text: // --------------------------------------------------------------------------
  //                   OpenMS -- Open-Source Mass Spectrometry
  // --------------------------------------------------------------------------
  // Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,
  // ETH Zurich, and Freie Universitaet Berlin 2002-2021.
  //
  // This software is released under a three-clause BSD license:
  //  * Redistributions of source code must retain the above copyright
  //    notice, this list of conditions and the following disclaimer.
  //  * Redistributions in binary form must reproduce the above copyright
  //    notice, this list of conditions and the following disclaimer in the
  //    documentation and/or other materials provided with the distribution.
  //  * Neither the name of any author or any participating institution
  //    may be used to endorse or promote products derived from this software
  //    without specific prior written permission.
  // For a full list of authors, refer to the file AUTHORS.
  // --------------------------------------------------------------------------
  // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
  // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
  // ARE DISCLAIMED. IN NO EVENT SHALL ANY OF THE AUTHORS OR THE CONTRIBUTING
  // INSTITUTIONS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
  // EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
  // PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
  // OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
  // WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
  // OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
  // ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  //
  // --------------------------------------------------------------------------
  // $Maintainer: Timo Sachsenberg $
  // $Authors: $
  // --------------------------------------------------------------------------
  
  #include <OpenMS/TRANSFORMATIONS/FEATUREFINDER/GaussFitter1D.h>
  
  #include <OpenMS/CONCEPT/Factory.h>
  
  namespace OpenMS
  {
    GaussFitter1D::GaussFitter1D() :
      MaxLikeliFitter1D()
    {
      setName(getProductName());
  
      defaults_.setValue("statistics:variance", 1.0, "Variance of the model.", {"advanced"});
      defaults_.setValue("statistics:mean", 1.0, "Mean value of the model.", {"advanced"});
      defaultsToParam_();
    }
  
    GaussFitter1D::GaussFitter1D(const GaussFitter1D& source) :
      MaxLikeliFitter1D(source)
    {
      updateMembers_();
    }
  
    GaussFitter1D::~GaussFitter1D()
    {
    }
  
    GaussFitter1D& GaussFitter1D::operator=(const GaussFitter1D& source)
    {
      if (&source == this)
      {
        return *this;
      }
      MaxLikeliFitter1D::operator=(source);
      updateMembers_();
  
      return *this;
    }
  
    GaussFitter1D::QualityType GaussFitter1D::fit1d(const RawDataArrayType& set, std::unique_ptr<InterpolationModel>& model)
    {
      // Calculate bounding box
      CoordinateType min_bb = set[0].getPos(), max_bb = set[0].getPos();
      for (UInt pos = 1; pos < set.size(); ++pos)
      {
        CoordinateType tmp = set[pos].getPos();
        if (min_bb > tmp)
        {
          min_bb = tmp;
        }
        if (max_bb < tmp)
        {
          max_bb = tmp;
        }
      }
  
      // Enlarge the bounding box by a few multiples of the standard deviation
      const CoordinateType stdev = sqrt(statistics_.variance()) * tolerance_stdev_box_;
      min_bb -= stdev;
      max_bb += stdev;
  
  
      // build model
      model = std::unique_ptr<InterpolationModel>(dynamic_cast<InterpolationModel*>(Factory<BaseModel<1>>::create("GaussModel")));
      model->setInterpolationStep(interpolation_step_);
  
      Param tmp;
      tmp.setValue("bounding_box:min", min_bb);
      tmp.setValue("bounding_box:max", max_bb);
      tmp.setValue("statistics:mean", statistics_.mean());
      tmp.setValue("statistics:variance", statistics_.variance());
      model->setParameters(tmp);
  
      // fit offset
      QualityType quality;
      quality = fitOffset_(model, set, stdev, stdev, interpolation_step_);
      if (std::isnan(quality))
      {
        quality = -1.0;
      }
      return quality;
    }
  
    void GaussFitter1D::updateMembers_()
    {
      MaxLikeliFitter1D::updateMembers_();
      statistics_.setMean(param_.getValue("statistics:mean"));
      statistics_.setVariance(param_.getValue("statistics:variance"));
    }
  
  }
  