1 #ifndef L1Trigger_TrackFindingTMTT_KalmanState_h 2 #define L1Trigger_TrackFindingTMTT_KalmanState_h 45 double r()
const {
return r_; }
46 double z()
const {
return z_; }
71 std::vector<Stub *>
stubs()
const;
double reducedChi2() const
unsigned nextLayer() const
unsigned nSkippedLayers() const
const Settings * settings() const
const TVectorD & vectorX() const
const KalmanState * last_state() const
const TMatrixD & matrixK() const
std::vector< Stub * > stubs() const
const KalmanState * last_state_
unsigned nStubLayers() const
const Settings * settings_
const KalmanState * last_update_state() const
bool good(const TP *tp) const
unsigned int kalmanChi2RphiScale_
const TMatrixD & matrixV() const
static const std::string kLayer("layer")
double chi2scaled() const
=== This is the base class for the linearised chi-squared track fit algorithms.
const TMatrixD & matrixC() const
KalmanState(const Settings *settings, const L1track3D &candidate, unsigned nSkipped, int kLayer, const KalmanState *last_state, const TVectorD &vecX, const TMatrixD &matC, const TMatrixD &matK, const TMatrixD &matV, Stub *stub, double chi2rphi, double chi2rz)
void setChi2(double chi2rphi, double chi2rz)
const L1track3D & candidate() const
unsigned int hitPattern() const