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Prior to rationalising 2 versions of BaseIO::write (scalar and vector)
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@ -36,13 +36,19 @@ Author: Guido Cossu <guido.cossu@ed.ac.uk>
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#include <Grid/Eigen/unsupported/CXX11/Tensor>
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namespace Grid {
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// TODO Support Grid::complex from GPU port
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template<typename T> using Grid_complex = std::complex<T>;
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// Returns original type, except for Grid_complex, where it returns the underlying type
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template<typename T> struct RealType { using type = T; };
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template<typename T> struct RealType<Grid_complex<T>> { using type = T; };
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namespace EigenIO {
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template<typename T> struct is_complex : public std::false_type {};
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template<typename T> struct is_complex<std::complex<T>>
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template<typename T> struct is_complex<Grid_complex<T>>
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: std::integral_constant<bool, std::is_arithmetic<T>::value> {};
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// Eigen tensors can be composed of arithmetic scalar and complex types
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// TODO Support Grid::comples from GPU port
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template<typename T> struct is_scalar : std::integral_constant<bool,
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std::is_arithmetic<T>::value || is_complex<T>::value> {};
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@ -88,11 +94,12 @@ namespace Grid {
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template <typename T, typename C = void> struct Traits {}; // C needed for specialisation
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// This defines the bottom level - i.e. it's a description of the underlying scalar
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template <typename T> struct Traits<T, typename std::enable_if<is_scalar<T>::value, void>::type> {
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using scalar_type = T; // Type of the underlying scalar
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using scalar_real = typename RealType<scalar_type>::type; // real type underlying scalar_type
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static constexpr unsigned int depth = 0; // How many levels of Grid Tensor there are (TensorLevel)
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static constexpr unsigned int rank = 0; // The rank of the grid tensor (i.e. how many indices used)
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static constexpr unsigned int rank_non_trivial = 0; // As per rank, but excludes those of dimension 1
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static constexpr unsigned int count = 1; // total number of elements (i.e. product of dimensions)
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using scalar_type = T; // Type of the underlying scalar
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static constexpr std::size_t scalar_size = sizeof(T); // Size of the underlying scalar in bytes
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static constexpr std::size_t size = scalar_size * count; // total size of elements in bytes
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static constexpr std::size_t Dimension(unsigned int dim) { return 0; } // Dimension size
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@ -114,11 +121,12 @@ namespace Grid {
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// count = 48
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};
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template <typename T> struct Traits<iScalar<T>> {
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using scalar_type = typename Traits<T>::scalar_type;
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using scalar_real = typename RealType<scalar_type>::type;
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static constexpr unsigned int depth = 1 + Traits<T>::depth;
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static constexpr unsigned int rank = 0 + Traits<T>::rank;
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static constexpr unsigned int rank_non_trivial = 0 + Traits<T>::rank_non_trivial;
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static constexpr unsigned int count = 1 * Traits<T>::count;
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using scalar_type = typename Traits<T>::scalar_type;
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static constexpr std::size_t scalar_size = Traits<T>::scalar_size;
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static constexpr std::size_t size = scalar_size * count;
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static constexpr std::size_t Dimension(unsigned int dim) {
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@ -127,11 +135,12 @@ namespace Grid {
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return Traits<T>::DimensionNT(dim); }
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};
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template <typename T, int N> struct Traits<iVector<T, N>> {
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using scalar_type = typename Traits<T>::scalar_type;
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using scalar_real = typename RealType<scalar_type>::type;
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static constexpr unsigned int depth = 1 + Traits<T>::depth;
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static constexpr unsigned int rank = 1 + Traits<T>::rank;
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static constexpr unsigned int rank_non_trivial = (N>1 ? 1 : 0) + Traits<T>::rank_non_trivial;
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static constexpr unsigned int count = N * Traits<T>::count;
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using scalar_type = typename Traits<T>::scalar_type;
