RiemannsMethod Class Reference

Class implementing the Riemann fit for two dimensional trajectory circle. More...

#include <RiemannsMethod.h>

Public Member Functions
	RiemannsMethod ()
	Default constructor.
void	update (CDCTrajectory2D &trajectory2D, CDCObservations2D &observations2D) const
	Executes the fit and updates the trajectory parameters. This may render the information in the observation object.
bool	isLineConstrained () const
	Getter for the indictor that lines should be fitted by this fitter.
bool	isOriginConstrained () const
	Getter for the indictor that curves through the origin should be fitted by this fitter.
void	setLineConstrained (bool constrained=true)
	Indicator if this fitter is setup to fit lines.
void	setOriginConstrained (bool constrained=true)
	Indicator if this fitter is setup to fit curves through the origin.

Private Member Functions
void	updateWithoutDriftLength (CDCTrajectory2D &trajectory2D, CDCObservations2D &observations2D) const
	Executes the fit without using the drift length information. More...
void	updateWithDriftLength (CDCTrajectory2D &trajectory2D, CDCObservations2D &observations2D) const
	Executes the fit using the drift length information. More...

Private Attributes
bool	m_lineConstrained
	Memory for the flag indicating that lines should be fitted.
bool	m_originConstrained
	Memory for the flag indicating that curves through the origin shall be fitter.

Detailed Description

Class implementing the Riemann fit for two dimensional trajectory circle.

Definition at line 30 of file RiemannsMethod.h.

Member Function Documentation

updateWithDriftLength()

void updateWithDriftLength ( CDCTrajectory2D &  trajectory2D, CDCObservations2D &  observations2D  ) const

private

Executes the fit using the drift length information.

This method is used if there is no drift length information is available from the observations.

Definition at line 146 of file RiemannsMethod.cc.

{
  EigenObservationMatrix eigenObservation = getEigenObservationMatrix(&observations2D);
  assert(eigenObservation.cols() == 4);
  size_t nObservations = observations2D.size();
 
  //cout << "updateWithRightLeft : " << endl;
  //observations always have the structure
  /*
  observables[0][0] == x of first point
  observables[0][1] == y of first point
  observables[0][2] == desired distance of first point
  */
 
  Eigen::Matrix< double, Eigen::Dynamic, 1 > distances = eigenObservation.col(2);
 
  //cout << "distances : " << endl << distances << endl;
 
  if ((isLineConstrained()) && (isOriginConstrained())) {
 
 
    Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic > projectedPoints(nObservations, 2);
 
    //all coordiates
    //projectedPoints.col(0) = Eigen::Matrix<double,Eigen::Dynamic,1>::Constant(nObservations,1.0);
    projectedPoints.col(0) = eigenObservation.col(0);
    projectedPoints.col(1) = eigenObservation.col(1);
    //projectedPoints.col(2) = eigenObservation.leftCols<2>().rowwise().squaredNorm() - distances.rowwise().squaredNorm();
 
    Eigen::Matrix<double, 2, 1> parameters =
      projectedPoints.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(distances);
 
    trajectory2D.setGlobalCircle(UncertainPerigeeCircle(PerigeeCircle::fromN(0.0, parameters(0), parameters(1), 0.0)));
  }
 
  else if ((! isLineConstrained()) && (isOriginConstrained())) {
 
    Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic > projectedPoints(nObservations, 3);
 
    //all coordiates
    //projectedPoints.col(0) = Eigen::Matrix<double,Eigen::Dynamic,1>::Constant(1.0);
    projectedPoints.col(0) = eigenObservation.col(0);
    projectedPoints.col(1) = eigenObservation.col(1);
    projectedPoints.col(2) = eigenObservation.leftCols<2>().rowwise().squaredNorm() - distances.rowwise().squaredNorm();
 
    Eigen::Matrix<double, 3, 1> parameters =
      projectedPoints.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(distances);
 
    trajectory2D.setGlobalCircle(UncertainPerigeeCircle(PerigeeCircle::fromN(0.0, parameters(0), parameters(1), parameters(2))));
  }
 
  else if ((isLineConstrained()) && (! isOriginConstrained())) {
 
    Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic > projectedPoints(nObservations, 3);
 
    //all coordiates
    projectedPoints.col(0) = Eigen::Matrix<double, Eigen::Dynamic, 1>::Constant(nObservations, 1.0);
    projectedPoints.col(1) = eigenObservation.col(0);
    projectedPoints.col(2) = eigenObservation.col(1);
    //projectedPoints.col(3) = eigenObservation.leftCols<2>().rowwise().squaredNorm()- distances.rowwise().squaredNorm();
 
    Eigen::Matrix<double, 3, 1> parameters =
      projectedPoints.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(distances);
 
    trajectory2D.setGlobalCircle(UncertainPerigeeCircle(PerigeeCircle::fromN(parameters(0), parameters(1), parameters(2), 0.0)));
    //fit.setParameters(parameters(0),parameters(1),parameters(2),0.0);
 
  }
 
  else if ((! isLineConstrained()) && (! isOriginConstrained())) {
 
    Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic > projectedPoints(nObservations, 4);
 
    //all coordiates
    projectedPoints.col(0) = Eigen::Matrix<double, Eigen::Dynamic, 1>::Constant(nObservations, 1.0);
    projectedPoints.col(1) = eigenObservation.col(0);
    projectedPoints.col(2) = eigenObservation.col(1);
    projectedPoints.col(3) = eigenObservation.leftCols<2>().rowwise().squaredNorm() - distances.rowwise().squaredNorm();
 
    //cout << "points : " << endl << projectedPoints << endl;
 
    Eigen::Matrix<double, 4, 1> parameters =
      projectedPoints.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(distances);
 
    trajectory2D.setGlobalCircle(UncertainPerigeeCircle(PerigeeCircle::fromN(parameters(0), parameters(1), parameters(2),
                                                        parameters(3))));
 
  }
}

updateWithoutDriftLength()

void updateWithoutDriftLength ( CDCTrajectory2D &  trajectory2D, CDCObservations2D &  observations2D  ) const

private

Executes the fit without using the drift length information.

This method is used if there is drift length information is available from the observations.

Definition at line 49 of file RiemannsMethod.cc.

---

The documentation for this class was generated from the following files:

• tracking/trackFindingCDC/fitting/include/RiemannsMethod.h
• tracking/trackFindingCDC/fitting/src/RiemannsMethod.cc