SpectraMerger.h

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// Copyright (c) 2002-present, The OpenMS Team -- EKU Tuebingen, ETH Zurich, and FU Berlin
// SPDX-License-Identifier: BSD-3-Clause
//
// --------------------------------------------------------------------------
// $Maintainer: Chris Bielow $
// $Authors: Chris Bielow, Andreas Bertsch, Lars Nilse $
// --------------------------------------------------------------------------
//
#pragma once
 
#include <OpenMS/COMPARISON/CLUSTERING/ClusterAnalyzer.h>
#include <OpenMS/COMPARISON/CLUSTERING/ClusterHierarchical.h>
#include <OpenMS/COMPARISON/CLUSTERING/CompleteLinkage.h>
#include <OpenMS/COMPARISON/CLUSTERING/SingleLinkage.h>
#include <OpenMS/COMPARISON/SPECTRA/SpectrumAlignment.h>
#include <OpenMS/CONCEPT/LogStream.h>
#include <OpenMS/CONCEPT/ProgressLogger.h>
#include <OpenMS/DATASTRUCTURES/DefaultParamHandler.h>
#include <OpenMS/FILTERING/DATAREDUCTION/SplineInterpolatedPeaks.h>
#include <OpenMS/KERNEL/BaseFeature.h>
#include <OpenMS/KERNEL/RangeUtils.h>
#include <OpenMS/KERNEL/StandardTypes.h>
 
#include <vector>
 
namespace OpenMS
{
 
  class OPENMS_DLLAPI SpectraMerger :
    public DefaultParamHandler, public ProgressLogger
  {
 
protected:
 
    /* Determine distance between two spectra
 
      Distance is determined as
 
        (d_rt/rt_max_ + d_mz/mz_max_) / 2
 
    */
    class SpectraDistance_ :
      public DefaultParamHandler
    {
public:
      SpectraDistance_() :
        DefaultParamHandler("SpectraDistance")
      {
        defaults_.setValue("rt_tolerance", 10.0, "Maximal RT distance (in [s]) for two spectra's precursors.");
        defaults_.setValue("mz_tolerance", 1.0, "Maximal m/z distance (in Da) for two spectra's precursors.");
        defaultsToParam_(); // calls updateMembers_
      }
 
      void updateMembers_() override
      {
        rt_max_ = (double) param_.getValue("rt_tolerance");
        mz_max_ = (double) param_.getValue("mz_tolerance");
      }
 
      double getSimilarity(const double d_rt, const double d_mz) const
      {
        //     1 - distance
        return 1 - ((d_rt / rt_max_ + d_mz / mz_max_) / 2);
      }
 
      // measure of SIMILARITY (not distance, i.e. 1-distance)!!
      double operator()(const BaseFeature& first, const BaseFeature& second) const
      {
        // get RT distance:
        double d_rt = fabs(first.getRT() - second.getRT());
        double d_mz = fabs(first.getMZ() - second.getMZ());
 
        if (d_rt > rt_max_ || d_mz > mz_max_)
        {
          return 0;
        }
 
        // calculate similarity (0-1):
        double sim = getSimilarity(d_rt, d_mz);
 
        return sim;
      }
 
protected:
      double rt_max_;
      double mz_max_;
 
    }; // end of SpectraDistance
 
public:
 
    typedef std::map<Size, std::vector<Size> > MergeBlocks;
 
    typedef std::map<Size, std::vector<std::pair<Size, double> > > AverageBlocks;
 
    // @name Constructors and Destructors
    // @{
    SpectraMerger();
 
    SpectraMerger(const SpectraMerger& source);
 
    SpectraMerger(SpectraMerger&& source) = default;
 
    ~SpectraMerger() override;
    // @}
 
    // @name Operators
    // @{
    SpectraMerger& operator=(const SpectraMerger& source);
 
