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Merge pull request #5 from kreczko/tau-svd-unfolding
Tau svd unfolding
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@@ -16,4 +16,6 @@ Makefile | |
| *~ | ||
| *Dict.cxx | ||
| *Dict_rdict.pcm | ||
| Test* | ||
| Test/* | ||
| /Test/ | ||
| *.pyc | ||
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| /* | ||
| * RooUnfoldUtils.h | ||
| * | ||
| * Created on: 24 Feb 2015 | ||
| * Author: kreczko | ||
| */ | ||
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| #ifndef INCLUDE_ROOUNFOLDUTILS_H | ||
| #define INCLUDE_ROOUNFOLDUTILS_H | ||
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| #include "TVectorD.h" | ||
| namespace RooUnfoldUtils { | ||
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| /** | ||
| * Calculates equidistant points on a logarithmic scale | ||
| * | ||
| * parameters: | ||
| * - tau_min: minimal tau value (> 0) | ||
| * - tau_max: maximal tau value | ||
| * - n_points: number of points (> 3) | ||
| */ | ||
| TVectorD* calculate_tau_scan_points(double tau_min, double tau_max, unsigned int n_points); | ||
| } | ||
| #endif /* INCLUDE_ROOUNFOLDUTILS_H */ |
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| // Author: Kerstin Tackmann, Andreas Hoecker, Heiko Lacker | ||
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| /********************************************************************************** | ||
| * * | ||
| * Project: TSVDUnfold_local - data unfolding based on Singular Value Decomposition * | ||
| * Package: ROOT * | ||
| * Class : TSVDUnfold_local * | ||
| * * | ||
| * Description: * | ||
| * Single class implementation of SVD data unfolding based on: * | ||
| * A. Hoecker, V. Kartvelishvili, * | ||
| * "SVD approach to data unfolding" * | ||
| * NIM A372, 469 (1996) [hep-ph/9509307] * | ||
| * * | ||
| * Authors: * | ||
| * Kerstin Tackmann <[email protected]> - CERN, Switzerland * | ||
| * Andreas Hoecker <[email protected]> - CERN, Switzerland * | ||
| * Heiko Lacker <[email protected]> - Humboldt U, Germany * | ||
| * * | ||
| * Copyright (c) 2010: * | ||
| * CERN, Switzerland * | ||
| * Humboldt University, Germany * | ||
| * * | ||
| **********************************************************************************/ | ||
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| ////////////////////////////////////////////////////////////////////////// | ||
| // // | ||
| // TSVDUnfold_local // | ||
| // // | ||
| // Data unfolding using Singular Value Decomposition (hep-ph/9509307) // | ||
| // Authors: Kerstin Tackmann, Andreas Hoecker, Heiko Lacker // | ||
| // // | ||
| ////////////////////////////////////////////////////////////////////////// | ||
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| #ifndef TSVDUNFOLD_LOCAL_H | ||
| #define TSVDUNFOLD_LOCAL_H | ||
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| #ifndef ROOT_TObject | ||
| #include "TObject.h" | ||
| #endif | ||
| #ifndef ROOT_TMatrixD | ||
| #include "TMatrixD.h" | ||
| #endif | ||
| #ifndef ROOT_TVectorD | ||
| #include "TVectorD.h" | ||
| #endif | ||
| #ifndef ROOT_TMatrixDSym | ||
| #include "TMatrixDSym.h" | ||
| #endif | ||
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| class TH1D; | ||
| class TH2D; | ||
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| class TSVDUnfold_local : public TObject { | ||
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| public: | ||
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| // Constructor | ||
| // Initialisation of unfolding | ||
| // "bdat" - measured data distribution (number of events) | ||
| // "Bcov" - covariance matrix for measured data distribution | ||
| // "bini" - reconstructed MC distribution (number of events) | ||
| // "xini" - truth MC distribution (number of events) | ||
| // "Adet" - detector response matrix (number of events) | ||
| TSVDUnfold_local( const TH1D* bdat, const TH1D* bini, const TH1D* xini, const TH2D* Adet ); | ||
| TSVDUnfold_local( const TH1D* bdat, TH2D* Bcov, const TH1D* bini, const TH1D* xini, const TH2D* Adet ); | ||
| TSVDUnfold_local( const TSVDUnfold_local& other ); | ||
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| // Destructor | ||
| virtual ~TSVDUnfold_local(); | ||
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| // Set option to normalize unfolded spectrum to unit area | ||
| // "normalize" - switch | ||
| void SetNormalize ( Bool_t normalize ) { fNormalize = normalize; } | ||
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| // Do the unfolding | ||
| // "kreg" - number of singular values used (regularisation) | ||
| TH1D* Unfold ( Int_t kreg ); | ||
