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Gotten rid of c++17
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5d6462b706
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@ -168,6 +168,32 @@ namespace Grid {
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//template <typename T> struct Traits<T, typename std::enable_if<is_tensor<T>::value, void>::type> : Traits<T> {};
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}
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// for_all helper function to call the lambda
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template <typename ETensor, typename Lambda>
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typename std::enable_if<EigenIO::is_tensor_of_scalar<ETensor>::value, void>::type
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for_all_do_lambda( Lambda lambda, typename ETensor::Scalar &scalar, typename ETensor::Index &Seq,
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std::array<std::size_t, ETensor::NumIndices + EigenIO::Traits<typename ETensor::Scalar>::rank_non_trivial> &MyIndex)
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{
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lambda( scalar, Seq++, MyIndex );
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}
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// for_all helper function to call the lambda
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template <typename ETensor, typename Lambda>
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typename std::enable_if<EigenIO::is_tensor_of_container<ETensor>::value, void>::type
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for_all_do_lambda( Lambda lambda, typename ETensor::Scalar &scalar, typename ETensor::Index Seq,
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std::array<std::size_t, ETensor::NumIndices + EigenIO::Traits<typename ETensor::Scalar>::rank_non_trivial> &MyIndex)
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{
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using Scalar = typename ETensor::Scalar; // This could be a Container - we'll check later
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const auto InnerRank = EigenIO::Traits<Scalar>::rank_non_trivial;
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const auto rank{ETensor::NumIndices};
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for( typename EigenIO::Traits<Scalar>::scalar_type &Source : scalar ) {
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lambda(Source, Seq++, MyIndex );
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// Now increment SubIndex
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for( auto i = InnerRank - 1; i != -1 && ++MyIndex[rank + i] == EigenIO::Traits<Scalar>::DimensionNT(i); i-- )
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MyIndex[i] = 0;
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}
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}
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// Calls a lamda (passing index and sequence number) for every member of an Eigen::Tensor
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// For efficiency, iteration proceeds in memory order,
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// ... but parameters guaranteed to be the same regardless of memory order
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@ -208,17 +234,7 @@ namespace Grid {
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Index Seq = 0;
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Scalar * pScalar = ET.data();
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for( std::size_t j = 0; j < NumScalars; j++ ) {
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// if constexpr is C++ 17 ... but otherwise need two specialisations (Container vs Scalar)
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if constexpr ( EigenIO::is_scalar<Scalar>::value ) {
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lambda( * pScalar, Seq++, MyIndex );
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} else {
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for( typename EigenIO::Traits<Scalar>::scalar_type &Source : * pScalar ) {
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lambda(Source, Seq++, MyIndex );
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// Now increment SubIndex
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for( auto i = rank + InnerRank - 1; i != rank - 1 && ++MyIndex[i] == Dims[i]; i-- )
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MyIndex[i] = 0;
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}
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}
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for_all_do_lambda<ETensor, Lambda>( lambda, * pScalar, Seq, MyIndex );
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// Now increment the index to pass to the lambda (bearing in mind we're walking in memory order)
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if( ETensor::Options & Eigen::RowMajor ) {
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for( auto i = rank - 1; i != -1 && ++MyIndex[i] == Dims[i]; i-- )
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@ -376,6 +392,36 @@ namespace Grid {
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typename std::enable_if<EigenIO::is_tensor<ETensor>::value, void>::type
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write(const std::string &s, const ETensor &output);
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// Helper functions for Scalar vs Container specialisations
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template <typename ETensor>
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inline typename std::enable_if<EigenIO::is_tensor_of_scalar<ETensor>::value, const typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type *>::type
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getFirstScalar(const ETensor &output)
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{
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return output.data();
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}
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template <typename ETensor>
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inline typename std::enable_if<EigenIO::is_tensor_of_container<ETensor>::value, const typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type *>::type
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getFirstScalar(const ETensor &output)
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{
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return output.data()->begin();
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}
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template <typename S>
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inline typename std::enable_if<EigenIO::is_scalar<S>::value, void>::type
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copyScalars(typename EigenIO::Traits<S>::scalar_type * &pCopy, const S &Source)
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{
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* pCopy ++ = Source;
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}
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template <typename S>
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inline typename std::enable_if<EigenIO::is_container<S>::value, void>::type
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copyScalars(typename EigenIO::Traits<S>::scalar_type * &pCopy, const S &Source)
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{
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for( const typename EigenIO::Traits<S>::scalar_type &item : Source )
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* pCopy ++ = item;
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}
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void scientificFormat(const bool set);
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bool isScientific(void);
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void setPrecision(const unsigned int prec);
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@ -417,12 +463,23 @@ namespace Grid {
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template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor_variable<ETensor>::value, void>::type