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static constexpr std::size_t scalar_size = Traits<T>::scalar_size;
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static constexpr std::size_t size = scalar_size * count;
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static constexpr std::size_t Dimension(unsigned int dim) {
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@ -141,11 +150,12 @@ namespace Grid {
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}
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};
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template <typename T, int N> struct Traits<iMatrix<T, N>> {
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using scalar_type = typename Traits<T>::scalar_type;
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using scalar_real = typename RealType<scalar_type>::type;
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static constexpr unsigned int depth = 1 + Traits<T>::depth;
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static constexpr unsigned int rank = 2 + Traits<T>::rank;
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static constexpr unsigned int rank_non_trivial = (N>1 ? 2 : 0) + Traits<T>::rank_non_trivial;
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static constexpr unsigned int count = N * N * Traits<T>::count;
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using scalar_type = typename Traits<T>::scalar_type;
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static constexpr std::size_t scalar_size = Traits<T>::scalar_size;
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static constexpr std::size_t size = scalar_size * count;
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static constexpr std::size_t Dimension(unsigned int dim) {
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@ -230,16 +240,22 @@ namespace Grid {
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}
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}
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// Used for sequential initialisations
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template<typename T> constexpr T Flag = 1;
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template<typename T> constexpr std::complex<T> Flag<std::complex<T>> {1, -1};
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// Returns the type of the real part of an arithmetic type
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template<typename T> struct RealType { using type = T; };
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template<typename T> struct RealType<std::complex<T>> { using type = T; };
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// Sequential initialisation of tensors
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// Would have preferred to define template variables for this, but that's c++ 17
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template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor<ETensor>::value && !EigenIO::is_complex<typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type>::value, void>::type
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SequentialInit( ETensor &ET, typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type Inc = 1 )
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{
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using Traits = EigenIO::Traits<typename ETensor::Scalar>;
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using scalar_type = typename Traits::scalar_type;
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for_all( ET, [&](scalar_type &c, typename ETensor::Index n, const std::array<size_t, ETensor::NumIndices + Traits::rank_non_trivial> &Dims ) {
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c = Inc * static_cast<scalar_type>(n);
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} );
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}
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template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor<ETensor>::value, void>::type
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Sequential_Init( ETensor &ET, typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type Inc = Flag<typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type> )
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typename std::enable_if<EigenIO::is_tensor<ETensor>::value && EigenIO::is_complex<typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type>::value, void>::type
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SequentialInit( ETensor &ET, typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type Inc={1,-1})
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{
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using Traits = EigenIO::Traits<typename ETensor::Scalar>;
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using scalar_type = typename Traits::scalar_type;
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@ -247,7 +263,7 @@ namespace Grid {
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c = Inc * static_cast<typename RealType<scalar_type>::type>(n);
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} );
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}
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// Helper to dump a tensor
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#ifdef DEBUG
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#define dump_tensor(args...) dump_tensor_func(args)
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@ -82,7 +82,7 @@ bool b = false;
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template <typename W, typename R, typename O>
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void ioTest(const std::string &filename, const O &object, const std::string &name)
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{
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std::cout << name << " IO test: writing ...";
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std::cout << "IO test: " << name << " -> " << filename << " ...";
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// writer needs to be destroyed so that writing physically happens
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{
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W writer(filename);