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| // Determine for given input error matrix covariance matrix of unfolded | ||
| // spectrum from toy simulation | ||
| // "cov" - covariance matrix on the measured spectrum, to be propagated | ||
| // "ntoys" - number of pseudo experiments used for the propagation | ||
| // "seed" - seed for pseudo experiments | ||
| virtual TH2D* GetUnfoldCovMatrix( const TH2D* cov, Int_t ntoys, Int_t seed = 1 ); | ||
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| // Determine covariance matrix of unfolded spectrum from finite statistics in | ||
| // response matrix | ||
| // "ntoys" - number of pseudo experiments used for the propagation | ||
| // "seed" - seed for pseudo experiments | ||
| virtual TH2D* GetAdetCovMatrix( Int_t ntoys, Int_t seed=1 ); | ||
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| // Regularisation parameter | ||
| Int_t GetKReg() const { return fKReg; } | ||
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| // Obtain the distribution of |d| (for determining the regularization) | ||
| TH1D* GetD() const; | ||
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| // Obtain the distribution of singular values | ||
| TH1D* GetSV() const; | ||
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| // Obtain the computed regularized covariance matrix | ||
| TH2D* GetXtau() const; | ||
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| // Obtain the computed inverse of the covariance matrix | ||
| TH2D* GetXinv() const; | ||
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| //Obtain the covariance matrix on the data | ||
| TH2D* GetBCov() const; | ||
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| // Helper functions | ||
| Double_t ComputeChiSquared( const TH1D& truspec, const TH1D& unfspec ); | ||
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| // only difference to TSVDUnfold in ROOT | ||
| protected: | ||
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| // Helper functions for vector and matrix operations | ||
| void FillCurvatureMatrix( TMatrixD& tCurv, TMatrixD& tC ) const; | ||
| static Double_t GetCurvature ( const TVectorD& vec, const TMatrixD& curv ); | ||
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| void InitHistos ( ); | ||
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| // Helper functions | ||
| static void H2V ( const TH1D* histo, TVectorD& vec ); | ||
| static void H2Verr ( const TH1D* histo, TVectorD& vec ); | ||
| static void V2H ( const TVectorD& vec, TH1D& histo ); | ||
| static void H2M ( const TH2D* histo, TMatrixD& mat ); | ||
| static void M2H ( const TMatrixD& mat, TH2D& histo ); | ||
| static TMatrixD MatDivVec( const TMatrixD& mat, const TVectorD& vec, Int_t zero=0 ); | ||
| static TVectorD CompProd ( const TVectorD& vec1, const TVectorD& vec2 ); | ||
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| static TVectorD VecDiv ( const TVectorD& vec1, const TVectorD& vec2, Int_t zero = 0 ); | ||
| static void RegularisedSymMatInvert( TMatrixDSym& mat, Double_t eps = 1e-3 ); | ||
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| // Class members | ||
| Int_t fNdim; //! Truth and reconstructed dimensions | ||
| Int_t fDdim; //! Derivative for curvature matrix | ||
| Bool_t fNormalize; //! Normalize unfolded spectrum to 1 | ||
| Int_t fKReg; //! Regularisation parameter | ||
| TH1D* fDHist; //! Distribution of d (for checking regularization) | ||
| TH1D* fSVHist; //! Distribution of singular values | ||
| TH2D* fXtau; //! Computed regularized covariance matrix | ||
| TH2D* fXinv; //! Computed inverse of covariance matrix | ||
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| // Input histos | ||
| const TH1D* fBdat; // measured distribution (data) | ||
| TH2D* fBcov; // covariance matrix of measured distribution (data) | ||
| const TH1D* fBini; // reconstructed distribution (MC) | ||
| const TH1D* fXini; // truth distribution (MC) | ||
| const TH2D* fAdet; // Detector response matrix | ||
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| // Evaluation of covariance matrices | ||
| TH1D* fToyhisto; //! Toy MC histogram | ||
| TH2D* fToymat; //! Toy MC detector response matrix | ||
| Bool_t fToyMode; //! Internal switch for covariance matrix propagation | ||
| Bool_t fMatToyMode; //! Internal switch for evaluation of statistical uncertainties from response matrix | ||
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| ClassDef( TSVDUnfold_local, 0 ) // Data unfolding using Singular Value Decomposition (hep-ph/9509307) | ||
| }; | ||
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| #endif |
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