SignalToNoiseEstimatorMedian.h

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// Copyright (c) 2002-present, OpenMS Inc. -- EKU Tuebingen, ETH Zurich, and FU Berlin
// SPDX-License-Identifier: BSD-3-Clause
//
// --------------------------------------------------------------------------
// $Maintainer: Chris Bielow $
// $Authors: $
// --------------------------------------------------------------------------
//
 
#pragma once
 
 
#include <OpenMS/PROCESSING/NOISEESTIMATION/SignalToNoiseEstimator.h>
#include <OpenMS/CONCEPT/LogStream.h>
#include <OpenMS/CONCEPT/Exception.h>
#include <OpenMS/DATASTRUCTURES/TypeAliases.h>
#include <vector>
#include <algorithm> //for std::max_element
 
namespace OpenMS
{
  template <typename Container = MSSpectrum>
  class SignalToNoiseEstimatorMedian :
    public SignalToNoiseEstimator<Container>
  {
 
public:
 
    enum IntensityThresholdCalculation {MANUAL = -1, AUTOMAXBYSTDEV = 0, AUTOMAXBYPERCENT = 1};
 
    using SignalToNoiseEstimator<Container>::stn_estimates_;
    using SignalToNoiseEstimator<Container>::defaults_;
    using SignalToNoiseEstimator<Container>::param_;
 
    typedef typename SignalToNoiseEstimator<Container>::PeakIterator PeakIterator;
    typedef typename SignalToNoiseEstimator<Container>::PeakType PeakType;
 
    typedef typename SignalToNoiseEstimator<Container>::GaussianEstimate GaussianEstimate;
 
    inline SignalToNoiseEstimatorMedian()
    {
      //set the name for DefaultParamHandler error messages
      this->setName("SignalToNoiseEstimatorMedian");
 
      defaults_.setValue("max_intensity", -1, "maximal intensity considered for histogram construction. By default, it will be calculated automatically (see auto_mode)." \
                                              " Only provide this parameter if you know what you are doing (and change 'auto_mode' to '-1')!" \
                                              " All intensities EQUAL/ABOVE 'max_intensity' will be added to the LAST histogram bin." \
                                              " If you choose 'max_intensity' too small, the noise estimate might be too small as well. " \
                                              " If chosen too big, the bins become quite large (which you could counter by increasing 'bin_count', which increases runtime)." \
                                              " In general, the Median-S/N estimator is more robust to a manual max_intensity than the MeanIterative-S/N.", {"advanced"});
      defaults_.setMinInt("max_intensity", -1);
 
      defaults_.setValue("auto_max_stdev_factor", 3.0, "parameter for 'max_intensity' estimation (if 'auto_mode' == 0): mean + 'auto_max_stdev_factor' * stdev", {"advanced"});
      defaults_.setMinFloat("auto_max_stdev_factor", 0.0);
      defaults_.setMaxFloat("auto_max_stdev_factor", 999.0);
 
      defaults_.setValue("auto_max_percentile", 95, "parameter for 'max_intensity' estimation (if 'auto_mode' == 1): auto_max_percentile th percentile", {"advanced"});
      defaults_.setMinInt("auto_max_percentile", 0);
      defaults_.setMaxInt("auto_max_percentile", 100);
 
      defaults_.setValue("auto_mode", 0, "method to use to determine maximal intensity: -1 --> use 'max_intensity'; 0 --> 'auto_max_stdev_factor' method (default); 1 --> 'auto_max_percentile' method", {"advanced"});
      defaults_.setMinInt("auto_mode", -1);
      defaults_.setMaxInt("auto_mode", 1);
 
      defaults_.setValue("win_len", 200.0, "window length in Thomson");
      defaults_.setMinFloat("win_len", 1.0);
 
      defaults_.setValue("bin_count", 30, "number of bins for intensity values");
      defaults_.setMinInt("bin_count", 3);
 
      defaults_.setValue("min_required_elements", 10, "minimum number of elements required in a window (otherwise it is considered sparse)");
      defaults_.setMinInt("min_required_elements", 1);
 
      defaults_.setValue("noise_for_empty_window", std::pow(10.0, 20), "noise value used for sparse windows", {"advanced"});
 
      defaults_.setValue("write_log_messages", "true", "Write out log messages in case of sparse windows or median in rightmost histogram bin");
      defaults_.setValidStrings("write_log_messages", {"true","false"});
 
      SignalToNoiseEstimator<Container>::defaultsToParam_();
    }
 
    inline SignalToNoiseEstimatorMedian(const SignalToNoiseEstimatorMedian & source) :
      SignalToNoiseEstimator<Container>(source)
    {
      updateMembers_();
    }
 
    inline SignalToNoiseEstimatorMedian & operator=(const SignalToNoiseEstimatorMedian & source)
    {
      if (&source == this) return *this;
 
      SignalToNoiseEstimator<Container>::operator=(source);
      updateMembers_();
      return *this;
    }
 
