OptimizePick.h

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//                   OpenMS -- Open-Source Mass Spectrometry
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// Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,
// ETH Zurich, and Freie Universitaet Berlin 2002-2012.
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// $Maintainer: Alexandra Zerck $
// $Authors: Eva Lange $
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#ifndef OPENMS_TRANSFORMATIONS_RAW2PEAK_OPTIMIZEPICK_H
#define OPENMS_TRANSFORMATIONS_RAW2PEAK_OPTIMIZEPICK_H

#include <OpenMS/TRANSFORMATIONS/RAW2PEAK/PeakShape.h>
#include <OpenMS/KERNEL/Peak1D.h>

#include <gsl/gsl_vector.h>
#include <gsl/gsl_multifit_nlin.h>
#include <gsl/gsl_blas.h>

#include <iostream>
#include <fstream>
#include <vector>

namespace OpenMS
{
  namespace OptimizationFunctions
  {
    typedef std::vector<Peak1D> RawDataVector;
    typedef RawDataVector::iterator PeakIterator;

    struct OPENMS_DLLAPI PenaltyFactors
    {
      PenaltyFactors() :
        pos(0), lWidth(0), rWidth(0) {}
      PenaltyFactors(const PenaltyFactors & p) :
        pos(p.pos), lWidth(p.lWidth), rWidth(p.rWidth) {}
      inline PenaltyFactors & operator=(const PenaltyFactors & p)
      {
        pos = p.pos;
        lWidth = p.lWidth;
        rWidth = p.rWidth;

        return *this;
      }

      ~PenaltyFactors(){}

      double pos;
      double lWidth;
      double rWidth;
    };

    int residual(const gsl_vector * x, void * params, gsl_vector * f);

    int jacobian(const gsl_vector * x, void * params, gsl_matrix * J);

    int evaluate(const gsl_vector * x, void * params, gsl_vector * f, gsl_matrix * J);

    void printSignal(const gsl_vector * x, void * param, float resolution = 0.25);
  }


  class OPENMS_DLLAPI OptimizePick
  {
public:

    struct Data
    {
      std::vector<double> positions;
      std::vector<double> signal;
      std::vector<PeakShape> peaks;

      OptimizationFunctions::PenaltyFactors penalties;

    };


    typedef std::vector<Peak1D> RawDataVector;
    typedef RawDataVector::iterator PeakIterator;


    OptimizePick() :
      max_iteration_(0),
      eps_abs_(0),
      eps_rel_(0) {}

    OptimizePick(const struct OptimizationFunctions::PenaltyFactors & penalties_,
                 const int max_iteration_,
                 const double eps_abs_,
                 const double eps_rel_);

    ~OptimizePick();

    inline const struct OptimizationFunctions::PenaltyFactors & getPenalties() const { return penalties_; }
    inline struct OptimizationFunctions::PenaltyFactors & getPenalties() { return penalties_; }
    inline void setPenalties(const struct OptimizationFunctions::PenaltyFactors & penalties) { penalties_ = penalties; }

    inline UInt getNumberIterations() const { return max_iteration_; }
    inline unsigned int & getNumberIterations() { return max_iteration_; }
    inline void setNumberIterations(const int max_iteration) { max_iteration_ = max_iteration; }

    inline DoubleReal getMaxAbsError() const { return eps_abs_; }
    inline double & getMaxAbsError() { return eps_abs_; }
    inline void setMaxAbsError(double eps_abs) { eps_abs_ = eps_abs; }

    inline DoubleReal getMaxRelError() const { return eps_rel_; }
    inline double & getMaxRelError() { return eps_rel_; }
    inline void setMaxRelError(double eps_rel) { eps_rel_ = eps_rel; }

    void optimize(std::vector<PeakShape> & peaks, Data & data);


protected:
    struct OptimizationFunctions::PenaltyFactors penalties_;

    unsigned int max_iteration_;

    double eps_abs_;
    double eps_rel_;

//       /** @brief Returns the squared pearson coefficient.

//         Computes the correlation of the peak and the original data given by the peak enpoints.
//         If the value is near 1, the fitted peakshape and the raw data are expected to be very similar.
//     */
//     double correlate_(const PeakShape& peak,
//                                          double left_endpoint,
//                                          double right_endpoint,Data& data);

  };
}

#endif