9 KalmanState::KalmanState(
const Settings *settings,
21 : settings_(settings),
23 last_state_(last_state),
29 l1track3D_(candidate) {
31 matC_.ResizeTo(matC.GetNrows(), matC.GetNcols());
33 matK_.ResizeTo(matK.GetNrows(), matK.GetNcols());
35 matV_.ResizeTo(matV.GetNrows(), matV.GetNcols());
49 if (
stub !=
nullptr) {
62 if (
stub !=
nullptr) {
64 if (tps.find(
tp) == tps.end())
82 if (
state->stub() !=
nullptr)
90 std::vector<Stub *> all_stubs;
96 all_stubs.push_back(
stub);
double reducedChi2() const
const KalmanState * last_state() const
std::vector< Stub * > stubs() const
const Settings * settings_
const KalmanState * last_update_state() const
bool good(const TP *tp) const
unsigned int kalmanChi2RphiScale_
static const std::string kLayer("layer")
=== This is the base class for the linearised chi-squared track fit algorithms.
const std::set< const TP * > & assocTPs() const
unsigned int kalmanChi2RphiScale() const
unsigned int hitPattern() const