18 treetxt =
"Alignment Parameters";
28 tree->Branch(
"Par", &
thePar,
"Par[CovRang]/D");
30 tree->Branch(
"Cov", &
theCov,
"Cov[CovarRang]/D");
87 for(
int row=0;row<covsize;row++)
103 const std::vector<bool> sel(
theCovRang,
true );
122 int nIndices =
tree->BuildIndex(
"Id",
"ObjId" );
123 edm::LogInfo(
"Alignment" ) <<
"@SUB=AlignmentParametersIORoot::setBranchAddresses" 124 <<
"number of indexed entries: " << nIndices;
align::ID id() const
Return the ID of Alignable, i.e. DetId of 'first' component GeomDet(Unit).
int writeOne(Alignable *ali)
Write AlignmentParameters of one Alignable.
AlignmentParameters * readOne(Alignable *ali, int &ierr)
Read AlignmentParameters of one Alignable.
const std::vector< bool > & selector(void) const
Get alignment parameter selector vector.
AlignmentParameters * alignmentParameters() const
Get the AlignmentParameters.
virtual int type() const =0
tell type (AlignmentParametersFactory::ParametersType - but no circular dependency) ...
ParametersType
enums for all available AlignmentParameters
const AlgebraicVector & parameters(void) const
Get alignment parameters.
void createBranches(void)
Create all branches and give names.
virtual StructureType alignableObjectId() const =0
Return the alignable type identifier.
double theCov[nParMax *(nParMax+1)/2]
void setValid(bool v)
Set validity flag.
virtual AlignmentParameters * clone(const AlgebraicVector &par, const AlgebraicSymMatrix &cov) const =0
Enforce clone methods in derived classes.
AlignmentParameters * createParameters(Alignable *ali, ParametersType parType, const std::vector< bool > &sel)
CLHEP::HepVector AlgebraicVector
int closeRoot(void)
close IO
AlignmentParametersIORoot()
Constructor.
void setBranchAddresses(void)
Set branch adresses.
align::StructureType theObjId
CLHEP::HepSymMatrix AlgebraicSymMatrix
ParametersType parametersType(const std::string &typeString)
convert string to ParametersType - exception if not known
virtual unsigned int hierarchyLevel() const
const AlgebraicSymMatrix & covariance(void) const
Get parameter covariance matrix.