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rAKA/src/common/aka_types.hh
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/**
* Copyright (©) 2011-2023 EPFL (Ecole Polytechnique Fédérale de Lausanne)
* Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
*
* This file is part of Akantu
*
* Akantu is free software: you can redistribute it and/or modify it under the
* terms of the GNU Lesser General Public License as published by the Free
* Software Foundation, either version 3 of the License, or (at your option) any
* later version.
*
* Akantu is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
* A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
* details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Akantu. If not, see <http://www.gnu.org/licenses/>.
*/
/* -------------------------------------------------------------------------- */
#include "aka_common.hh"
#include "aka_compatibilty_with_cpp_standard.hh"
#include "aka_error.hh"
/* -------------------------------------------------------------------------- */
#include <algorithm>
#include <array>
#include <initializer_list>
#include <numeric>
#include <type_traits>
#ifndef AKANTU_AKA_TYPES_HH
#define AKANTU_AKA_TYPES_HH
/* -------------------------------------------------------------------------- */
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE akantu::Idx
#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Eigen::ColMajor
#define EIGEN_DEFAULT_IO_FORMAT \
Eigen::IOFormat(Eigen::StreamPrecision, Eigen::DontAlignCols, ", ", ", ", \
"[", "]", "[", "]")
/* -------------------------------------------------------------------------- */
#define EIGEN_MATRIXBASE_PLUGIN "aka_types_eigen_matrix_base_plugin.hh"
#define EIGEN_MATRIX_PLUGIN "aka_types_eigen_matrix_plugin.hh"
#define EIGEN_PLAINOBJECTBASE_PLUGIN \
"aka_types_eigen_plain_object_base_plugin.hh"
#include <Eigen/Dense>
#include <Eigen/Eigenvalues>
/* -------------------------------------------------------------------------- */
namespace aka {
template <typename T> struct is_eigen_map : public std::false_type {};
template <typename PlainObjectType, int MapOptions, typename StrideType>
struct is_eigen_map<Eigen::Map<PlainObjectType, MapOptions, StrideType>>
: public std::true_type {};
/* -------------------------------------------------------------------------- */
template <typename T> struct is_eigen_matrix : public std::false_type {};
template <typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows,
int _MaxCols>
struct is_eigen_matrix<
Eigen::Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>>
: public std::true_type {};
/* -------------------------------------------------------------------------- */
template <typename T> struct is_eigen_matrix_base : public std::false_type {};
template <typename Derived>
struct is_eigen_matrix_base<Eigen::MatrixBase<Derived>>
: public std::true_type {};
} // namespace aka
namespace akantu {
using Eigen::Ref;
template <typename T, Eigen::Index n = Eigen::Dynamic>
using Vector = Eigen::Matrix<T, n, 1>;
template <typename T, Eigen::Index m = Eigen::Dynamic,
Eigen::Index n = Eigen::Dynamic>
using Matrix = Eigen::Matrix<T, m, n>;
template <typename T, Eigen::Index n = Eigen::Dynamic>
using VectorProxy =
Eigen::Map<std::conditional_t<std::is_const<T>::value,
const Vector<std::remove_const_t<T>, n>,
Vector<std::remove_const_t<T>, n>>>;
template <typename T, Eigen::Index m = Eigen::Dynamic,
Eigen::Index n = Eigen::Dynamic>
using MatrixProxy =
Eigen::Map<std::conditional_t<std::is_const<T>::value,
const Matrix<std::remove_const_t<T>, m, n>,
Matrix<std::remove_const_t<T>, m, n>>>;
using VectorXr = Vector<Real>;
using MatrixXr = Matrix<Real>;
enum NormType : int8_t { L_1 = 1, L_2 = 2, L_inf = -1 };
struct TensorTraitBase {};
template <size_t n> struct TensorTrait : public TensorTraitBase {};
} // namespace akantu
namespace aka {
template <typename Derived>
using is_vector = aka::bool_constant<
std::remove_reference_t<std::decay_t<Derived>>::IsVectorAtCompileTime>;
template <class V> inline constexpr bool is_vector_v = is_vector<V>::value;
template <typename Derived> using is_matrix = aka::negation<is_vector<Derived>>;
template <class M> inline constexpr bool is_matrix_v = is_matrix<M>::value;
template <typename... Ds>
using are_vectors = aka::conjunction<is_vector<Ds>...>;
template <class... Vs>
inline constexpr bool are_vectors_v = are_vectors<Vs...>::value;
template <typename... Ds>
using are_matrices = aka::conjunction<is_matrix<Ds>...>;
template <class... Ms>
inline constexpr bool are_matrices_v = are_matrices<Ms...>::value;
