CDCSZFitter.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.                  *
 **************************************************************************/
#include <tracking/trackFindingCDC/fitting/CDCSZFitter.h>
 
#include <tracking/trackFindingCDC/fitting/EigenObservationMatrix.h>
#include <tracking/trackFindingCDC/fitting/CDCSZObservations.h>
#include <tracking/trackFindingCDC/fitting/CDCObservations2D.h>
 
#include <tracking/trackFindingCDC/eventdata/tracks/CDCTrack.h>
#include <tracking/trackFindingCDC/eventdata/tracks/CDCSegmentPair.h>
 
#include <tracking/trackFindingCDC/eventdata/segments/CDCSegment3D.h>
#include <tracking/trackFindingCDC/eventdata/segments/CDCSegment2D.h>
 
#include <tracking/trackFindingCDC/eventdata/trajectories/CDCTrajectorySZ.h>
#include <tracking/trackFindingCDC/eventdata/trajectories/CDCTrajectory2D.h>
 
#include <tracking/trackFindingCDC/geometry/SZParameters.h>
 
#include <tracking/trackFindingCDC/numerics/EigenView.h>
 
#include <framework/logging/Logger.h>
 
#include <Eigen/Eigen>
#include <Eigen/QR>
#include <Eigen/Core>
 
using namespace Belle2;
using namespace TrackFindingCDC;
 
const CDCSZFitter& CDCSZFitter::getFitter()
{
  static CDCSZFitter szFitter;
  return szFitter;
}
 
namespace {
  UncertainSZLine fitZ(const Eigen::Matrix<double, 3, 3>& sumMatrixWSZ)
  {
    // Solve the normal equation X * n = y
    Eigen::Matrix<double, 2, 2> sumMatrixWS = sumMatrixWSZ.block<2, 2>(0, 0);
    Eigen::Matrix<double, 2, 1> sumVectorZOverWS = sumMatrixWSZ.block<2, 1>(0, 2);
    Eigen::Matrix<double, 2, 1> nZOverWS = sumMatrixWS.llt().solve(sumVectorZOverWS);
    double chi2 = sumMatrixWSZ(2, 2) - nZOverWS.transpose() * sumVectorZOverWS;
 
    using namespace NSZParameterIndices;
    SZParameters szParameters;
    szParameters(c_TanL) = nZOverWS(1);
    szParameters(c_Z0) = nZOverWS(0);
 
    SZPrecision szPrecision;
    szPrecision(c_TanL, c_TanL) = sumMatrixWS(1, 1);
    szPrecision(c_Z0, c_TanL) = sumMatrixWS(0, 1);
    szPrecision(c_TanL, c_Z0) = sumMatrixWS(1, 0);
    szPrecision(c_Z0, c_Z0) = sumMatrixWS(0, 0);
 
    SZCovariance szCovariance = SZUtil::covarianceFromFullPrecision(szPrecision);
    return UncertainSZLine(szParameters, szCovariance, chi2);
  }
 
  // Declare function as currently unused to avoid compiler warning
  UncertainSZLine fitSZ(const Eigen::Matrix<double, 3, 3>& sumMatrixWSZ) __attribute__((__unused__));
 
  UncertainSZLine fitSZ(const Eigen::Matrix<double, 3, 3>& sumMatrixWSZ)
  {
 
    // Matrix of averages
    Eigen::Matrix<double, 3, 3> averageMatrixWSZ = sumMatrixWSZ / sumMatrixWSZ(0);
 
    // Measurement means
    Eigen::Matrix<double, 3, 1> meansWSZ = averageMatrixWSZ.row(0);
 
    // Covariance matrix
    Eigen::Matrix<double, 3, 3> covMatrixWSZ = averageMatrixWSZ - meansWSZ * meansWSZ.transpose();
 
    Eigen::Matrix<double, 2, 2> covMatrixSZ = covMatrixWSZ.block<2, 2>(1, 1);
    Eigen::Matrix<double, 2, 1> meansSZ = meansWSZ.segment<2>(1);
 
    Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, 2, 2>> eigensolver(covMatrixSZ);
    if (eigensolver.info() != Eigen::Success) {
      B2WARNING(
        "SelfAdjointEigenSolver could not compute the eigen values of the observation matrix");
    }
 
    // the eigenvalues are generated in increasing order
    // we are interested in the lowest one since we want to compute the normal vector of the line
    // here
    Eigen::Matrix<double, 2, 1> nSZ = eigensolver.eigenvectors().col(0);
    B2INFO("nSZ " << nSZ);
    nSZ /= std::copysign(nSZ.norm(), -nSZ(1)); // Making n2 negative to normalize to forward along s
    Eigen::Matrix<double, 3, 1> nWSZ;
    // cppcheck-suppress constStatement
    nWSZ << -meansSZ.transpose() * nSZ, nSZ;
    B2INFO("nWSZ " << nWSZ);
 
    double chi2 = nWSZ.transpose() * sumMatrixWSZ * nWSZ;
 
    Eigen::Matrix<double, 3, 3> precN = sumMatrixWSZ;
 
    using namespace NSZParameterIndices;
    SZParameters szParameters;
    szParameters(c_TanL) = -nWSZ(1) / nWSZ(2);
    szParameters(c_Z0) = -nWSZ(0) / nWSZ(2);
 
