include/eigenpy/decompositions/sparse/LDLT.hpp

+/*
+ * Copyright 2024 INRIA
+ */
+
+#ifndef __eigenpy_decompositions_sparse_ldlt_hpp__
+#define __eigenpy_decompositions_sparse_ldlt_hpp__
+
+#include "eigenpy/eigenpy.hpp"
+#include "eigenpy/decompositions/sparse/SimplicialCholesky.hpp"
+#include "eigenpy/utils/scalar-name.hpp"
+
+namespace eigenpy {
+
+template <typename _MatrixType, int _UpLo = Eigen::Lower,
+          typename _Ordering =
+              Eigen::AMDOrdering<typename _MatrixType::StorageIndex> >
+struct SimplicialLDLTVisitor
+    : public boost::python::def_visitor<
+          SimplicialLDLTVisitor<_MatrixType, _UpLo, _Ordering> > {
+  typedef SimplicialLDLTVisitor<_MatrixType, _UpLo, _Ordering> Visitor;
+  typedef _MatrixType MatrixType;
+
+  typedef Eigen::SimplicialLDLT<MatrixType> Solver;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1, MatrixType::Options>
+      DenseVectorXs;
+  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic,
+                        MatrixType::Options>
+      DenseMatrixXs;
+
+  template <class PyClass>
+  void visit(PyClass &cl) const {
+    cl.def(bp::init<>(bp::arg("self"), "Default constructor"))
+        .def(bp::init<MatrixType>(bp::args("self", "matrix"),
+                                  "Constructs and performs the LDLT "
+                                  "factorization from a given matrix."))
+
+        .def("vectorD", &vectorD, bp::arg("self"),
+             "Returns the diagonal vector D.")
+        .def(SimplicialCholeskyVisitor<Solver>());
+  }
+
+  static void expose() {
+    static const std::string classname =
+        "SimplicialLDLT_" + scalar_name<Scalar>::shortname();
+    expose(classname);
+  }
+
+  static void expose(const std::string &name) {
+    bp::class_<Solver, boost::noncopyable>(
+        name.c_str(),
+        "A direct sparse LDLT Cholesky factorizations.\n\n"
+        "This class provides a LDL^T Cholesky factorizations of sparse "
+        "matrices that are selfadjoint and positive definite."
+        "The factorization allows for solving A.X = B where X and B can be "
+        "either dense or sparse.\n\n"
+        "In order to reduce the fill-in, a symmetric permutation P is applied "
+        "prior to the factorization such that the factorized matrix is P A "
+        "P^-1.",
+        bp::no_init)
+        .def(SimplicialLDLTVisitor())
+        .def(IdVisitor<Solver>());
+  }
+
+ private:
+  static DenseVectorXs vectorD(const Solver &self) { return self.vectorD(); }
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef __eigenpy_decompositions_sparse_ldlt_hpp__

include/eigenpy/decompositions/sparse/LLT.hpp

+/*
+ * Copyright 2024 INRIA
+ */
+
+#ifndef __eigenpy_decompositions_sparse_llt_hpp__
+#define __eigenpy_decompositions_sparse_llt_hpp__
+
+#include "eigenpy/eigenpy.hpp"
+#include "eigenpy/decompositions/sparse/SimplicialCholesky.hpp"
+#include "eigenpy/utils/scalar-name.hpp"
+
+namespace eigenpy {
+
+template <typename _MatrixType, int _UpLo = Eigen::Lower,
+          typename _Ordering =
+              Eigen::AMDOrdering<typename _MatrixType::StorageIndex> >
+struct SimplicialLLTVisitor
+    : public boost::python::def_visitor<
+          SimplicialLLTVisitor<_MatrixType, _UpLo, _Ordering> > {
+  typedef SimplicialLLTVisitor<_MatrixType, _UpLo, _Ordering> Visitor;
+  typedef _MatrixType MatrixType;
+
+  typedef Eigen::SimplicialLLT<MatrixType> Solver;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1, MatrixType::Options>
+      DenseVectorXs;
+  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic,
+                        MatrixType::Options>
+      DenseMatrixXs;
+
+  template <class PyClass>
+  void visit(PyClass &cl) const {
+    cl.def(bp::init<>(bp::arg("self"), "Default constructor"))
+        .def(bp::init<MatrixType>(bp::args("self", "matrix"),
+                                  "Constructs and performs the LLT "
+                                  "factorization from a given matrix."))
+
+        .def(SimplicialCholeskyVisitor<Solver>());
+  }
+
+  static void expose() {
+    static const std::string classname =
+        "SimplicialLLT_" + scalar_name<Scalar>::shortname();
+    expose(classname);
+  }
+
+  static void expose(const std::string &name) {
+    bp::class_<Solver, boost::noncopyable>(
+        name.c_str(),
+        "A direct sparse LLT Cholesky factorizations.\n\n"
+        "This class provides a LL^T Cholesky factorizations of sparse matrices "
+        "that are selfadjoint and positive definite."
+        "The factorization allows for solving A.X = B where X and B can be "
+        "either dense or sparse.\n\n"
+        "In order to reduce the fill-in, a symmetric permutation P is applied "
+        "prior to the factorization such that the factorized matrix is P A "
+        "P^-1.",
+        bp::no_init)
+        .def(SimplicialLLTVisitor())
+        .def(IdVisitor<Solver>());
+  }
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef __eigenpy_decompositions_sparse_llt_hpp__

include/eigenpy/decompositions/sparse/SimplicialCholesky.hpp

+/*
+ * Copyright 2024 INRIA
+ */
+
+#ifndef __eigenpy_decompositions_sparse_simplicial_cholesky_hpp__
+#define __eigenpy_decompositions_sparse_simplicial_cholesky_hpp__
+
+#include "eigenpy/eigenpy.hpp"
+#include "eigenpy/eigen/EigenBase.hpp"
+#include "eigenpy/decompositions/sparse/SparseSolverBase.hpp"
+
+#include <Eigen/SparseCholesky>
+
+namespace eigenpy {
+
+template <typename SimplicialDerived>
+struct SimplicialCholeskyVisitor
+    : public boost::python::def_visitor<
+          SimplicialCholeskyVisitor<SimplicialDerived> > {
+  typedef SimplicialDerived Solver;
+
+  typedef typename SimplicialDerived::MatrixType MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1, MatrixType::Options>
+      DenseVectorXs;
+  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic,
+                        MatrixType::Options>
+      DenseMatrixXs;
+
+  template <class PyClass>
+  void visit(PyClass &cl) const {
+    cl.def("analyzePattern", &Solver::analyzePattern,
+           bp::args("self", "matrix"),
+           "Performs a symbolic decomposition on the sparcity of matrix.\n"
+           "This function is particularly useful when solving for several "
+           "problems having the same structure.")
+
+        .def(EigenBaseVisitor<Solver>())
+        .def(SparseSolverBaseVisitor<Solver>())
+
+        .def("matrixL", &matrixL, bp::arg("self"),
+             "Returns the lower triangular matrix L.")
+        .def("matrixU", &matrixU, bp::arg("self"),
+             "Returns the upper triangular matrix U.")
+
+        .def("compute",
+             (Solver & (Solver::*)(const MatrixType &matrix)) & Solver::compute,
+             bp::args("self", "matrix"),
+             "Computes the sparse Cholesky decomposition of a given matrix.",
+             bp::return_self<>())
+
+        .def("determinant", &Solver::determinant, bp::arg("self"),
+             "Returns the determinant of the underlying matrix from the "
+             "current factorization.")
+
+        .def("factorize", &Solver::factorize, bp::args("self", "matrix"),
+             "Performs a numeric decomposition of a given matrix.\n"
+             "The given matrix must has the same sparcity than the matrix on "
+             "which the symbolic decomposition has been performed.\n"
+             "See also analyzePattern().")
+
+        .def("info", &Solver::info, bp::arg("self"),
+             "NumericalIssue if the input contains INF or NaN values or "
+             "overflow occured. Returns Success otherwise.")
+
+        .def("setShift", &Solver::setShift,
+             (bp::args("self", "offset"), bp::arg("scale") = RealScalar(1)),
+             "Sets the shift parameters that will be used to adjust the "
+             "diagonal coefficients during the numerical factorization.\n"
+             "During the numerical factorization, the diagonal coefficients "
+             "are transformed by the following linear model: d_ii = offset + "
+             "scale * d_ii.\n"
+             "The default is the identity transformation with offset=0, and "
+             "scale=1.",
+             bp::return_self<>())
+
+        .def("permutationP", &Solver::permutationP, bp::arg("self"),
+             "Returns the permutation P.",
+             bp::return_value_policy<bp::copy_const_reference>())
+        .def("permutationPinv", &Solver::permutationPinv, bp::arg("self"),
+             "Returns the inverse P^-1 of the permutation P.",
+             bp::return_value_policy<bp::copy_const_reference>());
+  }
+
+ private:
+  static MatrixType matrixL(const Solver &self) { return self.matrixL(); }
+  static MatrixType matrixU(const Solver &self) { return self.matrixU(); }
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef __eigenpy_decompositions_sparse_simplicial_cholesky_hpp__

include/eigenpy/decompositions/sparse/SparseSolverBase.hpp

+/*
+ * Copyright 2024 INRIA
+ */
+
+#ifndef __eigenpy_decompositions_sparse_sparse_solver_base_hpp__
+#define __eigenpy_decompositions_sparse_sparse_solver_base_hpp__
+
+#include "eigenpy/eigenpy.hpp"
+#include "eigenpy/eigen/EigenBase.hpp"
+
+#include <Eigen/SparseCholesky>
+
+namespace eigenpy {
+
+template <typename SimplicialDerived>
+struct SparseSolverBaseVisitor
+    : public boost::python::def_visitor<
+          SparseSolverBaseVisitor<SimplicialDerived> > {
+  typedef SimplicialDerived Solver;
+
+  typedef typename SimplicialDerived::MatrixType MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1, MatrixType::Options>
+      DenseVectorXs;
+  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic,
+                        MatrixType::Options>
+      DenseMatrixXs;
+
+  template <class PyClass>
+  void visit(PyClass &cl) const {
+    cl.def("solve", &solve<DenseVectorXs>, bp::args("self", "b"),
+           "Returns the solution x of A x = b using the current "
+           "decomposition of A.")
+        .def("solve", &solve<DenseMatrixXs>, bp::args("self", "B"),
+             "Returns the solution X of A X = B using the current "
+             "decomposition of A where B is a right hand side matrix.")
+
+        .def("solve", &solve<MatrixType>, bp::args("self", "B"),
+             "Returns the solution X of A X = B using the current "
+             "decomposition of A where B is a right hand side matrix.");
+  }
+
+ private:
+  template <typename MatrixOrVector>
+  static MatrixOrVector solve(const Solver &self, const MatrixOrVector &vec) {
+    return self.solve(vec);
+  }
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef __eigenpy_decompositions_sparse_sparse_solver_base_hpp__

include/eigenpy/decompositions/sparse/accelerate/accelerate.hpp

+/*
+ * Copyright 2024 INRIA
+ */
+
+#ifndef __eigenpy_decomposition_sparse_accelerate_accelerate_hpp__
+#define __eigenpy_decomposition_sparse_accelerate_accelerate_hpp__
+
+#include "eigenpy/eigenpy.hpp"
+#include "eigenpy/eigen/EigenBase.hpp"
+#include "eigenpy/decompositions/sparse/SparseSolverBase.hpp"
+
+#include <Eigen/AccelerateSupport>
+
+namespace eigenpy {
+
+template <typename AccelerateDerived>
+struct AccelerateImplVisitor : public boost::python::def_visitor<
+                                   AccelerateImplVisitor<AccelerateDerived> > {
+  typedef AccelerateDerived Solver;
+
+  typedef typename AccelerateDerived::MatrixType MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef MatrixType CholMatrixType;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+
+  template <class PyClass>
+  void visit(PyClass &cl) const {
+    cl
+
+        .def("analyzePattern", &Solver::analyzePattern,
+             bp::args("self", "matrix"),
+             "Performs a symbolic decomposition on the sparcity of matrix.\n"
+             "This function is particularly useful when solving for several "
+             "problems having the same structure.")
+
+        .def(EigenBaseVisitor<Solver>())
+        .def(SparseSolverBaseVisitor<Solver>())
+
+        .def("compute",
+             (Solver & (Solver::*)(const MatrixType &matrix)) & Solver::compute,
+             bp::args("self", "matrix"),
+             "Computes the sparse Cholesky decomposition of a given matrix.",
+             bp::return_self<>())
+
+        .def("factorize", &Solver::factorize, bp::args("self", "matrix"),
+             "Performs a numeric decomposition of a given matrix.\n"
+             "The given matrix must has the same sparcity than the matrix on "
+             "which the symbolic decomposition has been performed.\n"
+             "See also analyzePattern().")
+
+        .def("info", &Solver::info, bp::arg("self"),
+             "NumericalIssue if the input contains INF or NaN values or "
+             "overflow occured. Returns Success otherwise.")
+
+        .def("setOrder", &Solver::setOrder, bp::arg("self"), "Set order");
+  }
+
+  static void expose(const std::string &name, const std::string &doc = "") {
+    bp::class_<Solver, boost::noncopyable>(name.c_str(), doc.c_str(),
+                                           bp::no_init)
+        .def(AccelerateImplVisitor())
+
+        .def(bp::init<>(bp::arg("self"), "Default constructor"))
+        .def(bp::init<MatrixType>(bp::args("self", "matrix"),
+                                  "Constructs and performs the "
+                                  "factorization from a given matrix."));
+  }
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef __eigenpy_decomposition_sparse_accelerate_accelerate_hpp__

include/eigenpy/decompositions/sparse/cholmod/CholmodBase.hpp

+/*
+ * Copyright 2024 INRIA
+ */
+
+#ifndef __eigenpy_decomposition_sparse_cholmod_cholmod_base_hpp__
+#define __eigenpy_decomposition_sparse_cholmod_cholmod_base_hpp__
+
+#include "eigenpy/eigenpy.hpp"
+#include "eigenpy/eigen/EigenBase.hpp"
+#include "eigenpy/decompositions/sparse/SparseSolverBase.hpp"
+
+#include <Eigen/CholmodSupport>
+
+namespace eigenpy {
+
+template <typename CholdmodDerived>
+struct CholmodBaseVisitor
+    : public boost::python::def_visitor<CholmodBaseVisitor<CholdmodDerived> > {
+  typedef CholdmodDerived Solver;
+
+  typedef typename CholdmodDerived::MatrixType MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef MatrixType CholMatrixType;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+
+  template <class PyClass>
+  void visit(PyClass &cl) const {
+    cl.def("analyzePattern", &Solver::analyzePattern,
+           bp::args("self", "matrix"),
+           "Performs a symbolic decomposition on the sparcity of matrix.\n"
+           "This function is particularly useful when solving for several "
+           "problems having the same structure.")
+
+        .def(EigenBaseVisitor<Solver>())
+        .def(SparseSolverBaseVisitor<Solver>())
+
+        .def("compute",
+             (Solver & (Solver::*)(const MatrixType &matrix)) & Solver::compute,
+             bp::args("self", "matrix"),
+             "Computes the sparse Cholesky decomposition of a given matrix.",
+             bp::return_self<>())
+
+        .def("determinant", &Solver::determinant, bp::arg("self"),
+             "Returns the determinant of the underlying matrix from the "
+             "current factorization.")
+
+        .def("factorize", &Solver::factorize, bp::args("self", "matrix"),
+             "Performs a numeric decomposition of a given matrix.\n"
+             "The given matrix must has the same sparcity than the matrix on "
+             "which the symbolic decomposition has been performed.\n"
+             "See also analyzePattern().")
+
+        .def("info", &Solver::info, bp::arg("self"),
+             "NumericalIssue if the input contains INF or NaN values or "
+             "overflow occured. Returns Success otherwise.")
+
+        .def("logDeterminant", &Solver::logDeterminant, bp::arg("self"),
+             "Returns the log determinant of the underlying matrix from the "
+             "current factorization.")
+
+        .def("setShift", &Solver::setShift, (bp::args("self", "offset")),
+             "Sets the shift parameters that will be used to adjust the "
+             "diagonal coefficients during the numerical factorization.\n"
+             "During the numerical factorization, the diagonal coefficients "
+             "are transformed by the following linear model: d_ii = offset + "
+             "d_ii.\n"
+             "The default is the identity transformation with offset=0.",
+             bp::return_self<>());
+  }
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef __eigenpy_decomposition_sparse_cholmod_cholmod_base_hpp__

include/eigenpy/decompositions/sparse/cholmod/CholmodDecomposition.hpp

+/*
+ * Copyright 2024 INRIA
+ */
+
+#ifndef __eigenpy_decomposition_sparse_cholmod_cholmod_decomposition_hpp__
+#define __eigenpy_decomposition_sparse_cholmod_cholmod_decomposition_hpp__
+
+#include "eigenpy/eigenpy.hpp"
+#include "eigenpy/decompositions/sparse/cholmod/CholmodBase.hpp"
+
+namespace eigenpy {
+
+template <typename CholdmodDerived>
+struct CholmodDecompositionVisitor
+    : public boost::python::def_visitor<
+          CholmodDecompositionVisitor<CholdmodDerived> > {
+  typedef CholdmodDerived Solver;
+
+  template <class PyClass>
+  void visit(PyClass &cl) const {
+    cl
+
+        .def(CholmodBaseVisitor<Solver>())
+        .def("setMode", &Solver::setMode, bp::args("self", "mode"),
+             "Set the mode for the Cholesky decomposition.");
+  }
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef
+        // __eigenpy_decomposition_sparse_cholmod_cholmod_decomposition_hpp__

include/eigenpy/decompositions/sparse/cholmod/CholmodSimplicialLDLT.hpp

+/*
+ * Copyright 2024 INRIA
+ */
+
+#ifndef __eigenpy_decomposition_sparse_cholmod_cholmod_simplicial_ldlt_hpp__
+#define __eigenpy_decomposition_sparse_cholmod_cholmod_simplicial_ldlt_hpp__
+
+#include "eigenpy/eigenpy.hpp"
+#include "eigenpy/decompositions/sparse/cholmod/CholmodDecomposition.hpp"
+#include "eigenpy/utils/scalar-name.hpp"
+
+namespace eigenpy {
+
+template <typename MatrixType_, int UpLo_ = Eigen::Lower>
+struct CholmodSimplicialLDLTVisitor
+    : public boost::python::def_visitor<
+          CholmodSimplicialLDLTVisitor<MatrixType_, UpLo_> > {
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  typedef Eigen::CholmodSimplicialLDLT<MatrixType_, UpLo_> Solver;
+
+  template <class PyClass>
+  void visit(PyClass &cl) const {
+    cl
+
+        .def(CholmodBaseVisitor<Solver>())
+        .def(bp::init<>(bp::arg("self"), "Default constructor"))
+        .def(bp::init<MatrixType>(bp::args("self", "matrix"),
+                                  "Constructs and performs the LDLT "
+                                  "factorization from a given matrix."))
+
+        ;
+  }
+
+  static void expose() {
+    static const std::string classname =
+        "CholmodSimplicialLDLT_" + scalar_name<Scalar>::shortname();
+    expose(classname);
+  }
+
+  static void expose(const std::string &name) {
+    bp::class_<Solver, boost::noncopyable>(
+        name.c_str(),
+        "A simplicial direct Cholesky (LDLT) factorization and solver based on "
+        "Cholmod.\n\n"
+        "This class allows to solve for A.X = B sparse linear problems via a "
+        "simplicial LL^T Cholesky factorization using the Cholmod library."
+        "This simplicial variant is equivalent to Eigen's built-in "
+        "SimplicialLDLT class."
+        "Therefore, it has little practical interest. The sparse matrix A must "
+        "be selfadjoint and positive definite."
+        "The vectors or matrices X and B can be either dense or sparse.",
+        bp::no_init)
+        .def(CholmodSimplicialLDLTVisitor());
+  }
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef
+        // __eigenpy_decomposition_sparse_cholmod_cholmod_simplicial_ldlt_hpp__

include/eigenpy/decompositions/sparse/cholmod/CholmodSimplicialLLT.hpp

+/*
+ * Copyright 2024 INRIA
+ */
+
+#ifndef __eigenpy_decomposition_sparse_cholmod_cholmod_simplicial_llt_hpp__
+#define __eigenpy_decomposition_sparse_cholmod_cholmod_simplicial_llt_hpp__
+
+#include "eigenpy/eigenpy.hpp"
+#include "eigenpy/decompositions/sparse/cholmod/CholmodDecomposition.hpp"
+#include "eigenpy/utils/scalar-name.hpp"
+
+namespace eigenpy {
+
+template <typename MatrixType_, int UpLo_ = Eigen::Lower>
+struct CholmodSimplicialLLTVisitor
+    : public boost::python::def_visitor<
+          CholmodSimplicialLLTVisitor<MatrixType_, UpLo_> > {
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  typedef Eigen::CholmodSimplicialLLT<MatrixType_, UpLo_> Solver;
+
+  template <class PyClass>
+  void visit(PyClass &cl) const {
+    cl
+
+        .def(CholmodBaseVisitor<Solver>())
+        .def(bp::init<>(bp::arg("self"), "Default constructor"))
+        .def(bp::init<MatrixType>(bp::args("self", "matrix"),
+                                  "Constructs and performs the LLT "
+                                  "factorization from a given matrix."))
+
+        ;
+  }
+
+  static void expose() {
+    static const std::string classname =
+        "CholmodSimplicialLLT_" + scalar_name<Scalar>::shortname();
+    expose(classname);
+  }
+
+  static void expose(const std::string &name) {
+    bp::class_<Solver, boost::noncopyable>(
+        name.c_str(),
+        "A simplicial direct Cholesky (LLT) factorization and solver based on "
+        "Cholmod.\n\n"
+        "This class allows to solve for A.X = B sparse linear problems via a "
+        "simplicial LL^T Cholesky factorization using the Cholmod library."
+        "This simplicial variant is equivalent to Eigen's built-in "
+        "SimplicialLLT class."
+        "Therefore, it has little practical interest. The sparse matrix A must "
+        "be selfadjoint and positive definite."
+        "The vectors or matrices X and B can be either dense or sparse.",
+        bp::no_init)
+        .def(CholmodSimplicialLLTVisitor());
+  }
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef
+        // __eigenpy_decomposition_sparse_cholmod_cholmod_simplicial_llt_hpp__