template <typename... Ds>
using enable_if_matrices_t = std::enable_if_t<are_matrices<Ds...>::value>;
template <typename... Ds>
using enable_if_vectors_t = std::enable_if_t<are_vectors<Ds...>::value>;
/* -------------------------------------------------------------------------- */
template <typename T>
struct is_tensor : public std::is_base_of<akantu::TensorTraitBase, T> {};
template <typename PlainObjectType, int MapOptions, typename StrideType>
struct is_tensor<Eigen::Map<PlainObjectType, MapOptions, StrideType>>
: public std::true_type {};
template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
struct is_tensor<Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>>
: public std::true_type {};
template <typename T, Eigen::Index m, Eigen::Index n>
struct is_tensor<Eigen::Matrix<T, m, n>> : public std::true_type {};
template <typename T, size_t n>
using is_tensor_n = std::is_base_of<akantu::TensorTrait<n>, T>;
template <class T> inline constexpr bool is_tensor_v = is_tensor<T>::value;
template <class T, size_t n>
inline constexpr bool is_tensor_n_v = is_tensor_n<T, n>::value;
template <std::size_t n, typename T = void, typename... Ts>
using enable_if_tensors_n = std::enable_if<
aka::conjunction<
is_tensor_n<Ts, n>...,
std::is_same<
std::common_type_t<std::decay_t<typename Ts::value_type>...>,
std::decay_t<typename Ts::value_type>>...>::value,
T>;
template <std::size_t n, typename T = void, typename... Ts>
using enable_if_tensors_n_t = typename enable_if_tensors_n<n, T, Ts...>::type;
} // namespace aka
namespace akantu { // fwd declaration
template <typename T, Int ndim> class TensorBase;
template <typename T, Int ndim> class TensorProxy;
template <typename T, Int ndim> class Tensor;
} // namespace akantu
/* -------------------------------------------------------------------------- */
#include "aka_view_iterators.hh"
/* -------------------------------------------------------------------------- */
#include "aka_tensor.hh"
/* -------------------------------------------------------------------------- */
namespace akantu {
class ArrayBase;
/* -------------------------------------------------------------------------- */
namespace details {
template <typename T> struct MapPlainObjectType { using type = T; };
template <typename PlainObjectType, int MapOptions, typename StrideType>
struct MapPlainObjectType<
Eigen::Map<PlainObjectType, MapOptions, StrideType>> {
using type = PlainObjectType;
};
template <typename T>
using MapPlainObjectType_t = typename MapPlainObjectType<T>::type;
template <typename Scalar, Idx...> struct EigenMatrixViewHelper {};
template <typename Scalar, Idx RowsAtCompileTime>
struct EigenMatrixViewHelper<Scalar, RowsAtCompileTime> {
using type = Eigen::Matrix<Scalar, RowsAtCompileTime, 1>;
};
template <typename Scalar, Idx RowsAtCompileTime, Idx ColsAtCompileTime>
struct EigenMatrixViewHelper<Scalar, RowsAtCompileTime, ColsAtCompileTime> {
using type = Eigen::Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime>;
};
template <typename Scalar, Idx... sizes>
using EigenMatrixViewHelper_t =
typename EigenMatrixViewHelper<Scalar, sizes...>::type;
template <typename Array, Idx... sizes> class EigenView {
static_assert(sizeof...(sizes) == 1 or sizeof...(sizes) == 2,
"Eigen only supports Vector and Matrices");
private:
template <
class A = Array,
std::enable_if_t<aka::is_array<std::decay_t<A>>::value> * = nullptr>
auto array_size() const {
return array.get().size() * array.get().getNbComponent();
}
template <
class A = Array,
std::enable_if_t<not aka::is_array<std::decay_t<A>>::value> * = nullptr>
auto array_size() const {
return array.get().size();
}
using ArrayRef_t = decltype(std::ref(std::declval<Array>()));
public:
using size_type = typename std::decay_t<Array>::size_type;
using value_type = typename std::decay_t<Array>::value_type;
EigenView(Array && array, decltype(sizes)... sizes_)
: array(std::ref(array)), sizes_(sizes_...) {}
EigenView(Array && array) : array(array), sizes_(sizes...) {}
EigenView(const EigenView & other) = default;
EigenView(EigenView && other) noexcept = default;
auto operator=(const EigenView & other) -> EigenView & = default;
auto operator=(EigenView && other) noexcept -> EigenView & = default;
template <typename A = Array, std::enable_if_t<not std::is_const_v<
std::remove_reference_t<A>>> * = nullptr>
decltype(auto) begin() {
return aka::make_from_tuple<::akantu::view_iterator<
Eigen::Map<EigenMatrixViewHelper_t<value_type, sizes...>>>>(
std::tuple_cat(std::make_tuple(array.get().data()), sizes_));
}