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Reshape(ETensor &t, const std::array<typename ETensor::Index, ETensor::NumDimensions> &dims );
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/*template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor_fixed<ETensor>::value, std::size_t>::type
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DimSize(ETensor &t, std::size_t dim );
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template <typename ETensor>
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typename std::enable_if<EigenIO::is_tensor_variable<ETensor>::value, std::size_t>::type
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DimSize(ETensor &t, std::size_t dim );*/
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// Helper functions for Scalar vs Container specialisations
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template <typename S>
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inline typename std::enable_if<EigenIO::is_scalar<S>::value, void>::type
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copyScalars(S &Dest, const typename EigenIO::Traits<S>::scalar_type * &pSource)
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{
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Dest = * pSource ++;
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}
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template <typename S>
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inline typename std::enable_if<EigenIO::is_container<S>::value, void>::type
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copyScalars(S &Dest, const typename EigenIO::Traits<S>::scalar_type * &pSource)
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{
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for( typename EigenIO::Traits<S>::scalar_type &item : Dest )
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item = * pSource ++;
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}
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protected:
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template <typename U>
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void fromString(U &output, const std::string &s);
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@ -498,7 +555,7 @@ namespace Grid {
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// Eigen::Tensors of Grid tensors (iScalar, iVector, iMatrix)
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template <typename T>
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template <typename ETensor/*, typename U, int N*/>
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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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Writer<T>::write(const std::string &s, const ETensor &output)
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{
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@ -528,10 +585,11 @@ namespace Grid {
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Scalar * pCopyBuffer = nullptr;
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const Index TotalNumElements = NumElements * Traits::count;
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if( !CopyData ) {
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if constexpr ( ContainerRank == 0 )
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/*if constexpr ( ContainerRank == 0 )
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pWriteBuffer = output.data();
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else
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pWriteBuffer = output.data()->begin();
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pWriteBuffer = output.data()->begin();*/
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pWriteBuffer = getFirstScalar( output );
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} else {
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// Regardless of the Eigen::Tensor storage order, the copy will be Row Major
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pCopyBuffer = static_cast<Scalar *>(malloc(TotalNumElements * sizeof(Scalar)));
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@ -540,12 +598,14 @@ namespace Grid {
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std::array<Index, TensorRank> MyIndex;
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for( auto &idx : MyIndex ) idx = 0;
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for( auto n = 0; n < NumElements; n++ ) {
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if constexpr ( ContainerRank == 0 )
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* pCopy ++ = output( MyIndex );
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const Container & c = output( MyIndex );
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/*if constexpr ( ContainerRank == 0 )
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* pCopy ++ = c;
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else {
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for( const Scalar &Source : output( MyIndex ) )
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for( const Scalar &Source : c )
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* pCopy ++ = Source;
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}
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}*/
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copyScalars( pCopy, c );
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// Now increment the index
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for( int i = output.NumDimensions - 1; i >= 0 && ++MyIndex[i] == output.dimension(i); i-- )
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MyIndex[i] = 0;
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@ -656,8 +716,8 @@ namespace Grid {
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using Scalar = typename Traits::scalar_type;
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// read the (flat) data and dimensionality
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std::vector<std::size_t> dimData;
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std::vector<Scalar> buf;
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std::vector<std::size_t> dimData;
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std::vector<Scalar> buf;
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upcast->readMultiDim( s, buf, dimData );
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// Make sure that the number of elements read matches dimensions read
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std::size_t NumElements = 1;
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@ -717,15 +777,16 @@ namespace Grid {
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// Copy the data into the tensor
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ETDims MyIndex;
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for( auto &d : MyIndex ) d = 0;
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std::size_t idx = 0;
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const Scalar * pSource = &buf[0];
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for( auto n = 0 ; n < NumElements ; n++ ) {
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Container & c = output( MyIndex );
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if constexpr ( EigenIO::is_scalar<Container>::value ) {
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/*if constexpr ( EigenIO::is_scalar<Container>::value ) {
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c = buf[idx++];
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} else {
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for( Scalar & s : c )
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s = buf[idx++];
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}
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}*/
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copyScalars( c, pSource );
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// Now increment the index
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for( int i = ETensor::NumDimensions - 1; i >= 0 && ++MyIndex[i] == dims[i]; i-- )
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MyIndex[i] = 0;
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