum at each iteration
+ 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
+ THnSparse *fOriginalPrior; // This is the original prior distribution : will not be 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)
// 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
- void Smooth(); // smooth the unfolded spectrum using the neighbouring cells
+ 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 SmoothUsingNeighbours(); // smoothes the unfolded spectrum using the neighbouring cells
+ Short_t SmoothUsingFunction(); // smoothes the unfolded spectrum using a fit function
ClassDef(AliCFUnfolding,0);
};
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff