eclAutocovarianceCalibrationC3Algorithm.cc

/**************************************************************************
 * basf2 (Belle II Analysis Software Framework)                           *
 * Author: The Belle II Collaboration                                     *
 *                                                                        *
 * See git log for contributors and copyright holders.                    *
 * This file is licensed under LGPL-3.0, see LICENSE.md.                  *
 **************************************************************************/
 
/* Own header. */
#include <ecl/calibration/eclAutocovarianceCalibrationC3Algorithm.h>
 
/* ECL headers. */
#include <ecl/dataobjects/ECLElementNumbers.h>
#include <ecl/dbobjects/ECLAutoCovariance.h>
 
/* ROOT headers. */
#include <TFile.h>
#include <TGraph.h>
#include <TH2I.h>
#include"TMatrixDSym.h"
#include"TDecompChol.h"
 
using namespace Belle2;
using namespace ECL;
 
eclAutocovarianceCalibrationC3Algorithm::eclAutocovarianceCalibrationC3Algorithm():
  CalibrationAlgorithm("eclAutocovarianceCalibrationC3Collector")
{
  setDescription(
    "Computes the covariance matrix for each crystal"
  );
}
 
CalibrationAlgorithm::EResult eclAutocovarianceCalibrationC3Algorithm::calibrate()
{
 
 
  gROOT->SetBatch();
 
  std::vector<double> cryIDs;
  std::vector<double> noiseMatrix00Vector;
  std::vector<double> autoCov00Vector;
  std::vector<double> totalCountsVector;
  std::vector<double> invertAttempts;
 
  auto CovarianceMatrixInfoVsCrysID = getObjectPtr<TH2F>("CovarianceMatrixInfoVsCrysID");
 
  ECLAutoCovariance* Autocovariances = new ECLAutoCovariance();
 
  for (int ID = 0; ID < ECLElementNumbers::c_NCrystals; ID++) {
 
    m_u2 = 1.0; //Reseting
 
    double totalCounts = CovarianceMatrixInfoVsCrysID->GetBinContent(CovarianceMatrixInfoVsCrysID->GetBin(ID + 1,
                         m_numberofADCPoints + 1));
 
    if (totalCounts < m_TotalCountsThreshold) {
      B2INFO("eclAutocovarianceCalibrationC3Algorithm: warning total entries for cell ID " << ID + 1 << " is only: " << totalCounts <<
             " Requirement is m_TotalCountsThreshold: " << m_TotalCountsThreshold);
      return c_NotEnoughData;
    }
 
    TMatrixDSym NoiseMatrix;
    NoiseMatrix.ResizeTo(m_numberofADCPoints, m_numberofADCPoints);
    for (int i = 0; i < m_numberofADCPoints; i++) {
      for (int j = 0; j < m_numberofADCPoints; j++) {
        int index = abs(i - j);
        NoiseMatrix(i, j) = double(CovarianceMatrixInfoVsCrysID->GetBinContent(CovarianceMatrixInfoVsCrysID->GetBin(ID + 1,
                                   index + 1))) / (totalCounts - 1) / (double(m_numberofADCPoints - index));
      }
    }
 
    TMatrixDSym NoiseMatrixReduced(m_numberofADCPoints);
    for (int i = 0; i < m_numberofADCPoints; i++) {
      for (int j = 0; j < m_numberofADCPoints; j++) {
        NoiseMatrixReduced(i, j) = (NoiseMatrix(0, abs(i - j)));
      }
    }
 
    bool invert_successful = 0;
    int invert_attempt = 0;
    double tempAutoCov[m_numberofADCPoints];
    for (int i = 0; i < m_numberofADCPoints; i++) tempAutoCov[i] = NoiseMatrixReduced(0, i);
    std::vector<double> buf(m_numberofADCPoints);
    while (invert_successful == 0) {
 
      Autocovariances->setAutoCovariance(ID + 1, tempAutoCov);
      Autocovariances->getAutoCovariance(ID + 1, buf.data());
 
      TMatrixDSym NoiseMatrix_check(m_numberofADCPoints);
      for (int i = 0; i < m_numberofADCPoints; i++) {
        for (int j = 0; j < m_numberofADCPoints; j++) {
          NoiseMatrix_check(i, j) = buf[abs(i - j)];
        }
      }
 
      TDecompChol dc(NoiseMatrix_check);
      invert_successful = dc.Invert(NoiseMatrix_check);
      if (invert_successful == 0) {
 
        if (invert_attempt > 4) {
          B2INFO("eclAutocovarianceCalibrationC3Algorithm iD " << ID << " invert_attempt limit reached " << invert_attempt);
          B2INFO("eclAutocovarianceCalibrationC3Algorithm setting m_u2 to zero");
          m_u2 = 0.0;
        }
        if (invert_attempt > 100) {
          B2INFO("eclAutocovarianceCalibrationC3Algorithm unable to invert!");
          return c_NotEnoughData;
        }
 
        B2INFO("eclAutocovarianceCalibrationC3Algorithm iD " << ID << " invert_attempt " << invert_attempt);
 
        for (int i = 0; i < m_numberofADCPoints; i++) B2INFO("old[" << i << "] = " <<  tempAutoCov[i]);
        for (int i = 1; i < m_numberofADCPoints; i++) tempAutoCov[i] *= (m_u2 / (1. + exp((i - m_u0) / m_u1)));
        for (int i = 0; i < m_numberofADCPoints; i++) B2INFO("new[" << i << "] = " <<  tempAutoCov[i]);
 
      } else {
        noiseMatrix00Vector.push_back(NoiseMatrix_check(0, 0));
      }
      invert_attempt++;
    }
 
    cryIDs.push_back(ID + 1);
    autoCov00Vector.push_back(tempAutoCov[0]);
    totalCountsVector.push_back(totalCounts);
    invertAttempts.push_back(invert_attempt);
 
  }
 
  saveCalibration(Autocovariances, "ECLAutoCovariance");
 
  auto gautoCov00Vector = new TGraph(cryIDs.size(), cryIDs.data(), autoCov00Vector.data());
  gautoCov00Vector->SetName("gautoCov00Vector");
  gautoCov00Vector->SetMarkerStyle(20);
  auto gnoiseMatrix00Vector = new TGraph(cryIDs.size(), cryIDs.data(), noiseMatrix00Vector.data());
  gnoiseMatrix00Vector->SetName("gnoiseMatrix00Vector");
  gnoiseMatrix00Vector->SetMarkerStyle(20);
  auto gtotalCountsVector = new TGraph(cryIDs.size(), cryIDs.data(), totalCountsVector.data());
  gtotalCountsVector->SetName("gtotalCountsVector");
  gtotalCountsVector->SetMarkerStyle(20);
  auto ginvertAttempts = new TGraph(cryIDs.size(), cryIDs.data(), invertAttempts.data());
  ginvertAttempts->SetName("ginvertAttempts");
  ginvertAttempts->SetMarkerStyle(20);
 
  TString fName = m_outputName;
  TDirectory::TContext context;
  TFile* histfile = new TFile(fName, "recreate");
  histfile->cd();
  gautoCov00Vector->Write();
  gnoiseMatrix00Vector->Write();
  gtotalCountsVector->Write();
  ginvertAttempts->Write();
  histfile->Close();
  delete histfile;
 
  return c_OK;
}