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@ -92,149 +92,105 @@ void ioTest(const std::string &filename, const O &object, const std::string &nam
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std::cout << " done. reading...";
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R reader(filename);
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O buf;
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std::unique_ptr<O> buf( new O ); // In case object too big for stack
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read(reader, "testobject", buf);
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bool good = Serializable::CompareMember(object, buf);
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read(reader, "testobject", *buf);
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bool good = Serializable::CompareMember(object, *buf);
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if (!good) {
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std::cout << " failure!" << std::endl;
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if constexpr (EigenIO::is_tensor<O>::value)
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dump_tensor(buf,"???");
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dump_tensor(*buf,"???");
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exit(EXIT_FAILURE);
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}
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std::cout << " done." << std::endl;
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}
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#ifdef HAVE_HDF5
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typedef Eigen::Tensor<int, 5> ShortRank5Tensor;
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//typedef int TestScalar;
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typedef std::complex<double> TestScalar;
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typedef Eigen::Tensor<TestScalar, 3, Eigen::StorageOptions::RowMajor> TestTensor;
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typedef Eigen::TensorFixedSize<TestScalar, Eigen::Sizes<9,4,2>, Eigen::StorageOptions::RowMajor> TestTensorFixed;
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typedef std::vector<TestTensorFixed> aTestTensorFixed;
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typedef Eigen::TensorFixedSize<SpinColourVector, Eigen::Sizes<11,3,2>, Eigen::StorageOptions::RowMajor> LSCTensor;
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typedef Eigen::TensorFixedSize<LorentzColourMatrix, Eigen::Sizes<5,7,2>, Eigen::StorageOptions::RowMajor> LCMTensor;
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// From Test_serialisation.cc
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using TensorSingle = Eigen::TensorFixedSize<int, Eigen::Sizes<1>>;
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using TensorSimple = Eigen::Tensor<iMatrix<TestScalar,1>, 6>;
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typedef Eigen::Tensor<unsigned short, 5> TensorRank5UShort;
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typedef Eigen::Tensor<int, 5, Eigen::StorageOptions::RowMajor> TensorRank5IntAlt;
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typedef Eigen::Tensor<TestScalar, 3, Eigen::StorageOptions::RowMajor> TensorRank3;
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typedef Eigen::TensorFixedSize<TestScalar, Eigen::Sizes<9,4,2>, Eigen::StorageOptions::RowMajor> Tensor_9_4_2;
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typedef std::vector<Tensor_9_4_2> aTensor_9_4_2;
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typedef Eigen::TensorFixedSize<SpinColourVector, Eigen::Sizes<6,5>> LSCTensor;
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#ifdef DEBUG
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typedef Eigen::TensorFixedSize<iMatrix<iVector<iMatrix<iVector<LorentzColourMatrix,5>,2>,7>,3>, Eigen::Sizes<2,2,11,10,9>, Eigen::StorageOptions::RowMajor> LCMTensor;
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#endif
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class PerambIOTestClass: Serializable {
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public:
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using PerambTensor = Eigen::Tensor<SpinColourVector, 6, Eigen::StorageOptions::RowMajor>;
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GRID_SERIALIZABLE_CLASS_MEMBERS(PerambIOTestClass
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, ShortRank5Tensor, shortRank5Tensor
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, PerambTensor, Perambulator
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, SpinColourVector, spinColourVector
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, SpinColourMatrix, spinColourMatrix
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, std::vector<std::string>, DistilParameterNames
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, std::vector<int>, DistilParameterValues
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, PerambTensor, Perambulator
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, PerambTensor, Perambulator2
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, SpinColourVector, scv
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, SpinColourMatrix, scm
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//, TestTensor, Critter
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//, TestTensorFixed, FixedCritter
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//, aTestTensorFixed, aFixedCritter
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//, LSCTensor, MyLSCTensor
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//, LCMTensor, MyLCMTensor
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, TensorRank5UShort, tensorRank5UShort
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, TensorRank3, tensorRank3
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, Tensor_9_4_2, tensor_9_4_2
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, aTensor_9_4_2, atensor_9_4_2
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, LSCTensor, MyLSCTensor
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#ifdef DEBUG
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, LCMTensor, MyLCMTensor
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#endif
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);
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PerambIOTestClass() : Perambulator(2,3,1,4,5,1), Perambulator2(7,1,6,1,5,1),