include/eigenpy/decompositions/sparse/cholmod/CholmodSupernodalLLT.hpp

+/*
+ * Copyright 2024 INRIA
+ */
+
+#ifndef __eigenpy_decomposition_sparse_cholmod_cholmod_supernodal_llt_hpp__
+#define __eigenpy_decomposition_sparse_cholmod_cholmod_supernodal_llt_hpp__
+
+#include "eigenpy/eigenpy.hpp"
+#include "eigenpy/decompositions/sparse/cholmod/CholmodDecomposition.hpp"
+#include "eigenpy/utils/scalar-name.hpp"
+
+namespace eigenpy {
+
+template <typename MatrixType_, int UpLo_ = Eigen::Lower>
+struct CholmodSupernodalLLTVisitor
+    : public boost::python::def_visitor<
+          CholmodSupernodalLLTVisitor<MatrixType_, UpLo_> > {
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  typedef Eigen::CholmodSupernodalLLT<MatrixType_, UpLo_> Solver;
+
+  template <class PyClass>
+  void visit(PyClass &cl) const {
+    cl
+
+        .def(CholmodBaseVisitor<Solver>())
+        .def(bp::init<>(bp::arg("self"), "Default constructor"))
+        .def(bp::init<MatrixType>(bp::args("self", "matrix"),
+                                  "Constructs and performs the LLT "
+                                  "factorization from a given matrix."))
+
+        ;
+  }
+
+  static void expose() {
+    static const std::string classname =
+        "CholmodSupernodalLLT_" + scalar_name<Scalar>::shortname();
+    expose(classname);
+  }
+
+  static void expose(const std::string &name) {
+    bp::class_<Solver, boost::noncopyable>(
+        name.c_str(),
+        "A supernodal direct Cholesky (LLT) factorization and solver based on "
+        "Cholmod.\n\n"
+        "This class allows to solve for A.X = B sparse linear problems via a "
+        "supernodal LL^T Cholesky factorization using the Cholmod library."
+        "This supernodal variant performs best on dense enough problems, e.g., "
+        "3D FEM, or very high order 2D FEM."
+        "The sparse matrix A must be selfadjoint and positive definite. The "
+        "vectors or matrices X and B can be either dense or sparse.",
+        bp::no_init)
+        .def(CholmodSupernodalLLTVisitor());
+  }
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef
+        // __eigenpy_decomposition_sparse_cholmod_cholmod_supernodal_llt_hpp__

include/eigenpy/deprecation-policy.hpp

+//
+// Copyright (C) 2020 INRIA
+// Copyright (C) 2024 LAAS-CNRS, INRIA
+//
+#ifndef __eigenpy_deprecation_hpp__
+#define __eigenpy_deprecation_hpp__
+
+#include "eigenpy/fwd.hpp"
+
+namespace eigenpy {
+
+enum class DeprecationType { DEPRECATION, FUTURE };
+
+namespace detail {
+
+inline PyObject *deprecationTypeToPyObj(DeprecationType dep) {
+  switch (dep) {
+    case DeprecationType::DEPRECATION:
+      return PyExc_DeprecationWarning;
+    case DeprecationType::FUTURE:
+      return PyExc_FutureWarning;
+    default:  // The switch handles all cases explicitly, this should never be
+              // triggered.
+      throw std::invalid_argument(
+          "Undefined DeprecationType - this should never be triggered.");
+  }
+}
+
+}  // namespace detail
+
+/// @brief A Boost.Python call policy which triggers a Python warning on
+/// precall.
+template <DeprecationType deprecation_type = DeprecationType::DEPRECATION,
+          class BasePolicy = bp::default_call_policies>
+struct deprecation_warning_policy : BasePolicy {
+  using result_converter = typename BasePolicy::result_converter;
+  using argument_package = typename BasePolicy::argument_package;
+
+  deprecation_warning_policy(const std::string &warning_msg)
+      : BasePolicy(), m_what(warning_msg) {}
+
+  std::string what() const { return m_what; }
+
+  const BasePolicy *derived() const {
+    return static_cast<const BasePolicy *>(this);
+  }
+
+  template <class ArgPackage>
+  bool precall(const ArgPackage &args) const {
+    PyErr_WarnEx(detail::deprecationTypeToPyObj(deprecation_type),
+                 m_what.c_str(), 1);
+    return derived()->precall(args);
+  }
+
+ protected:
+  const std::string m_what;
+};
+
+template <DeprecationType deprecation_type = DeprecationType::DEPRECATION,
+          class BasePolicy = bp::default_call_policies>
+struct deprecated_function
+    : deprecation_warning_policy<deprecation_type, BasePolicy> {
+  deprecated_function(const std::string &msg =
+                          "This function has been marked as deprecated, and "
+                          "will be removed in the future.")
+      : deprecation_warning_policy<deprecation_type, BasePolicy>(msg) {}
+};
+
+template <DeprecationType deprecation_type = DeprecationType::DEPRECATION,
+          class BasePolicy = bp::default_call_policies>
+struct deprecated_member
+    : deprecation_warning_policy<deprecation_type, BasePolicy> {
+  deprecated_member(const std::string &msg =
+                        "This attribute or method has been marked as "
+                        "deprecated, and will be removed in the future.")
+      : deprecation_warning_policy<deprecation_type, BasePolicy>(msg) {}
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef __eigenpy_deprecation_hpp__

include/eigenpy/details.hpp

 /*
  * Copyright 2014-2019, CNRS
- * Copyright 2018-2019, INRIA
+ * Copyright 2018-2024, INRIA
  */
 
