static void test_composition()
{
Eigen::Tensor<float, 2, DataLayout> matrix(7, 11);
matrix.setRandom();
const DSizes<ptrdiff_t, 3> newDims(1, 1, 11);
Eigen::Tensor<float, 3, DataLayout> tensor =
matrix.slice(DSizes<ptrdiff_t, 2>(2, 0), DSizes<ptrdiff_t, 2>(1, 11)).reshape(newDims);
VERIFY_IS_EQUAL(tensor.dimensions().TotalSize(), 11);
VERIFY_IS_EQUAL(tensor.dimension(0), 1);
VERIFY_IS_EQUAL(tensor.dimension(1), 1);
VERIFY_IS_EQUAL(tensor.dimension(2), 11);
for (int i = 0; i < 11; ++i) {
VERIFY_IS_EQUAL(tensor(0,0,i), matrix(2,i));
}
}
Eigen::Tensor<std::complex<double>,2>
to_Tnl_pn(const Eigen::Tensor<std::complex<double>,2>& Tnl, alps::gf::statistics::statistics_type s) {
int niw = Tnl.dimension(0);
int nl = Tnl.dimension(1);
Eigen::Tensor<std::complex<double>,2> Tnl_pn(2*niw, nl);
Tnl_pn.setZero();
if (s==alps::gf::statistics::FERMIONIC) {
for (int l=0; l<nl; ++l) {
for (int n=0; n<niw; ++n) {
Tnl_pn(niw + n, l) = Tnl(n, l);
Tnl_pn(niw - 1 - n, l) = std::conj(Tnl(n, l));
}
}
} else if (s==alps::gf::statistics::BOSONIC) {
for (int l=0; l<nl; ++l) {
for (int n=0; n<niw; ++n) {
Tnl_pn(niw + n, l) = Tnl(n, l);
}
for (int n=1; n<niw; ++n) {
Tnl_pn(niw - n, l) = std::conj(Tnl(n, l));
}
}
} else {
throw std::runtime_error("Unknown statistics type");
}
return Tnl_pn;
}
void save( Archive & ar,
const Eigen::Tensor<T,N> & t,
const unsigned int file_version )
{
int n0 = t.dimension(0);
ar << BOOST_SERIALIZATION_NVP( n0 );
if ( N >= 2 )
{
int n1 = t.dimension(1);
ar << BOOST_SERIALIZATION_NVP( n1 );
}
if ( N >= 3 )
{
int n2 = t.dimension(2);
ar << BOOST_SERIALIZATION_NVP( n2 );
}
ar << boost::serialization::make_array( t.data(),
t.size() );
}