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DistilParameterNames {"alpha", "beta", "gamma", "delta", "epsilon", "what's f?"},
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DistilParameterValues{2,3,1,4,5,1}
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, shortRank5Tensor{5,4,3,2,1}
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//, Critter(7,3,2)//, aFixedCritter(3)
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PerambIOTestClass()
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: DistilParameterNames {"alpha", "beta", "gamma", "delta", "epsilon", "zeta"}
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, DistilParameterValues{2,3,1,4,5,1}
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, Perambulator(2,3,1,4,5,1)
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, Perambulator2(7,1,6,1,5,1)
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, tensorRank5UShort{5,4,3,2,1}
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, tensorRank3(7,3,2)
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{
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Sequential_Init(Perambulator);
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Sequential_Init(Perambulator2, {-3.1415927,7});
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Sequential_Init(shortRank5Tensor);
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Grid_complex<double> Flag{1,-3.1415927};
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SequentialInit(Perambulator, Flag);
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SequentialInit(Perambulator2, Flag);
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SequentialInit(tensorRank5UShort);
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SequentialInit(tensorRank3, Flag);
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SequentialInit(tensor_9_4_2, Flag);
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for( auto &t : atensor_9_4_2 )
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SequentialInit(t, Flag);
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SequentialInit( MyLSCTensor );
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#ifdef DEBUG
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SequentialInit( MyLCMTensor );
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#endif
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}
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};
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void EigenHdf5IOTest(void) {
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SpinColourVector scv, scv2;
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scv2 = scv;
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ioTest<Hdf5Writer, Hdf5Reader, SpinColourVector>("iotest_vector.h5", scv, "SpinColourVector");
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SpinColourMatrix scm;
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ioTest<Hdf5Writer, Hdf5Reader, SpinColourMatrix>("iotest_matrix.h5", scm, "SpinColourMatrix");
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#define TensorWriteReadInnerNoInit( T ) \
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ioTest<Hdf5Writer, Hdf5Reader, T>("iotest_"s + std::to_string(++TestNum) + "_" #T ".h5", t, #T);
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#define TensorWriteReadInner( T ) SequentialInit( t ); TensorWriteReadInnerNoInit( T )
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#define TensorWriteRead( T ) { T t ; TensorWriteReadInner( T ) }
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#define TensorWriteReadV(T, ... ) { T t( __VA_ARGS__ ); TensorWriteReadInner( T ) }
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#define TensorWriteReadLarge( T ) { std::unique_ptr<T> p{new T}; T &t{*p}; TensorWriteReadInnerNoInit(T) }
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constexpr TestScalar Inc{1,-1};
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void EigenHdf5IOTest(void)
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{
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unsigned int TestNum = 0;
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using namespace std::string_literals;
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TensorWriteRead( TensorSingle )
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TensorWriteReadV( TensorSimple, 1, 1, 1, 1, 1, 1 )
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TensorWriteReadV( TensorRank3, 6, 3, 2 )
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TensorWriteRead ( Tensor_9_4_2 )
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TensorWriteRead ( LSCTensor )
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TensorWriteReadLarge( PerambIOTestClass )
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#ifdef DEBUG
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std::cout << "sizeof( LCMTensor ) = " << sizeof( LCMTensor ) / 1024 / 1024 << " MB" << std::endl;
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TensorWriteReadLarge ( LCMTensor )
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// Also write > 4GB of complex numbers (I suspect this will fail inside Hdf5)
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{
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using TestTensorSingle = Eigen::TensorFixedSize<int, Eigen::Sizes<1>>;
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TestTensorSingle ts;
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ts(0) = 7; // lucky
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ioTest<Hdf5Writer, Hdf5Reader, TestTensorSingle>("iotest_single.h5", ts, "Tensor_single");
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static constexpr size_t Num = 0x11000000;
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std::cout << "Stress test: " << Num * sizeof( Grid_complex<double> ) / 1024 / 1024
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<< " MB array of complex<double>" << std::endl;
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using Stress = std::vector<Grid_complex<double>>;
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Stress t (Num);
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TensorWriteReadInnerNoInit( Stress );
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}
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{