 
    ~SignalToNoiseEstimatorMedian() override
    {}
 
    double getSparseWindowPercent() const
    {
      return sparse_window_percent_;
    }
 
    double getHistogramRightmostPercent() const
    {
      return histogram_oob_percent_;
    }
 
protected:
 
 
    void computeSTN_(const Container& c) override
    {
      //first element in the scan
      PeakIterator scan_first_ = c.begin();
      //last element in the scan
      PeakIterator scan_last_ = c.end();
 
      // reset counter for sparse windows
      sparse_window_percent_ = 0;
      // reset counter for histogram overflow
      histogram_oob_percent_ = 0;
 
      // reset the results
      stn_estimates_.clear();
      stn_estimates_.resize(c.size());
 
      // maximal range of histogram needs to be calculated first
      if (auto_mode_ == AUTOMAXBYSTDEV)
      {
        // use MEAN+auto_max_intensity_*STDEV as threshold
        GaussianEstimate gauss_global = SignalToNoiseEstimator<Container>::estimate_(scan_first_, scan_last_);
        max_intensity_ = gauss_global.mean + std::sqrt(gauss_global.variance) * auto_max_stdev_Factor_;
      }
      else if (auto_mode_ == AUTOMAXBYPERCENT)
      {
        // get value at "auto_max_percentile_"th percentile
        // we use a histogram approach here as well.
        if ((auto_max_percentile_ < 0) || (auto_max_percentile_ > 100))
        {
          std::string s = StringUtils::toStr(auto_max_percentile_);
          throw Exception::InvalidValue(__FILE__,
                                        __LINE__,
                                        OPENMS_PRETTY_FUNCTION,
                                        "auto_mode is on AUTOMAXBYPERCENT! auto_max_percentile is not in [0,100]. Use setAutoMaxPercentile(<value>) to change it!",
                                        s);
        }
 
        std::vector<int> histogram_auto(100, 0);
 
        // find maximum of current scan
        auto maxIt = std::max_element(c.begin(), c.end() ,[](const PeakType& a, const PeakType& b){ return a.getIntensity() > b.getIntensity();});
        typename PeakType::IntensityType maxInt = maxIt->getIntensity();
 
        double bin_size = maxInt / 100;
 
        // fill histogram
        for(const auto& peak : c)
        {
            ++histogram_auto[(int) ((peak.getIntensity() - 1) / bin_size)];
        }
 
        // add up element counts in histogram until ?th percentile is reached
        int elements_below_percentile = (int) (auto_max_percentile_ * c.size() / 100);
        int elements_seen = 0;
        int i = -1;
        PeakIterator run = scan_first_;
 
        while (run != scan_last_ && elements_seen < elements_below_percentile)
        {
          ++i;
          elements_seen += histogram_auto[i];
          ++run;
        }
 
        max_intensity_ = (((double)i) + 0.5) * bin_size;
      }
      else //if (auto_mode_ == MANUAL)
      {
        if (max_intensity_ <= 0)
        {
          std::string s = StringUtils::toStr(max_intensity_);
          throw Exception::InvalidValue(__FILE__,
                                        __LINE__,
                                        OPENMS_PRETTY_FUNCTION,
                                        "auto_mode is on MANUAL! max_intensity is <=0. Needs to be positive! Use setMaxIntensity(<value>) or enable auto_mode!",
                                        s);
        }
      }
 
      if (max_intensity_ < 0)
      {
        OPENMS_LOG_WARN << "SignalToNoiseEstimatorMedian: the max_intensity_ value should be positive! " << max_intensity_ << std::endl;
        return;
      }
 
      PeakIterator window_pos_center  = scan_first_;
      PeakIterator window_pos_borderleft = scan_first_;
      PeakIterator window_pos_borderright = scan_first_;
 
      double window_half_size = win_len_ / 2;
      double bin_size = std::max(1.0, max_intensity_ / bin_count_); // at least size of 1 for intensity bins
      int bin_count_minus_1 = bin_count_ - 1;
 
      std::vector<int> histogram(bin_count_, 0);
      std::vector<double> bin_value(bin_count_, 0);
      // calculate average intensity that is represented by a bin
      for (int bin = 0; bin < bin_count_; bin++)
      {
        histogram[bin] = 0;
        bin_value[bin] = (bin + 0.5) * bin_size;
      }
      // bin in which a datapoint would fall
      int to_bin = 0;
 
      // index of bin where the median is located
      int median_bin = 0;
      // additive number of elements from left to x in histogram
      int element_inc_count = 0;
 
      // tracks elements in current window, which may vary because of unevenly spaced data
      int elements_in_window = 0;
      // number of windows
      int window_count = 0;
 
      // number of elements where we find the median
      int element_in_window_half = 0;
 
      double noise;    // noise value of a datapoint
 
      SignalToNoiseEstimator<Container>::startProgress(0, c.size(), "noise estimation of data");
 
      // MAIN LOOP
      while (window_pos_center != scan_last_)
      {
 
        // erase all elements from histogram that will leave the window on the LEFT side
        while ((*window_pos_borderleft).getPos() < (*window_pos_center).getPos() - window_half_size)
        {
          to_bin = std::max(std::min<int>((int)((*window_pos_borderleft).getIntensity() / bin_size), bin_count_minus_1), 0);
          --histogram[to_bin];
          --elements_in_window;
          ++window_pos_borderleft;
        }
 
        // add all elements to histogram that will enter the window on the RIGHT side
        while ((window_pos_borderright != scan_last_)
              && ((*window_pos_borderright).getPos() <= (*window_pos_center).getPos() + window_half_size))
        {
          //std::cerr << (*window_pos_borderright).getIntensity() << " " << bin_size << " " << bin_count_minus_1 << std::endl;
          to_bin = std::max(std::min<int>((int)((*window_pos_borderright).getIntensity() / bin_size), bin_count_minus_1), 0);
          ++histogram[to_bin];
          ++elements_in_window;
          ++window_pos_borderright;
        }
 
        if (elements_in_window < min_required_elements_)
        {
          noise = noise_for_empty_window_;
          ++sparse_window_percent_;
        }
        else
        {
          // find bin i where ceil[elements_in_window/2] <= sum_c(0..i){ histogram[c] }
          median_bin = -1;
          element_inc_count = 0;
          element_in_window_half = (elements_in_window + 1) / 2;
          while (median_bin < bin_count_minus_1 && element_inc_count < element_in_window_half)
          {
            ++median_bin;
            element_inc_count += histogram[median_bin];
          }
 
          // increase the error count
          if (median_bin == bin_count_minus_1) {++histogram_oob_percent_; }
 
          // Interpolate within the median bin instead of just reporting its center.
          // Using the bin center alone means the noise estimate can only take one of
          // 'bin_count_' discrete values; a tiny, insignificant shift in which side of
          // a bin boundary the cumulative-median crossing falls on then causes a full
          // bin-width jump in the reported noise, even though the underlying data barely
          // changed. Assuming points are uniformly distributed within the bin, we can
          // instead estimate where inside the bin the crossing actually occurs.
          const int elements_in_median_bin = histogram[median_bin];
          double noise_estimate;
          if (elements_in_median_bin > 0)
          {
            const int elements_before_median_bin = element_inc_count - elements_in_median_bin;
            const double median_bin_lower_edge = median_bin * bin_size;
            noise_estimate = median_bin_lower_edge
              + ((double)(element_in_window_half - elements_before_median_bin) / elements_in_median_bin) * bin_size;
          }
          else // only possible if the rightmost bin was hit while empty (already flagged above)
          {
            noise_estimate = bin_value[median_bin];
          }
 
          // just avoid division by 0
          noise = std::max(1.0, noise_estimate);
        }
 
        // store result
        stn_estimates_[window_count] = (*window_pos_center).getIntensity() / noise;
 
 
        // advance the window center by one datapoint
        ++window_pos_center;
        ++window_count;
        // update progress
        SignalToNoiseEstimator<Container>::setProgress(window_count);
 
      } // end while
 
      SignalToNoiseEstimator<Container>::endProgress();
 
      sparse_window_percent_ = sparse_window_percent_ * 100 / window_count;
      histogram_oob_percent_ = histogram_oob_percent_ * 100 / window_count;
 
      // warn if percentage of sparse windows is above 20%
      if (sparse_window_percent_ > 20 && write_log_messages_)
      {
        OPENMS_LOG_WARN << "WARNING in SignalToNoiseEstimatorMedian: "
                 << sparse_window_percent_
                 << "% of all windows were sparse. You should consider increasing 'win_len' or decreasing 'min_required_elements'"
                 << std::endl;
      }
 
      // warn if percentage of possibly wrong median estimates is above 1%
      if (histogram_oob_percent_ > 1 && write_log_messages_)
      {
        OPENMS_LOG_WARN << "WARNING in SignalToNoiseEstimatorMedian: "
                 << histogram_oob_percent_
                 << "% of all Signal-to-Noise estimates are too high, because the median was found in the rightmost histogram-bin. "
                 << "You should consider increasing 'max_intensity' (and maybe 'bin_count' with it, to keep bin width reasonable)"
                 << std::endl;
      }
 
    } // end of shiftWindow_
 
    void updateMembers_() override
    {
      max_intensity_           = (double)param_.getValue("max_intensity");
      auto_max_stdev_Factor_   = (double)param_.getValue("auto_max_stdev_factor");
      auto_max_percentile_     = param_.getValue("auto_max_percentile");
      auto_mode_               = param_.getValue("auto_mode");
      win_len_                 = (double)param_.getValue("win_len");
      bin_count_               = param_.getValue("bin_count");
      min_required_elements_   = param_.getValue("min_required_elements");
      noise_for_empty_window_  = (double)param_.getValue("noise_for_empty_window");
      write_log_messages_      = (bool)param_.getValue("write_log_messages").toBool();
      stn_estimates_.clear();
    }
 
    double max_intensity_;
    double auto_max_stdev_Factor_;
    double auto_max_percentile_;
    int    auto_mode_;
    double win_len_;
    int    bin_count_;
    int min_required_elements_;
    double noise_for_empty_window_;
 
    // whether to write out log messages in the case of failure
    bool write_log_messages_;
 
    // counter for sparse windows
    double sparse_window_percent_;
    // counter for histogram overflow
    double histogram_oob_percent_;
 
 
  };
 
} // namespace OpenMS