/// Execute algorithm.
void Schrodinger1D::exec()
{
double startX = get("StartX");
double endX = get("EndX");
if (endX <= startX)
{
throw std::invalid_argument("StartX must be <= EndX");
}
IFunction_sptr VPot = getClass("VPot");
chebfun vpot( 0, startX, endX );
vpot.bestFit( *VPot );
size_t nBasis = vpot.n() + 1;
std::cerr << "n=" << nBasis << std::endl;
//if (n < 3)
{
nBasis = 200;
vpot.resize( nBasis );
}
const double beta = get("Beta");
auto kinet = new ChebCompositeOperator;
kinet->addRight( new ChebTimes(-beta) );
kinet->addRight( new ChebDiff2 );
auto hamiltonian = new ChebPlus;
hamiltonian->add('+', kinet );
hamiltonian->add('+', new ChebTimes(VPot) );
GSLMatrix L;
hamiltonian->createMatrix( vpot.getBase(), L );
GSLVector d;
GSLMatrix v;
L.diag( d, v );
std::vector<double> norms = vpot.baseNorm();
assert(norms.size() == L.size1());
assert(norms.size() == L.size2());
for(size_t i = 0; i < norms.size(); ++i)
{
double factor = 1.0 / norms[i];
for(size_t j = i; j < norms.size(); ++j)
{
v.multiplyBy(i,j,factor);
}
}
// eigenvectors orthogonality check
// GSLMatrix v1 = v;
// GSLMatrix tst;
// tst = Tr(v1) * v;
// std::cerr << tst << std::endl;
std::vector<size_t> indx(L.size1());
getSortedIndex( d, indx );
auto eigenvalues = API::TableWorkspace_ptr(dynamic_cast<API::TableWorkspace*>(
API::WorkspaceFactory::instance().create("TableWorkspace"))
);
eigenvalues->setRowCount(nBasis);
setProperty("Eigenvalues", eigenvalues);
eigenvalues->addColumn("double","N");
auto nColumn = static_cast<API::TableColumn<double>*>(eigenvalues->getColumn("N").get());
nColumn->asNumeric()->setPlotRole(API::NumericColumn::X);
auto& nc = nColumn->data();
eigenvalues->addDoubleColumn("Energy");
auto eColumn = static_cast<API::TableColumn<double>*>(eigenvalues->getColumn("Energy").get());
eColumn->asNumeric()->setPlotRole(API::NumericColumn::Y);
auto& ec = eigenvalues->getDoubleData("Energy");
boost::scoped_ptr<ChebfunVector> eigenvectors(new ChebfunVector);
chebfun fun0(nBasis,startX,endX);
ChebFunction_sptr theSum(new ChebFunction(fun0));
// collect indices of spurious eigenvalues to move them to the back
std::vector<size_t> spurious;
// index for good eigenvalues
size_t n = 0;
for(size_t j = 0; j < nBasis; ++j)
{
size_t i = indx[j];
chebfun fun(fun0);
fun.setP(v,i);
// check eigenvalues for spurious ones
chebfun dfun(fun);
dfun.square();
double norm = dfun.integr();
// I am not sure that it's a solid condition
if ( norm < 0.999999 )
{
// bad eigenvalue
spurious.push_back(j);
}
else
{
nc[n] = double(n);
ec[n] = d[i];
eigenvectors->add(ChebFunction_sptr(new ChebFunction(fun)));
// test sum of functions squares
*theSum += dfun;
// chebfun dfun(fun);
// hamiltonian->apply(fun,dfun);
// dfun *= fun;
// std::cerr << "ener["<<n<<"]=" << ec[n] << ' ' << norm << ' ' << dfun.integr() << std::endl;
++n;
}
}
GSLVector eigv;
ChebfunVector *eigf = NULL;
improve(hamiltonian, eigenvectors.get(), eigv, &eigf);
eigenvalues->setRowCount( eigv.size() );
for(size_t i = 0; i < eigv.size(); ++i)
{
nc[i] = double(i);
ec[i] = eigv[i];
}
eigf->add(theSum);
setProperty("Eigenvectors",ChebfunVector_sptr(eigf));
//makeQuadrature(eigf);
}