 #ifndef __eigenpy_details_hpp__
 #define __eigenpy_details_hpp__
 
-#include "eigenpy/details/rvalue_from_python_data.hpp"
 #include "eigenpy/fwd.hpp"
-
-#include <patchlevel.h> // For PY_MAJOR_VERSION
-#include <numpy/arrayobject.h>
-#include <iostream>
-
+#include "eigenpy/eigen-allocator.hpp"
+#include "eigenpy/eigen-from-python.hpp"
+#include "eigenpy/eigen-to-python.hpp"
 #include "eigenpy/eigenpy.hpp"
+#include "eigenpy/exception.hpp"
+#include "eigenpy/numpy-type.hpp"
 #include "eigenpy/registration.hpp"
-#include "eigenpy/map.hpp"
-
-#define GET_PY_ARRAY_TYPE(array) PyArray_ObjectType(reinterpret_cast<PyObject *>(array), 0)
-
-namespace eigenpy
-{
-  template <typename SCALAR>  struct NumpyEquivalentType {};
-  template <> struct NumpyEquivalentType<double>  { enum { type_code = NPY_DOUBLE };};
-  template <> struct NumpyEquivalentType<int>     { enum { type_code = NPY_INT    };};
-  template <> struct NumpyEquivalentType<long>    { enum { type_code = NPY_LONG    };};
-  template <> struct NumpyEquivalentType<float>   { enum { type_code = NPY_FLOAT  };};
-  
-  template <typename SCALAR1, typename SCALAR2>
-  struct FromTypeToType : public boost::false_type {};
-  
-  template <typename SCALAR>
-  struct FromTypeToType<SCALAR,SCALAR> : public boost::true_type {};
-  
-  template <> struct FromTypeToType<int,long> : public boost::true_type {};
-  template <> struct FromTypeToType<int,float> : public boost::true_type {};
-  template <> struct FromTypeToType<int,double> : public boost::true_type {};
-  
-  template <> struct FromTypeToType<long,float> : public boost::true_type {};
-  template <> struct FromTypeToType<long,double> : public boost::true_type {};
-  
-  template <> struct FromTypeToType<float,double> : public boost::true_type {};
+#include "eigenpy/scalar-conversion.hpp"
 
-  namespace bp = boost::python;
+namespace eigenpy {
 
-  enum NP_TYPE
-  {
-    DEFAULT_TYPE,
-    MATRIX_TYPE,
-    ARRAY_TYPE
-  };
-  
-  struct NumpyType
-  {
-    
-    static NumpyType & getInstance()
-    {
-      static NumpyType instance;
-      return instance;
-    }
+template <typename EigenType,
+          typename BaseType = typename get_eigen_base_type<EigenType>::type,
+          typename Scalar = typename EigenType::Scalar>
+struct expose_eigen_type_impl;
 
-    operator bp::object () { return CurrentNumpyType; }
-
-    bp::object make(PyArrayObject* pyArray, bool copy = false)
-    { return make((PyObject*)pyArray,copy); }
-    
-    bp::object make(PyObject* pyObj, bool copy = false)
-    {
-      if (getType() == DEFAULT_TYPE) {
-        std::cerr <<
-          "eigenpy warning: you use the deprecated class numpy.matrix without explicily asking for it. "
-          "The default behaviour will change to numpy.array at next major release.\n"
-          "- Either call eigenpy.switchToNumpyMatrix() before using eigenpy to suppress this warning\n"
-          "- or call eigenpy.switchToNumpyArray() and adapt your code accordingly.\n"
-          "See https://github.com/stack-of-tasks/eigenpy/issues/87 for further details."
-          << std::endl;
-        switchToNumpyMatrix();
-      }
-      bp::object m;
-      if(PyType_IsSubtype(reinterpret_cast<PyTypeObject*>(CurrentNumpyType.ptr()),NumpyMatrixType))
-        m = NumpyMatrixObject(bp::object(bp::handle<>(pyObj)), bp::object(), copy);
-//        m = NumpyAsMatrixObject(bp::object(bp::handle<>(pyObj)));
-      else if(PyType_IsSubtype(reinterpret_cast<PyTypeObject*>(CurrentNumpyType.ptr()),NumpyArrayType))
-        m = bp::object(bp::handle<>(pyObj)); // nothing to do here
-
-      Py_INCREF(m.ptr());
-      return m;
-    }
-    
-    static void setNumpyType(bp::object & obj)
-    {
-      PyTypeObject * obj_type = PyType_Check(obj.ptr()) ? reinterpret_cast<PyTypeObject*>(obj.ptr()) : obj.ptr()->ob_type;
-      if(PyType_IsSubtype(obj_type,getInstance().NumpyMatrixType))
-        switchToNumpyMatrix();
-      else if(PyType_IsSubtype(obj_type,getInstance().NumpyArrayType))
-        switchToNumpyArray();
-    }
-    
-    static void switchToNumpyArray()
-    {
-      getInstance().CurrentNumpyType = getInstance().NumpyArrayObject;
-      getType() = ARRAY_TYPE;
-    }
-    
-    static void switchToNumpyMatrix()
-    {
-      getInstance().CurrentNumpyType = getInstance().NumpyMatrixObject;
-      getType() = MATRIX_TYPE;
-    }
-    
-    static NP_TYPE & getType()
-    {
-      static NP_TYPE np_type;
-      return np_type;
-    }
-
-  protected:
-    NumpyType()
-    {
-      pyModule = bp::import("numpy");
-#if PY_MAJOR_VERSION >= 3
-      // TODO I don't know why this Py_INCREF is necessary.
-      // Without it, the destructor of NumpyType SEGV sometimes.
-      Py_INCREF(pyModule.ptr());
-#endif
-      
-      NumpyMatrixObject = pyModule.attr("matrix");
-      NumpyMatrixType = reinterpret_cast<PyTypeObject*>(NumpyMatrixObject.ptr());
-      NumpyArrayObject = pyModule.attr("ndarray");
-      NumpyArrayType = reinterpret_cast<PyTypeObject*>(NumpyArrayObject.ptr());
-      //NumpyAsMatrixObject = pyModule.attr("asmatrix");
-      //NumpyAsMatrixType = reinterpret_cast<PyTypeObject*>(NumpyAsMatrixObject.ptr());
-      
-      CurrentNumpyType = NumpyMatrixObject; // default conversion
-      getType() = DEFAULT_TYPE;
-    }
+template <typename MatType, typename Scalar>
+struct expose_eigen_type_impl<MatType, Eigen::MatrixBase<MatType>, Scalar> {
+  static void run() {
+    if (check_registration<MatType>()) return;
 
-    bp::object CurrentNumpyType;
-    bp::object pyModule;
-    
-    // Numpy types
-    bp::object NumpyMatrixObject; PyTypeObject * NumpyMatrixType;
-    //bp::object NumpyAsMatrixObject; PyTypeObject * NumpyAsMatrixType;
-    bp::object NumpyArrayObject; PyTypeObject * NumpyArrayType;
-    
-  };
-  
-  template<typename MatType>
-  struct EigenObjectAllocator
-  {
-    typedef MatType Type;
-    typedef typename MatType::Scalar Scalar;
-    
-    static void allocate(PyArrayObject * pyArray, void * storage)
-    {
-      const int rows = (int)PyArray_DIMS(pyArray)[0];
-      const int cols = (int)PyArray_DIMS(pyArray)[1];
-      
-      Type * mat_ptr = new (storage) Type(rows,cols);
-      
-      if(NumpyEquivalentType<Scalar>::type_code == GET_PY_ARRAY_TYPE(pyArray))
-      {
-        *mat_ptr = MapNumpy<MatType,Scalar>::map(pyArray); // avoid useless cast
-        return;
-      }
-      
-      if(GET_PY_ARRAY_TYPE(pyArray) == NPY_INT)
-      {
-        *mat_ptr = MapNumpy<MatType,int>::map(pyArray).template cast<Scalar>();
-        return;
-      }
-      
-      if(GET_PY_ARRAY_TYPE(pyArray) == NPY_LONG)
-      {
-        *mat_ptr = MapNumpy<MatType,long>::map(pyArray).template cast<Scalar>();
-        return;
-      }
-      
-      if(GET_PY_ARRAY_TYPE(pyArray) == NPY_FLOAT)
-      {
-        *mat_ptr = MapNumpy<MatType,float>::map(pyArray).template cast<Scalar>();
-        return;
-      }
-      
-      if(GET_PY_ARRAY_TYPE(pyArray) == NPY_DOUBLE)
-      {
-        *mat_ptr = MapNumpy<MatType,double>::map(pyArray).template cast<Scalar>();
-        return;
-      }
-    }
-    
-    /// \brief Copy mat into the Python array using Eigen::Map
-    template<typename MatrixDerived>
-    static void copy(const Eigen::MatrixBase<MatrixDerived> & mat_,
-                     PyArrayObject * pyArray)
-    {
-      const MatrixDerived & mat = const_cast<const MatrixDerived &>(mat_.derived());
-      
-      if(NumpyEquivalentType<Scalar>::type_code == GET_PY_ARRAY_TYPE(pyArray))
-      {
-        MapNumpy<MatType,Scalar>::map(pyArray) = mat; // no cast needed
-        return;
-      }
-      
-      if(GET_PY_ARRAY_TYPE(pyArray) == NPY_INT)
-      {
-        MapNumpy<MatType,int>::map(pyArray) = mat.template cast<int>();
-        return;
-      }
-      
-      if(GET_PY_ARRAY_TYPE(pyArray) == NPY_LONG)
-      {
-        MapNumpy<MatType,long>::map(pyArray) = mat.template cast<long>();
-        return;
-      }
-      
-      if(GET_PY_ARRAY_TYPE(pyArray) == NPY_FLOAT)
-      {
-        MapNumpy<MatType,float>::map(pyArray) = mat.template cast<float>();
-        return;
-      }
-      
-      if(GET_PY_ARRAY_TYPE(pyArray) == NPY_DOUBLE)
-      {
-        MapNumpy<MatType,double>::map(pyArray) = mat.template cast<double>();
-        return;
-      }
-    }
-  };
-  
-#if EIGEN_VERSION_AT_LEAST(3,2,0)
-  template<typename MatType>
-  struct EigenObjectAllocator< eigenpy::Ref<MatType> >
-  {
-    typedef eigenpy::Ref<MatType> Type;
-    typedef typename MatType::Scalar Scalar;
-    
-    static void allocate(PyArrayObject * pyArray, void * storage)
-    {
-      typename MapNumpy<MatType,Scalar>::EigenMap numpyMap = MapNumpy<MatType,Scalar>::map(pyArray);
-      new (storage) Type(numpyMap);
-    }
-    
-    static void copy(Type const & mat, PyArrayObject * pyArray)
-    {
-      EigenObjectAllocator<MatType>::copy(mat,pyArray);
-    }
-  };
+    // to-python
+    EigenToPyConverter<MatType>::registration();
+#if EIGEN_VERSION_AT_LEAST(3, 2, 0)
+    EigenToPyConverter<Eigen::Ref<MatType> >::registration();
+    EigenToPyConverter<const Eigen::Ref<const MatType> >::registration();
 #endif
-  
-  /* --- TO PYTHON -------------------------------------------------------------- */
-  
-  template<typename MatType>
-  struct EigenToPy
-  {
-    static PyObject* convert(MatType const & mat)
-    {
-      typedef typename MatType::Scalar Scalar;
-      assert( (mat.rows()<INT_MAX) && (mat.cols()<INT_MAX) 
-	      && "Matrix range larger than int ... should never happen." );
-      const int R  = (int)mat.rows(), C = (int)mat.cols();
 