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using TestTensorSimple = Eigen::Tensor<iMatrix<TestScalar,1>, 6>;
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TestTensorSimple ts(1,1,1,1,1,1);
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ts(0,0,0,0,0,0) = Inc * 3.1415927;
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ioTest<Hdf5Writer, Hdf5Reader, TestTensorSimple>("iotest_simple.h5", ts, "Tensor_simple");
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}
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TestTensor t(6,3,2);
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TestScalar Val{Inc};
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for( int i = 0 ; i < t.dimension(0) ; i++)
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for( int j = 0 ; j < t.dimension(1) ; j++)
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for( int k = 0 ; k < t.dimension(2) ; k++) {
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t(i,j,k) = Val;
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Val += Inc;
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}
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ioTest<Hdf5Writer, Hdf5Reader, TestTensor>("iotest_tensor.h5", t, "eigen_tensor_instance_name");
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//dump_tensor(t, "t");
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// Now serialise a fixed size tensor
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using FixedTensor = Eigen::TensorFixedSize<TestScalar, Eigen::Sizes<8,4,3>>;
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FixedTensor tf;
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Val = Inc;
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for( int i = 0 ; i < tf.dimension(0) ; i++)
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for( int j = 0 ; j < tf.dimension(1) ; j++)
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for( int k = 0 ; k < tf.dimension(2) ; k++) {
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tf(i,j,k) = Val;
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Val += Inc;
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}
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ioTest<Hdf5Writer, Hdf5Reader, FixedTensor>("iotest_tensor_fixed.h5", tf, "eigen_tensor_fixed_name");
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//dump_tensor(tf, "tf");
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PerambIOTestClass o;
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//dump_tensor(o.Perambulator, "Perambulator" );
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dump_tensor(o.shortRank5Tensor, "shortRank5Tensor");
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/*for_all( o.FixedCritter, [&](TestScalar &c, float f, const std::array<size_t,TestTensorFixed::NumIndices> &Dims ){
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c = TestScalar{f,-f};
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//std::cout << " a(" << Dims[0] << "," << Dims[1] << "," << Dims[2] << ")=" << c;
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} );
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for( auto &z : o.aFixedCritter )
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for_all( z, [&](TestScalar &c, float f, const std::array<size_t,TestTensorFixed::NumIndices> &Dims ){
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c = TestScalar{f,-f};
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//std::cout << " a(" << Dims[0] << "," << Dims[1] << "," << Dims[2] << ")=" << c;
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} );*/
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ioTest<Hdf5Writer, Hdf5Reader, PerambIOTestClass>("iotest_object.h5", o, "PerambIOTestClass_object_instance_name");
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//DumpMemoryOrder(o.Perambulator);
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// Tensor of spin colour
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LSCTensor l;
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Val = 0;
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for( int i = 0 ; i < l.dimension(0) ; i++)
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for( int j = 0 ; j < l.dimension(1) ; j++)
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for( int k = 0 ; k < l.dimension(2) ; k++)
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for( int s = 0 ; s < Ns ; s++ )
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for( int c = 0 ; c < Nc ; c++ )
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{
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l(i,j,k)()(s)(c) = Val;
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Val += Inc;
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}
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ioTest<Hdf5Writer, Hdf5Reader, LSCTensor>("iotest_LSCTensor.h5", l, "LSCTensor_object_instance_name");
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//dump_tensor(l, "l");
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// Tensor of spin colour
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LCMTensor l2;
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Val = 0;
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for( int i = 0 ; i < l2.dimension(0) ; i++)
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for( int j = 0 ; j < l2.dimension(1) ; j++)
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for( int k = 0 ; k < l2.dimension(2) ; k++)
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for( int l = 0 ; l < Ns ; l++ )
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for( int c = 0 ; c < Nc ; c++ )
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for( int c2 = 0 ; c2 < Nc ; c2++ )
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{
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l2(i,j,k)(l)()(c,c2) = Val;
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Val += Inc;
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}
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ioTest<Hdf5Writer, Hdf5Reader, LCMTensor>("iotest_LCMTensor.h5", l2, "LCMTensor_object_instance_name");
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//dump_tensor(l2, "l2");
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#endif
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}
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#endif
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