    SpectraMerger& operator=(SpectraMerger&& source) = default;
    // @}
 
    // @name Merging functions
    // @{
    template <typename MapType>
    void mergeSpectraBlockWise(MapType& exp)
    {
      IntList ms_levels = param_.getValue("block_method:ms_levels");
      Int rt_block_size(param_.getValue("block_method:rt_block_size"));
      double rt_max_length = (param_.getValue("block_method:rt_max_length"));
 
      if (rt_max_length == 0) // no rt restriction set?
      {
        rt_max_length = (std::numeric_limits<double>::max)(); // set max rt span to very large value
      }
 
      for (IntList::iterator it_mslevel = ms_levels.begin(); it_mslevel < ms_levels.end(); ++it_mslevel)
      {
        MergeBlocks spectra_to_merge;
        Size idx_block(0);
        SignedSize block_size_count(rt_block_size + 1);
        Size idx_spectrum(0);
        for (typename MapType::const_iterator it1 = exp.begin(); it1 != exp.end(); ++it1)
        {
          if (Int(it1->getMSLevel()) == *it_mslevel)
          {
            // block full if it contains a maximum number of scans or if maximum rt length spanned
            if (++block_size_count >= rt_block_size ||
                exp[idx_spectrum].getRT() - exp[idx_block].getRT() > rt_max_length)
            {
              block_size_count = 0;
              idx_block = idx_spectrum;
            }
            else
            {
              spectra_to_merge[idx_block].push_back(idx_spectrum);
            }
          }
 
          ++idx_spectrum;
        }
        // check if last block had sacrifice spectra
        if (block_size_count == 0) //block just got initialized
        {
          spectra_to_merge[idx_block] = std::vector<Size>();
        }
 
        // merge spectra, remove all old MS spectra and add new consensus spectra
        mergeSpectra_(exp, spectra_to_merge, *it_mslevel);
      }
 
      exp.sortSpectra();
    }
 
    template <typename MapType>
    void mergeSpectraPrecursors(MapType& exp)
    {
 
      // convert spectra's precursors to clusterizable data
      Size data_size;
      std::vector<BinaryTreeNode> tree;
      std::map<Size, Size> index_mapping;
      // local scope to save memory - we do not need the clustering stuff later
      {
        std::vector<BaseFeature> data;
 
        for (Size i = 0; i < exp.size(); ++i)
        {
          if (exp[i].getMSLevel() != 2)
          {
            continue;
          }
 
          // remember which index in distance data ==> experiment index
          index_mapping[data.size()] = i;
 
          // make cluster element
          BaseFeature bf;
          bf.setRT(exp[i].getRT());
          const auto& pcs = exp[i].getPrecursors(); 
          // keep the first Precursor
          if (pcs.empty())
          {
            throw Exception::MissingInformation(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION, String("Scan #") + String(i) + " does not contain any precursor information! Unable to cluster!");
          }
          if (pcs.size() > 1)
          {
            OPENMS_LOG_WARN << "More than one precursor found. Using first one!" << std::endl;
          }
          bf.setMZ(pcs[0].getMZ());
          data.push_back(bf);
        }
 
        data_size = data.size();
        SpectraDistance_ llc;
        llc.setParameters(param_.copy("precursor_method:", true));
        SingleLinkage sl;
        DistanceMatrix<float> dist; // will be filled
        ClusterHierarchical ch;
 
        // clustering ; threshold is implicitly at 1.0, i.e. distances of 1.0 (== similarity 0) will not be clustered
        ch.cluster<BaseFeature, SpectraDistance_>(data, llc, sl, tree, dist);
      }
 
      // extract the clusters
      ClusterAnalyzer ca;
      std::vector<std::vector<Size> > clusters;
      // count number of real tree nodes (not the -1 ones):
      Size node_count = 0;
      for (Size ii = 0; ii < tree.size(); ++ii)
      {
        if (tree[ii].distance >= 1)
        {
          tree[ii].distance = -1;  // manually set to disconnect, as SingleLinkage does not support it
        }
        if (tree[ii].distance != -1)
        {
          ++node_count;
        }
      }
      ca.cut(data_size - node_count, tree, clusters);
 