template <typename A = Array, std::enable_if_t<not std::is_const_v<
std::remove_reference_t<A>>> * = nullptr>
decltype(auto) end() {
return aka::make_from_tuple<::akantu::view_iterator<
Eigen::Map<EigenMatrixViewHelper_t<value_type, sizes...>>>>(
std::tuple_cat(std::make_tuple(array.get().data() + array_size()),
sizes_));
}
decltype(auto) begin() const {
return aka::make_from_tuple<::akantu::view_iterator<
Eigen::Map<const EigenMatrixViewHelper_t<value_type, sizes...>>>>(
std::tuple_cat(std::make_tuple(array.get().data()), sizes_));
}
decltype(auto) end() const {
return aka::make_from_tuple<::akantu::view_iterator<
Eigen::Map<const EigenMatrixViewHelper_t<value_type, sizes...>>>>(
std::tuple_cat(std::make_tuple(array.get().data() + array_size()),
sizes_));
}
private:
ArrayRef_t array;
std::tuple<decltype(sizes)...> sizes_;
};
} // namespace details
template <Idx RowsAtCompileTime, typename Array>
decltype(auto) make_view(Array && array, Idx rows = RowsAtCompileTime) {
return details::EigenView<Array, RowsAtCompileTime>(
std::forward<Array>(array), rows);
}
template <Idx RowsAtCompileTime, Idx ColsAtCompileTime, typename Array>
decltype(auto) make_view(Array && array, Idx rows = RowsAtCompileTime,
Idx cols = ColsAtCompileTime) {
return details::EigenView<Array, RowsAtCompileTime, ColsAtCompileTime>(
std::forward<Array>(array), rows, cols);
}
template <Idx RowsAtCompileTime, typename Array>
decltype(auto) make_const_view(const Array & array,
Idx rows = RowsAtCompileTime) {
return make_view<RowsAtCompileTime>(array, rows);
}
template <Idx RowsAtCompileTime, Idx ColsAtCompileTime, typename Array>
decltype(auto) make_const_view(const Array & array,
Idx rows = RowsAtCompileTime,
Idx cols = ColsAtCompileTime) {
return make_view<RowsAtCompileTime, ColsAtCompileTime>(array, rows, cols);
}
} // namespace akantu
namespace Eigen {
template <typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::zero() {
return this->fill(0.);
}
/* -------------------------------------------------------------------------- */
/* Vector */
/* -------------------------------------------------------------------------- */
template <typename Derived>
template <typename ED, typename T,
std::enable_if_t<not std::is_const<T>::value and
ED::IsVectorAtCompileTime> *>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE decltype(auto)
MatrixBase<Derived>::begin() {
return ::akantu::view_iterator<typename Derived::Scalar>(
this->derived().data());
}
template <typename Derived>
template <typename ED, typename T,
std::enable_if_t<not std::is_const<T>::value and
ED::IsVectorAtCompileTime> *>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE decltype(auto)
MatrixBase<Derived>::end() {
return ::akantu::view_iterator<typename Derived::Scalar>(
this->derived().data() + this->size());
}
template <typename Derived>
template <typename ED, std::enable_if_t<ED::IsVectorAtCompileTime> *>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE decltype(auto)
MatrixBase<Derived>::begin() const {
using Scalar = typename Derived::Scalar;
return ::akantu::const_view_iterator<Scalar>(this->derived().data());
}
template <typename Derived>
template <typename ED, std::enable_if_t<ED::IsVectorAtCompileTime> *>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE decltype(auto)
MatrixBase<Derived>::end() const {
using Scalar = typename Derived::Scalar;
return ::akantu::const_view_iterator<Scalar>(this->derived().data() +
this->size());
}
/* -------------------------------------------------------------------------- */
/* Matrix */
/* -------------------------------------------------------------------------- */
template <typename Derived>
template <typename ED, typename T,
std::enable_if_t<not std::is_const<T>::value and
not ED::IsVectorAtCompileTime> *>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE decltype(auto)
MatrixBase<Derived>::begin() {
return ::akantu::view_iterator<
Map<Matrix<typename Derived::Scalar, Derived::RowsAtCompileTime, 1>>>(
this->derived().data(), this->rows());
}
template <typename Derived>
template <typename ED, typename T,
std::enable_if_t<not std::is_const<T>::value and
not ED::IsVectorAtCompileTime> *>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE decltype(auto)
MatrixBase<Derived>::end() {
return ::akantu::view_iterator<
Map<Matrix<typename Derived::Scalar, Derived::RowsAtCompileTime, 1>>>(
this->derived().data() + this->size(), this->rows());
}
template <typename Derived>
template <typename ED, std::enable_if_t<not ED::IsVectorAtCompileTime> *>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE decltype(auto)