    Eigen::Matrix<double, 3, 2> ambiguitySZToN = Eigen::Matrix<double, 3, 2>::Zero();
    ambiguitySZToN(0, c_Z0) = -nWSZ(2);
    ambiguitySZToN(0, c_TanL) = -nWSZ(1) * nWSZ(0);
    ambiguitySZToN(1, c_TanL) = -nWSZ(2) * nWSZ(2) * nWSZ(2);
    ambiguitySZToN(2, c_TanL) = nWSZ(1) * nWSZ(2) * nWSZ(2);
 
    SZPrecision szPrecision;
    mapToEigen(szPrecision) = ambiguitySZToN.transpose() * precN * ambiguitySZToN;
 
    SZCovariance szCovariance = SZUtil::covarianceFromFullPrecision(szPrecision);
    return UncertainSZLine(szParameters, szCovariance, chi2);
  }
}
 
CDCTrajectorySZ CDCSZFitter::fitWithStereoHits(const CDCTrack& track) const
{
  const bool onlyStereo = true;
  CDCSZObservations observationsSZ(EFitVariance::c_Proper, onlyStereo);
  observationsSZ.appendRange(track);
  if (observationsSZ.size() > 3) {
    CDCTrajectorySZ szTrajectory;
    update(szTrajectory, observationsSZ);
    return szTrajectory;
  } else {
    return CDCTrajectorySZ::basicAssumption();
  }
}
 
CDCTrajectorySZ CDCSZFitter::fit(const CDCSegment2D& stereoSegment,
                                 const CDCTrajectory2D& axialTrajectory2D) const
{
  B2ASSERT("Expected stereo segment", not stereoSegment.isAxial());
 
  CDCTrajectorySZ trajectorySZ;
  update(trajectorySZ, stereoSegment, axialTrajectory2D);
  return trajectorySZ;
}
 
CDCTrajectorySZ CDCSZFitter::fit(const CDCSegment3D& segment3D) const
{
  CDCSZObservations observationsSZ;
  observationsSZ.appendRange(segment3D);
  return fit(std::move(observationsSZ));
}
 
CDCTrajectorySZ CDCSZFitter::fit(CDCSZObservations observationsSZ) const
{
  CDCTrajectorySZ trajectorySZ;
  update(trajectorySZ, observationsSZ);
  return trajectorySZ;
}
 
void CDCSZFitter::update(const CDCSegmentPair& segmentPair) const
{
  const CDCSegment2D* ptrStereoSegment = segmentPair.getStereoSegment();
  const CDCSegment2D* ptrAxialSegment = segmentPair.getAxialSegment();
 
  assert(ptrStereoSegment);
  assert(ptrAxialSegment);
 
  const CDCSegment2D& stereoSegment = *ptrStereoSegment;
  const CDCSegment2D& axialSegment = *ptrAxialSegment;
  const CDCTrajectory2D& axialTrajectory2D = axialSegment.getTrajectory2D();
 
  CDCTrajectorySZ trajectorySZ;
  update(trajectorySZ, stereoSegment, axialTrajectory2D);
 
  CDCTrajectory3D trajectory3D(axialTrajectory2D, trajectorySZ);
  segmentPair.setTrajectory3D(trajectory3D);
}
 
CDCTrajectorySZ CDCSZFitter::fit(const CDCObservations2D& observations2D) const
{
  CDCSZObservations szObservations;
  for (size_t i = 0; i < observations2D.size(); ++i) {
    const double s = observations2D.getX(i);
    const double z = observations2D.getY(i);
    szObservations.fill(s, z);
  }
  return fit(std::move(szObservations));
}
 
void CDCSZFitter::update(CDCTrajectorySZ& trajectorySZ,
                         const CDCSegment2D& stereoSegment,
                         const CDCTrajectory2D& axialTrajectory2D) const
{
  B2ASSERT("Expected stereo segment", not stereoSegment.isAxial());
 
  // recostruct the stereo segment
  CDCSZObservations observationsSZ;
  for (const CDCRecoHit2D& recoHit2D : stereoSegment) {
    CDCRecoHit3D recoHit3D = CDCRecoHit3D::reconstruct(recoHit2D, axialTrajectory2D);
    observationsSZ.append(recoHit3D);
  }
 
  update(trajectorySZ, observationsSZ);
}
 
void CDCSZFitter::update(CDCTrajectorySZ& trajectorySZ, CDCSZObservations& observationsSZ) const
{
  trajectorySZ.clear();
  if (observationsSZ.size() < 3) {
    return;
  }
 
  // Determine NDF : Line fit eats up 2 degrees of freedom.
  size_t ndf = observationsSZ.size() - 2;
 
  // Matrix of weighted sums
  Eigen::Matrix<double, 3, 3> sumMatrixWSZ = getWSZSumMatrix(observationsSZ);
  UncertainSZLine uncertainSZLine = fitZ(sumMatrixWSZ);
 
  uncertainSZLine.setNDF(ndf);
  trajectorySZ.setSZLine(uncertainSZLine);
}