      // convert to blocks
      MergeBlocks spectra_to_merge;
 
      for (Size i_outer = 0; i_outer < clusters.size(); ++i_outer)
      {
        if (clusters[i_outer].size() <= 1)
        {
          continue;
        }
        // init block with first cluster element
        Size cl_index0 = clusters[i_outer][0];
        spectra_to_merge[index_mapping[cl_index0]] = std::vector<Size>();
        // add all other elements
        for (Size i_inner = 1; i_inner < clusters[i_outer].size(); ++i_inner)
        {
          spectra_to_merge[index_mapping[cl_index0]].push_back(index_mapping[clusters[i_outer][i_inner]]);
        }
      }
 
      // do it
      mergeSpectra_(exp, spectra_to_merge, 2);
 
      exp.sortSpectra();
    }
 
 
    template <typename MapType>
    void average(MapType& exp, const String& average_type, int ms_level = -1)
    {
      // MS level to be averaged
      if (ms_level < 0)
      {
        ms_level = param_.getValue("average_gaussian:ms_level");
        if (average_type == "tophat")
        {
          ms_level = param_.getValue("average_tophat:ms_level");
        }
      }
      
      // spectrum type (profile, centroid or automatic)
      std::string spectrum_type = param_.getValue("average_gaussian:spectrum_type");
      if (average_type == "tophat")
      {
        spectrum_type = std::string(param_.getValue("average_tophat:spectrum_type"));
      }
 
      // parameters for Gaussian averaging
      double fwhm(param_.getValue("average_gaussian:rt_FWHM"));
      double factor = -4 * log(2.0) / (fwhm * fwhm); // numerical factor within Gaussian
      double cutoff(param_.getValue("average_gaussian:cutoff"));
      //double precursor_mass_ppm = param_.getValue("average_gaussian:precursor_mass_tol");
      //int precursor_max_charge = param_.getValue("average_gaussian:precursor_max_charge");
 
      // parameters for Top-Hat averaging
      bool unit(param_.getValue("average_tophat:rt_unit") == "scans"); // true if RT unit is 'scans', false if RT unit is 'seconds'
      double range(param_.getValue("average_tophat:rt_range")); // range of spectra to be averaged over
      double range_seconds = range / 2; // max. +/- <range_seconds> seconds from master spectrum
      int range_scans = static_cast<int>(range); // in case of unit scans, the param is used as integer
      if ((range_scans % 2) == 0)
      {
        ++range_scans;
      }
      range_scans = (range_scans - 1) / 2; // max. +/- <range_scans> scans from master spectrum
 
      AverageBlocks spectra_to_average_over;
 
      // loop over RT
      int n(0); // spectrum index
      int cntr(0); // spectrum counter
      for (typename MapType::const_iterator it_rt = exp.begin(); it_rt != exp.end(); ++it_rt)
      {
        if (Int(it_rt->getMSLevel()) == ms_level)
        {
          int m; // spectrum index
          int steps;
          bool terminate_now;
          typename MapType::const_iterator it_rt_2;
 
          // go forward (start at next downstream spectrum; the current spectrum will be covered when looking backwards)
          steps = 0;
          m = n + 1;
          it_rt_2 = it_rt + 1;
          terminate_now = false;
          while (it_rt_2 != exp.end() && !terminate_now)
          {
            if (Int(it_rt_2->getMSLevel()) == ms_level)
            {
              double weight = 1;
              if (average_type == "gaussian")
              {
                //factor * (rt_2 -rt)^2
                double base = it_rt_2->getRT() - it_rt->getRT();
                weight = std::exp(factor * base * base);
              }
              std::pair<Size, double> p(m, weight);
              spectra_to_average_over[n].push_back(p);
 
              ++steps;
            }
            if (average_type == "gaussian")
            {
              // Gaussian
              double base = it_rt_2->getRT() - it_rt->getRT();
              terminate_now = std::exp(factor * base * base) < cutoff;
            }
            else if (unit)
            {
              // Top-Hat with RT unit = scans
              terminate_now = (steps > range_scans);
            }
            else
            {
              // Top-Hat with RT unit = seconds
              terminate_now = (std::abs(it_rt_2->getRT() - it_rt->getRT()) > range_seconds);
            }
            ++m;
            ++it_rt_2;
          }
 
