GainChannelNormalize.h

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#ifndef GainChannelNormalize_h
#define GainChannelNormalize_h

#include <cmath>
#include <string>
#include <vector>

#include "IString.h"
#include "HiCalTypes.h"
#include "HiCalUtil.h"
#include "HiCalConf.h"
#include "Module.h"
#include "FileName.h"

#include "IException.h"

namespace Isis {

  class GainChannelNormalize : public Module {

    public:
      //  Constructors and Destructor
      GainChannelNormalize() : Module("GainChannelNormalize") { }
      GainChannelNormalize(const HiCalConf &conf) :
                           Module("GainChannelNormalize") {
        init(conf);
      }

      virtual ~GainChannelNormalize() { }

    private:

      void init(const HiCalConf &conf) {
        _history.clear();
        DbProfile prof = conf.getMatrixProfile();
        _history.add("Profile["+ prof.Name()+"]");

        double bin = ToDouble(prof("Summing"));
        double tdi = ToDouble(prof("Tdi"));
        double _normalizer = 128.0 / tdi / (bin*bin);
        _history.add("ModeNormalizer["+ToString(_normalizer)+"]");

        HiVector z = loadCsv("Gains", conf, prof, 0);
        int nsamps = ToInteger(prof("Samples"));
        _data = HiVector(nsamps);
        // Support a single value being read or equal to the number of samples
        if ( z.dim() == 1 ) {
          _data = z[0];
        }
        else if ( z.dim() == nsamps) {
          _data = z;
        }
        else {
          std::ostringstream mess;
          mess << "Expected 1 or " << nsamps << " values from CSV file "
               << getcsvFile() << " but got " << z.dim() << " instead!";
          throw IException(IException::User, mess.str(), _FILEINFO_);
        }

        //  Apply the factor to the data
        for ( int i = 0 ; i < _data.dim() ; i++ ) { _data[i] *= _normalizer; }
        return;
      }

  };

}     // namespace Isis
#endif