MatrixBase<Derived>::begin() const {
using Scalar = typename Derived::Scalar;
return ::akantu::const_view_iterator<
Map<const Matrix<Scalar, Derived::RowsAtCompileTime, 1>>>(
const_cast<Scalar *>(this->derived().data()), this->rows());
}
template <typename Derived>
template <typename ED, std::enable_if_t<not ED::IsVectorAtCompileTime> *>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE decltype(auto)
MatrixBase<Derived>::end() const {
using Scalar = typename Derived::Scalar;
return ::akantu::const_view_iterator<
Map<const Matrix<Scalar, Derived::RowsAtCompileTime, 1>>>(
const_cast<Scalar *>(this->derived().data()) + this->size(),
this->rows());
}
template <typename Derived>
template <typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
MatrixBase<Derived>::eig(MatrixBase<OtherDerived> & values) const {
EigenSolver<akantu::details::MapPlainObjectType_t<std::decay_t<Derived>>>
solver(*this, false);
using OtherScalar = typename OtherDerived::Scalar;
// as advised by the Eigen developers even though this is a UB
// auto & values = const_cast<MatrixBase<OtherDerived> &>(values_);
if constexpr (std::is_floating_point<OtherScalar>{}) {
values = solver.eigenvalues().real();
} else {
values = solver.eigenvalues();
}
}
template <typename Derived>
template <typename D1, typename D2>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
MatrixBase<Derived>::eig(MatrixBase<D1> & values, MatrixBase<D2> & vectors,
bool sort) const {
EigenSolver<akantu::details::MapPlainObjectType_t<std::decay_t<Derived>>>
solver(*this, true);
// as advised by the Eigen developers even though this is a UB
// auto & values = const_cast<MatrixBase<D1> &>(values_);
// auto & vectors = const_cast<MatrixBase<D2> &>(vectors_);
auto norm = this->norm();
using OtherScalar = typename D1::Scalar;
if ((solver.eigenvectors().imag().template lpNorm<Infinity>() >
1e-15 * norm) and
std::is_floating_point<OtherScalar>::value) {
AKANTU_EXCEPTION("This matrix has complex eigenvectors()");
}
if (not sort) {
if constexpr (std::is_floating_point<OtherScalar>{}) {
values = solver.eigenvalues().real();
vectors = solver.eigenvectors().real();
} else {
values = solver.eigenvalues();
vectors = solver.eigenvectors();
}
return;
}
if (not std::is_floating_point<OtherScalar>::value) {
AKANTU_EXCEPTION("Cannot sort complex eigen values");
}
values = solver.eigenvalues().real();
PermutationMatrix<Dynamic> P(values.size());
P.setIdentity();
std::sort(P.indices().data(), P.indices().data() + P.indices().size(),
[&values](const Index & a, const Index & b) {
return (values(a) - values(b)) > 0;
});
if constexpr (std::is_floating_point<OtherScalar>{}) {
values = P.transpose() * values;
vectors = solver.eigenvectors().real() * P;
}
return;
}
template <typename Derived>
template <typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
MatrixBase<Derived>::eigh(const MatrixBase<OtherDerived> & values_) const {
SelfAdjointEigenSolver<
akantu::details::MapPlainObjectType_t<std::decay_t<Derived>>>
solver(*this, EigenvaluesOnly);
// as advised by the Eigen developers even though this is a UB
auto & values = const_cast<MatrixBase<OtherDerived> &>(values_);
values = solver.eigenvalues();
}
template <typename Derived>
template <typename D1, typename D2>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
MatrixBase<Derived>::eigh(const MatrixBase<D1> & values_,
const MatrixBase<D2> & vectors_, bool sort) const {
SelfAdjointEigenSolver<
akantu::details::MapPlainObjectType_t<std::decay_t<Derived>>>
solver(*this, ComputeEigenvectors);
// as advised by the Eigen developers, even though this is a UB
auto & values = const_cast<MatrixBase<D1> &>(values_);
auto & vectors = const_cast<MatrixBase<D2> &>(vectors_);
if (not sort) {
values = solver.eigenvalues();
vectors = solver.eigenvectors();
return;
}
values = solver.eigenvalues();
PermutationMatrix<Dynamic> P(values.size());
P.setIdentity();
std::sort(P.indices().data(), P.indices().data() + P.indices().size(),
[&values](const Index & a, const Index & b) {
return (values(a) - values(b)) > 0;
});
values = P.transpose() * values;
vectors = solver.eigenvectors() * P; // permutes the columns (eigen vectors)
}
} // namespace Eigen
namespace std {
template <typename POD1, typename POD2, int MapOptions, typename StrideType>
struct is_convertible<Eigen::Map<POD1, MapOptions, StrideType>,
Eigen::Map<POD2, MapOptions, StrideType>>
: aka::bool_constant<is_convertible<POD1, POD2>::value> {};
} // namespace std
#endif /* AKANTU_AKA_TYPES_HH */
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