          // go backward
          steps = 0;
          m = n;
          it_rt_2 = it_rt;
          terminate_now = false;
          while (it_rt_2 != exp.begin() && !terminate_now)
          {
            if (Int(it_rt_2->getMSLevel()) == ms_level)
            {
              double weight = 1;
              if (average_type == "gaussian")
              {
                double base = it_rt_2->getRT() - it_rt->getRT();
                weight = std::exp(factor * base * base);
              }
              std::pair<Size, double> p(m, weight);
              spectra_to_average_over[n].push_back(p);
 
              ++steps;
            }
            if (average_type == "gaussian")
            {
              // Gaussian
              double base = it_rt_2->getRT() - it_rt->getRT();
              terminate_now = std::exp(factor * base * base) < cutoff;
            }
            else if (unit)
            {
              // Top-Hat with RT unit = scans
              terminate_now = (steps > range_scans);
            }
            else
            {
              // Top-Hat with RT unit = seconds
              terminate_now = (std::abs(it_rt_2->getRT() - it_rt->getRT()) > range_seconds);
            }
            --m;
            --it_rt_2;
          }
          ++cntr;
        }
        ++n;
      }
 
      if (cntr == 0)
      {
        //return;
        throw Exception::InvalidParameter(__FILE__,
                                          __LINE__,
                                          OPENMS_PRETTY_FUNCTION,
                                          "Input mzML does not have any spectra of MS level specified by ms_level.");
      }
 
      // normalize weights
      for (AverageBlocks::iterator it = spectra_to_average_over.begin(); it != spectra_to_average_over.end(); ++it)
      {
        double sum(0.0);
        for (const auto& weight: it->second)
        {
          sum += weight.second;
        }
 
        for (auto& weight: it->second)
        {
          weight.second /= sum;
        }
      }
 
      // determine type of spectral data (profile or centroided)
      SpectrumSettings::SpectrumType type;
      if (spectrum_type == "automatic")
      {
        Size idx = spectra_to_average_over.begin()->first; // index of first spectrum to be averaged
        type = exp[idx].getType(true);
      }
      else if (spectrum_type == "profile")
      {
        type = SpectrumSettings::PROFILE;
      }
      else if (spectrum_type == "centroid")
      {
        type = SpectrumSettings::CENTROID;
      }
      else
      {
        throw Exception::InvalidParameter(__FILE__,__LINE__,OPENMS_PRETTY_FUNCTION, "Spectrum type has to be one of automatic, profile or centroid.");
      }
 
      // generate new spectra
      if (type == SpectrumSettings::CENTROID)
      {
        averageCentroidSpectra_(exp, spectra_to_average_over, ms_level);
      }
      else
      {
        averageProfileSpectra_(exp, spectra_to_average_over, ms_level);
      }
 
      exp.sortSpectra();
    }
 
    // @}
 
protected:
 
    template <typename MapType>
    void mergeSpectra_(MapType& exp, const MergeBlocks& spectra_to_merge, const UInt ms_level)
    {
      double mz_binning_width(param_.getValue("mz_binning_width"));
      std::string mz_binning_unit(param_.getValue("mz_binning_width_unit"));
 
      // merge spectra
      MapType merged_spectra;
 
      std::map<Size, Size> cluster_sizes;
      std::set<Size> merged_indices;
 
      // set up alignment
      SpectrumAlignment sas;
      Param p;
      p.setValue("tolerance", mz_binning_width);
      if (!(mz_binning_unit == "Da" || mz_binning_unit == "ppm"))
      {
        throw Exception::IllegalSelfOperation(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION);  // sanity check
      }
 
      p.setValue("is_relative_tolerance", mz_binning_unit == "Da" ? "false" : "true");
      sas.setParameters(p);
      std::vector<std::pair<Size, Size> > alignment;
 
      Size count_peaks_aligned(0);
      Size count_peaks_overall(0);
 
