#include "TNamed.h"
#include "THnSparse.h"
+#include "AliLog.h"
class TF1;
-class TProfile;
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);
- AliCFUnfolding(const AliCFUnfolding& c);
- AliCFUnfolding& operator= (const AliCFUnfolding& c);
+ 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 SetMaxNumberOfIterations(Int_t n = 10) {fMaxNumIterations=n; fNRandomIterations=n; }
-
- /*
- The following is for correct error estimation
- thanks to Marta Verweij
- */
- void SetUseCorrelatedErrors (Double_t maxConvergence = 1.e-06 , UInt_t randomSeed = 0) {
- fUseCorrelatedErrors = kTRUE ;
- fRandomSeed = randomSeed ;
- SetMaxConvergencePerDOF(maxConvergence) ;
+ 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 UnsetCorrelatedErrors() {fUseCorrelatedErrors=kFALSE;}
-
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
void Unfold();
- THnSparse* GetResponse() const {return fResponse;}
- THnSparse* GetInverseResponse() const {return fInverseResponse;}
- THnSparse* GetPrior() const {return fPrior;}
- 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;}
- TProfile* GetDeltaUnfoldedProfile() const {return fDeltaUnfoldedP;}
- Int_t GetDOF(); // Returns number of degrees of freedom
+ 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
- THnSparse *fResponse; // 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
- 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 *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 (modified)
- THnSparse *fMeasuredOrig; // Measured spectrum to be unfolded : dimensions must be N = number of variables (not modified)
- Int_t fMaxNumIterations; // Maximum number of iterations to be performed
- 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; 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"
-
- /* correlated error calculation */
- Double_t fMaxConvergence; // Convergence criterion in case of correlated error calculation
- Bool_t fUseCorrelatedErrors;// Calculate correlated errors for the final unfolded spectrum; default is kTRUE
- Int_t fNRandomIterations; // Number of random distributed measured spectra
-
+ //
+ 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
- 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)
- THnSparse *fProjResponseInT; // Projection of the response matrix on TRUE axis
- THnSparse *fUnfolded; // 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
+ //
+ 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 *fRandomizedDist; // Randomized distribution for each bin of the measured spectrum to calculate correlated errors
+ 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 *fRandomUnfolded;
- TProfile *fDeltaUnfoldedP; // Profile of the delta-unfolded distribution
- Int_t fNCalcCorrErrors; // book keeping to prevend infinite loop
+ 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
/* correlated error calculation */
Double_t GetConvergence(); // Returns convergence criterion
void CalculateCorrelatedErrors(); // Calculates correlated errors for the final unfolded spectrum
- void InitDeltaUnfoldedProfile(); // Initializes the fDeltaUnfoldedP Profiles with spread option
void CreateRandomizedDist(); // Create randomized dist from measured distribution
void FillDeltaUnfoldedProfile(); // Fills the fDeltaUnfoldedP profile
void SetMaxConvergencePerDOF (Double_t val);
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff