PedAlgoRollingMean.cxx

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//
//  PedAlgoRollingMean source
//

#ifndef larana_OPTICALDETECTOR_PEDALGOROLLINGMEAN_CXX
#define larana_OPTICALDETECTOR_PEDALGOROLLINGMEAN_CXX

#include "PedAlgoRollingMean.h"
#include "UtilFunc.h"

//#include <ctime>
#include <iostream>
namespace pmtana{

  //*****************************************************************
  PedAlgoRollingMean::PedAlgoRollingMean(const std::string name)
    : PMTPedestalBase(name)
      //*****************************************************************
  {
    srand(static_cast<unsigned int>(time(0)));
  }

  //**************************************************************************
  PedAlgoRollingMean::PedAlgoRollingMean(const fhicl::ParameterSet &pset,
  //PedAlgoRollingMean::PedAlgoRollingMean(const ::fcllite::PSet &pset,
                                             const std::string name)
    : PMTPedestalBase(name)
      //############################################################
  {

    _sample_size    = pset.get<size_t>("SampleSize");
    _max_sigma      = pset.get<float> ("MaxSigma");
    _ped_range_max  = pset.get<float> ("PedRangeMax");
    _ped_range_min  = pset.get<float> ("PedRangeMin");

    // _range          = pset.get<int>   ("RandomRange");
    // _divisions      = pset.get<double>("RandomRangeDivisions");
    _threshold      = pset.get<double>("Threshold");
    _diff_threshold = pset.get<double>("DiffBetweenGapsThreshold");
    _diff_adc_count = pset.get<double>("DiffADCCounts");

    _n_presamples   = pset.get<int>("NPrePostSamples");
    
    //_random_shift   = pset.get<double>("RandomRangeShift");
    // Random seed number generator
    //srand(static_cast<unsigned int>(time(0)));
  }

  //*******************************************
  PedAlgoRollingMean::~PedAlgoRollingMean()
  //*******************************************
  {}

  //****************************************************************************
  bool PedAlgoRollingMean::ComputePedestal( const pmtana::Waveform_t& wf,
                                            pmtana::PedestalMean_t&   mean_v,
                                            pmtana::PedestalSigma_t&  sigma_v)
  //****************************************************************************
  {

    // parameters
    if(wf.size()<=(_sample_size * 2))
      return false;

    const size_t window_size = _sample_size*2;

    // middle mean
    for(size_t i=0; i< wf.size(); ++i) {

      mean_v[i]  = 0;
      sigma_v[i] = 0;

      if( i < _sample_size || i >= (wf.size() - _sample_size) ) continue;

      mean_v[i]  = mean (wf,i - _sample_size,window_size);
      sigma_v[i] = std  (wf,mean_v[i],i - _sample_size,window_size);
    }

    // front mean
    for(size_t i=0; i<_sample_size; ++i) {

      mean_v[i]  = mean_v [_sample_size];
      sigma_v[i] = sigma_v[_sample_size];
      
    }
    // tail mean
    for(size_t i=(wf.size() - _sample_size); i<wf.size(); ++i) {

      mean_v[i]  = mean_v [wf.size() - _sample_size -1];
      sigma_v[i] = sigma_v[wf.size() - _sample_size -1];

    }
    
    float  best_sigma = 1.1e9;
    size_t best_sigma_index = 0;
    size_t num_good_adc = 0;

    for(size_t i=0; i<sigma_v.size(); ++i) {
      // Only consider adcs which mean is in the allowed range
      auto const& mean  = mean_v[i];
      
      if( mean < _ped_range_min || mean > _ped_range_max ) continue;

      auto const& sigma = sigma_v[i];
      if(sigma < best_sigma) {
        best_sigma = sigma;
        best_sigma_index = i;
      }

      if(sigma < _max_sigma) num_good_adc += 1;
    }


    if( num_good_adc < 1 ) {
      std::cerr << "\033[93m<<" << __FUNCTION__ << ">>\033[00m Could not find good pedestal at all..." << std::endl;
      return false;
    }

    // If not enough # of good mean indices, use the best guess within this waveform
    if(best_sigma > _max_sigma || num_good_adc < 3) {
      for(size_t i=0; i<mean_v.size(); ++i) {
        mean_v[i]  = mean_v.at  ( best_sigma_index );
        sigma_v[i] = sigma_v.at ( best_sigma_index );
      }

      return true;
    }

    
    // Else do extrapolation, or random seed depending on what we find...

    unsigned nbins = 1000;  

    const auto mode_mean  = BinnedMaxOccurrence(mean_v ,nbins);
    const auto mode_sigma = BinnedMaxOccurrence(sigma_v,nbins);

    //auto mode_mean  = BinnedMaxTH1D(mean_v ,nbins);
    //auto mode_sigma = BinnedMaxTH1D(sigma_v,nbins);

    //std::cout<<mode_mean<<" +/- "<<mode_sigma<<std::endl;

    _diff_threshold *= mode_sigma;
    
    double diff_cutoff = _diff_threshold < _diff_adc_count ? _diff_adc_count : _diff_threshold;

    int last_good_index = -1;

    for(size_t i=0; i < wf.size(); ++i) {
      
      auto const mean  = mean_v[i];
      auto const sigma = sigma_v[i];

      // if(sigma <= _max_sigma && mean < _ped_range_max && mean > _ped_range_min) {
      // not sure if this works well for basline that is "linear" seen by David K
      
      if(sigma <= _threshold * mode_sigma && fabs(mean - mode_mean) <= _threshold * mode_sigma) {

        if(last_good_index<0) {
          last_good_index = (int)i;
          continue;
        }

        if( ( last_good_index + 1 ) < (int) i ) {

          auto diff = fabs(mean_v.at(last_good_index) - mean);

          if ( diff >  diff_cutoff) {
            //this should become generic interpolation function, for now lets leave.
            float slope = (mean - mean_v.at(last_good_index)) / (float(i - last_good_index));
            
            for(size_t j = last_good_index + 1; j < i; ++j) {
              mean_v.at(j)  = slope * ( float(j - last_good_index) ) + mean_v.at(last_good_index);
              sigma_v.at(j) = mode_sigma;
            }
          }
          else {
            //difference roughly the same lets fill with constant baseline
            
            auto presample_mean  = edge_aware_mean(wf,last_good_index - _n_presamples, last_good_index);
            auto postsample_mean = edge_aware_mean(wf,i, _n_presamples);
            
            auto diff_pre  = fabs(presample_mean  - mode_mean);
            auto diff_post = fabs(postsample_mean - mode_mean);

            auto constant_mean = diff_pre <= diff_post ? presample_mean : postsample_mean;
            
            for(size_t j = last_good_index + 1; j < i; ++j) {
              //mean_v.at(j)  = floor( mean_v.at(last_good_index) ) + _random_shift + (double) ( rand() % _range) / _divisions;
              mean_v.at(j)  = constant_mean;
              sigma_v.at(j) = mode_sigma;
            }
          }
        }
        last_good_index = (int)i;
      }
    }
    

    // Next do extrapolation to the first and end (if needed)
    // vic: for now we leave this i'm not sure if this really needs
    //      to be tuned until I can make unit test
    // update: yes this needs work...

    if(sigma_v.front() > mode_sigma) {

      int first_index  = -1;
      int second_index = -1;

      for(size_t i=0; i < wf.size(); ++i) {
        if( sigma_v.at(i) < mode_sigma ) {
          if( first_index < 0 ) first_index = (int)i;
          else if( second_index < 0 ) {
            second_index = (int)i;
            break;
          }
        }
      }
      
      if(first_index < 0 || second_index < 0) {
        std::cerr <<"\033[93m<<" << __FUNCTION__ << ">>\033[00m Could not find good pedestal for CDF"
                  << "\n"
                  << "first_index:  " << first_index << "\n"
                  << "second_index: " << second_index << "\n"
                  << "If one of these is less than zero, this means there is pulse\n"
                  << "on first sample and baseline never went back down. Returning false here.";
        return false;
      }


      auto diff = fabs(mean_v.at(first_index) - mean_v.at(second_index));
      
      if ( diff >  diff_cutoff) {
        
        float slope = (mean_v.at(second_index) - mean_v.at(first_index)) / (float(second_index - first_index));
        
        for(int j=0; j < first_index; ++j) {
          mean_v.at(j)  = mean_v.at(first_index) - slope * (first_index - j);
          sigma_v.at(j) = _max_sigma;
        }
        
      } else {

        auto postsample_mean = edge_aware_mean(wf,first_index, first_index + _n_presamples);

        for(int j=0; j < first_index; ++j) {
          //mean_v.at(j)  = floor( mean_v.at(second_index) ) + _random_shift + (double) ( rand() % _range) / _divisions;
          mean_v.at(j)  = postsample_mean;
          sigma_v.at(j) = mode_sigma;
        }
      }
      
    }
    
    
    if(sigma_v.back() > mode_sigma) {

      int first_index  = -1;
      int second_index = -1;
      
      for(int i = wf.size()-1; i >= 0; --i) {
        if(sigma_v.at(i) < mode_sigma) {
          if( second_index < 0 ) second_index = (int)i;
          else if( first_index < 0 ) {
            first_index = (int)i;
            break;
          }
        }
      }
      
      if(first_index < 0 || second_index < 0) {
        std::cerr <<"\033[93m<<" << __FUNCTION__ << ">>\033[00m Could not find good pedestal for CDF"
                  << "\n"
                  << "first_index:  " << first_index << "\n"
                  << "second_index: " << second_index << "\n"
                  << "If one of these is less than zero, this means there is pulse\n"
                  << "on the last sample and baseline never went back down. Returning false here.";
        return false;
      }
            
      
      auto diff = fabs(mean_v.at(second_index) - mean_v.at(first_index) );
      
      if ( diff >  diff_cutoff) {
    
        float slope = (mean_v.at(second_index) - mean_v.at(first_index)) / (float(second_index - first_index));
        for(int j = second_index+1; j < int(wf.size()); ++j) {
          mean_v.at(j)  = mean_v.at(second_index) + slope * (j-second_index);
          sigma_v.at(j) = _max_sigma;
        }

      }
      else {
        
        auto presample_mean  = edge_aware_mean(wf,first_index - _n_presamples, second_index);
        
        for(int j = second_index+1; j < int(wf.size()); ++j) {
          //mean_v.at(j)  = floor( mean_v.at(first_index) ) + _random_shift + (double) ( rand() % _range) / _divisions;
          mean_v.at(j)  = presample_mean;
          sigma_v.at(j) = mode_sigma;
        }
      }
    }

    
    return true;

  }

}

#endif