      // each BLOCK
      for (auto it = spectra_to_merge.begin(); it != spectra_to_merge.end(); ++it)
      {
        ++cluster_sizes[it->second.size() + 1]; // for stats
 
        typename MapType::SpectrumType consensus_spec = exp[it->first];
        consensus_spec.setMSLevel(ms_level);
 
        merged_indices.insert(it->first);
 
        double rt_average = consensus_spec.getRT();
        double precursor_mz_average = 0.0;
        Size precursor_count(0);
        if (!consensus_spec.getPrecursors().empty())
        {
          precursor_mz_average = consensus_spec.getPrecursors()[0].getMZ();
          ++precursor_count;
        }
 
        count_peaks_overall += consensus_spec.size();
 
        String consensus_native_id = consensus_spec.getNativeID();
 
        // block elements
        for (auto sit = it->second.begin(); sit != it->second.end(); ++sit)
        {
          consensus_spec.unify(exp[*sit]); // append meta info
          merged_indices.insert(*sit);
 
          rt_average += exp[*sit].getRT();
          if (ms_level >= 2 && exp[*sit].getPrecursors().size() > 0)
          {
            precursor_mz_average += exp[*sit].getPrecursors()[0].getMZ();
            ++precursor_count;
          }
 
          // add native ID to consensus native ID, comma separated
          consensus_native_id += ",";
          consensus_native_id += exp[*sit].getNativeID();
 
          // merge data points
          sas.getSpectrumAlignment(alignment, consensus_spec, exp[*sit]);
          count_peaks_aligned += alignment.size();
          count_peaks_overall += exp[*sit].size();
 
          Size align_index(0);
          Size spec_b_index(0);
 
          // sanity check for number of peaks
          Size spec_a = consensus_spec.size(), spec_b = exp[*sit].size(), align_size = alignment.size();
          for (auto pit = exp[*sit].begin(); pit != exp[*sit].end(); ++pit)
          {
            if (alignment.empty() || alignment[align_index].second != spec_b_index)
              // ... add unaligned peak
            {
              consensus_spec.push_back(*pit);
            }
            // or add aligned peak height to ALL corresponding existing peaks
            else
            {
              Size counter(0);
              Size copy_of_align_index(align_index);
 
              while (!alignment.empty() && 
                     copy_of_align_index < alignment.size() && 
                     alignment[copy_of_align_index].second == spec_b_index)
              {
                ++copy_of_align_index;
                ++counter;
              } // Count the number of peaks in a which correspond to a single b peak.
 
              while (!alignment.empty() &&
                     align_index < alignment.size() &&  
                     alignment[align_index].second == spec_b_index)
              {
                consensus_spec[alignment[align_index].first].setIntensity(consensus_spec[alignment[align_index].first].getIntensity() +
                    (pit->getIntensity() / (double)counter)); // add the intensity divided by the number of peaks
                ++align_index; // this aligned peak was explained, wait for next aligned peak ...
                if (align_index == alignment.size())
                {
                  alignment.clear();  // end reached -> avoid going into this block again
                }
              }
              align_size = align_size + 1 - counter; //Decrease align_size by number of
            }
            ++spec_b_index;
          }
          consensus_spec.sortByPosition(); // sort, otherwise next alignment will fail
          if (spec_a + spec_b - align_size != consensus_spec.size())
          {
            OPENMS_LOG_WARN << "wrong number of features after merge. Expected: " << spec_a + spec_b - align_size << " got: " << consensus_spec.size() << "\n";
          }
        }
        rt_average /= it->second.size() + 1;
        consensus_spec.setRT(rt_average);
        
        // set new consensus native ID
        consensus_spec.setNativeID(consensus_native_id);
 
        if (ms_level >= 2)
        {
          if (precursor_count)
          {
            precursor_mz_average /= precursor_count;
          }
          auto& pcs = consensus_spec.getPrecursors();
          pcs.resize(1);
          pcs[0].setMZ(precursor_mz_average);
          consensus_spec.setPrecursors(pcs);
        }
 
        if (consensus_spec.empty())
        {
          continue;
        }
        else
        {
          merged_spectra.addSpectrum(std::move(consensus_spec));
        }
      }
 
      OPENMS_LOG_INFO << "Cluster sizes:\n";
      for (const auto& cl_size : cluster_sizes)
      {
        OPENMS_LOG_INFO << "  size " << cl_size.first << ": " << cl_size.second << "x\n";
      }
 
      char buffer[200];
      sprintf(buffer, "%d/%d (%.2f %%) of blocked spectra", (int)count_peaks_aligned,
              (int)count_peaks_overall, float(count_peaks_aligned) / float(count_peaks_overall) * 100.);
      OPENMS_LOG_INFO << "Number of merged peaks: " << String(buffer) << "\n";
 
      // remove all spectra that were within a cluster
      typename MapType::SpectrumType empty_spec;
      MapType exp_tmp;
      for (Size i = 0; i < exp.size(); ++i)
      {
        if (merged_indices.count(i) == 0) // save unclustered ones
        {
          exp_tmp.addSpectrum(exp[i]);
          exp[i] = empty_spec;
        }
      }
 
      //Meta_Data will not be cleared
      exp.clear(false);
      exp.getSpectra().insert(exp.end(), std::make_move_iterator(exp_tmp.begin()),
                                         std::make_move_iterator(exp_tmp.end()));
 
      // ... and add consensus spectra
      exp.getSpectra().insert(exp.end(), std::make_move_iterator(merged_spectra.begin()),
                                         std::make_move_iterator(merged_spectra.end()));
 
    }
 
    template <typename MapType>
    void averageProfileSpectra_(MapType& exp, const AverageBlocks& spectra_to_average_over, const UInt ms_level)
    {
      MapType exp_tmp; // temporary experiment for averaged spectra
 
      double mz_binning_width(param_.getValue("mz_binning_width"));
      std::string mz_binning_unit(param_.getValue("mz_binning_width_unit"));
 
      unsigned progress = 0;
      std::stringstream progress_message;
      progress_message << "averaging profile spectra of MS level " << ms_level;
      startProgress(0, spectra_to_average_over.size(), progress_message.str());
 
      // loop over blocks
      for (AverageBlocks::const_iterator it = spectra_to_average_over.begin(); it != spectra_to_average_over.end(); ++it)
      {
        setProgress(++progress);
 
        // loop over spectra in blocks
        std::vector<double> mz_positions_all; // m/z positions from all spectra
        for (const auto& spec : it->second)
        {
          // loop over m/z positions
          for (typename MapType::SpectrumType::ConstIterator it_mz = exp[spec.first].begin(); it_mz < exp[spec.first].end(); ++it_mz)
          {
            mz_positions_all.push_back(it_mz->getMZ());
          }
        }
 
        sort(mz_positions_all.begin(), mz_positions_all.end());
 
        std::vector<double> mz_positions; // positions at which the averaged spectrum should be evaluated
        std::vector<double> intensities;
        double last_mz = std::numeric_limits<double>::min(); // last m/z position pushed through from mz_position to mz_position_2
        double delta_mz(mz_binning_width); // for m/z unit Da
        for (const auto mz_pos : mz_positions_all)
        {
          if (mz_binning_unit == "ppm")
          {
            delta_mz = mz_binning_width * mz_pos / 1000000;
          }
 
          if ((mz_pos - last_mz) > delta_mz)
          {
            mz_positions.push_back(mz_pos);
            intensities.push_back(0.0);
            last_mz = mz_pos;
          }
        }
 
        // loop over spectra in blocks
        for (const auto& spec : it->second)
        {
          SplineInterpolatedPeaks spline(exp[spec.first]);
          SplineInterpolatedPeaks::Navigator nav = spline.getNavigator();
 
          // loop over m/z positions
          for (Size i = spline.getPosMin(); i < mz_positions.size(); ++i)
          {
            if ((spline.getPosMin() < mz_positions[i]) && (mz_positions[i] < spline.getPosMax()))
            {
              intensities[i] += nav.eval(mz_positions[i]) * (spec.second); // spline-interpolated intensity * weight
            }
          }
        }
 
        // update spectrum
        typename MapType::SpectrumType average_spec = exp[it->first];
        average_spec.clear(false); // Precursors are part of the meta data, which are not deleted.
 
        // refill spectrum
        for (Size i = 0; i < mz_positions.size(); ++i)
        {
          typename MapType::PeakType peak;
          peak.setMZ(mz_positions[i]);
          peak.setIntensity(intensities[i]);
          average_spec.push_back(peak);
        }
 
        // store spectrum temporarily
        exp_tmp.addSpectrum(std::move(average_spec));
      }
 
      endProgress();
 
      // loop over blocks
      int n(0);
      for (AverageBlocks::const_iterator it = spectra_to_average_over.begin(); it != spectra_to_average_over.end(); ++it)
      {
        exp[it->first] = exp_tmp[n];
        ++n;
      }
    }
 
    template <typename MapType>
    void averageCentroidSpectra_(MapType& exp, const AverageBlocks& spectra_to_average_over, const UInt ms_level)
    {
      MapType exp_tmp; // temporary experiment for averaged spectra
 
      double mz_binning_width(param_.getValue("mz_binning_width"));
      std::string mz_binning_unit(param_.getValue("mz_binning_width_unit"));
 
      unsigned progress = 0;
      ProgressLogger logger;
      std::stringstream progress_message;
      progress_message << "averaging centroid spectra of MS level " << ms_level;
      logger.startProgress(0, spectra_to_average_over.size(), progress_message.str());
 
      // loop over blocks
      for (AverageBlocks::const_iterator it = spectra_to_average_over.begin(); it != spectra_to_average_over.end(); ++it)
      {
        logger.setProgress(++progress);
 
        // collect peaks from all spectra
        // loop over spectra in blocks
        std::vector<std::pair<double, double> > mz_intensity_all; // m/z positions and peak intensities from all spectra
        for (const auto& weightedMZ: it->second)
        {
          // loop over m/z positions
          for (typename MapType::SpectrumType::ConstIterator it_mz = exp[weightedMZ.first].begin(); it_mz < exp[weightedMZ.first].end(); ++it_mz)
          {
            std::pair<double, double> mz_intensity(it_mz->getMZ(), (it_mz->getIntensity() * weightedMZ.second)); // m/z, intensity * weight
            mz_intensity_all.push_back(mz_intensity);
          }
        }
 
        sort(mz_intensity_all.begin(), mz_intensity_all.end());
 
        // generate new spectrum
        std::vector<double> mz_new;
        std::vector<double> intensity_new;
        double last_mz = std::numeric_limits<double>::min();
        double delta_mz = mz_binning_width;
        double sum_mz(0);
        double sum_intensity(0);
        Size count(0);
        for (const auto& mz_pos : mz_intensity_all)
        {
          if (mz_binning_unit == "ppm")
          {
            delta_mz = mz_binning_width * (mz_pos.first) / 1000000;
          }
 
          if (((mz_pos.first - last_mz) > delta_mz) && (count > 0))
          {
            mz_new.push_back(sum_mz / count);
            intensity_new.push_back(sum_intensity); // intensities already weighted
 
            sum_mz = 0;
            sum_intensity = 0;
 
            last_mz = mz_pos.first;
            count = 0;
          }
 
          sum_mz += mz_pos.first;
          sum_intensity += mz_pos.second;
          ++count;
        }
        if (count > 0)
        {
          mz_new.push_back(sum_mz / count);
          intensity_new.push_back(sum_intensity); // intensities already weighted
        }
 
        // update spectrum
        typename MapType::SpectrumType average_spec = exp[it->first];
        average_spec.clear(false); // Precursors are part of the meta data, which are not deleted.
 
        // refill spectrum
        for (Size i = 0; i < mz_new.size(); ++i)
        {
          typename MapType::PeakType peak;
          peak.setMZ(mz_new[i]);
          peak.setIntensity(intensity_new[i]);
          average_spec.push_back(peak);
        }
 
        // store spectrum temporarily
        exp_tmp.addSpectrum(std::move(average_spec));
      }
 
      logger.endProgress();
 
      // loop over blocks
      int n(0);
      for (const auto& spectral_index : spectra_to_average_over)
      {
        exp[spectral_index.first] = std::move(exp_tmp[n]);
        ++n;
      }
    }
  };
}