-      PyArrayObject* pyArray;
-      // Allocate Python memory
-      if(C == 1 && NumpyType::getType() == ARRAY_TYPE) // Handle array with a single dimension
-      {
-        npy_intp shape[1] = { R };
-        pyArray = (PyArrayObject*) PyArray_SimpleNew(1, shape,
-                                                     NumpyEquivalentType<Scalar>::type_code);
-      }
-      else
-      {
-        npy_intp shape[2] = { R,C };
-        pyArray = (PyArrayObject*) PyArray_SimpleNew(2, shape,
-                                                     NumpyEquivalentType<Scalar>::type_code);
-      }
-
-      // Allocate memory
-      EigenObjectAllocator<MatType>::copy(mat,pyArray);
-      
-      // Create an instance (either np.array or np.matrix)
-      return NumpyType::getInstance().make(pyArray).ptr();
-    }
-  };
-  
-  /* --- FROM PYTHON ------------------------------------------------------------ */
-
-  template<typename MatType>
-  struct EigenFromPy
-  {
-    /// \brief Determine if pyObj can be converted into a MatType object
-    static void* convertible(PyArrayObject* pyArray)
-    {
-      if(!PyArray_Check(pyArray))
-        return 0;
-
-      if(MatType::IsVectorAtCompileTime)
-      {
-        // Special care of scalar matrix of dimension 1x1.
-        if(PyArray_DIMS(pyArray)[0] == 1 && PyArray_DIMS(pyArray)[1] == 1)
-          return pyArray;
-        
-        if(PyArray_DIMS(pyArray)[0] > 1 && PyArray_DIMS(pyArray)[1] > 1)
-        {
-#ifndef NDEBUG
-          std::cerr << "The number of dimension of the object does not correspond to a vector" << std::endl;
-#endif
-          return 0;
-        }
-        
-        if(((PyArray_DIMS(pyArray)[0] == 1) && (MatType::ColsAtCompileTime == 1))
-           || ((PyArray_DIMS(pyArray)[1] == 1) && (MatType::RowsAtCompileTime == 1)))
-        {
-#ifndef NDEBUG
-          if(MatType::ColsAtCompileTime == 1)
-            std::cerr << "The object is not a column vector" << std::endl;
-          else
-            std::cerr << "The object is not a row vector" << std::endl;
-#endif
-          return 0;
-        }
-      }
-      
-      if(PyArray_NDIM(pyArray) != 2)
-      {
-        if ( (PyArray_NDIM(pyArray) !=1) || (! MatType::IsVectorAtCompileTime) )
-        {
-#ifndef NDEBUG
-          std::cerr << "The number of dimension of the object is not correct." << std::endl;
-#endif
-          return 0;
-        }
-      }
-      
-      if(PyArray_NDIM(pyArray) == 2)
-      {
-        const int R = (int)PyArray_DIMS(pyArray)[0];
-        const int C = (int)PyArray_DIMS(pyArray)[1];
-        
-        if( (MatType::RowsAtCompileTime!=R)
-           && (MatType::RowsAtCompileTime!=Eigen::Dynamic) )
-          return 0;
-        if( (MatType::ColsAtCompileTime!=C)
-           && (MatType::ColsAtCompileTime!=Eigen::Dynamic) )
-          return 0;
-      }
-      
-      // Check if the Scalar type of the obj_ptr is compatible with the Scalar type of MatType
-      if(GET_PY_ARRAY_TYPE(pyArray) == NPY_INT)
-      {
-        if(!FromTypeToType<int,typename MatType::Scalar>::value)
-        {
-#ifndef NDEBUG
-          std::cerr << "The Python matrix scalar type (int) cannot be converted into the scalar type of the Eigen matrix. Loss of arithmetic precision" << std::endl;
-#endif
-          return 0;
-        }
-      }
-      else if(GET_PY_ARRAY_TYPE(pyArray) == NPY_LONG)
-      {
-        if(!FromTypeToType<long,typename MatType::Scalar>::value)
-        {
-#ifndef NDEBUG
-          std::cerr << "The Python matrix scalar type (long) cannot be converted into the scalar type of the Eigen matrix. Loss of arithmetic precision" << std::endl;
-#endif
-          return 0;
-        }
-      }
-      else if(GET_PY_ARRAY_TYPE(pyArray) == NPY_FLOAT)
-      {
-        if(!FromTypeToType<float,typename MatType::Scalar>::value)
-        {
-#ifndef NDEBUG
-          std::cerr << "The Python matrix scalar type (float) cannot be converted into the scalar type of the Eigen matrix. Loss of arithmetic precision" << std::endl;
-#endif
-          return 0;
-        }
-      }
-      else if(GET_PY_ARRAY_TYPE(pyArray) == NPY_DOUBLE)
-      {
-        if(!FromTypeToType<double,typename MatType::Scalar>::value)
-        {
-#ifndef NDEBUG
-          std::cerr << "The Python matrix scalar (double) type cannot be converted into the scalar type of the Eigen matrix. Loss of arithmetic precision." << std::endl;
-#endif
-          return 0;
-        }
-      }
-      else if(GET_PY_ARRAY_TYPE(pyArray) != NumpyEquivalentType<typename MatType::Scalar>::type_code)
-      {
-#ifndef NDEBUG
-        std::cerr << "The internal type as no Eigen equivalent." << std::endl;
-#endif
-        
-        return 0;
-      }
-      
-#ifdef NPY_1_8_API_VERSION
-      if(!(PyArray_FLAGS(pyArray)))
-#else
-      if(!(PyArray_FLAGS(pyArray) & NPY_ALIGNED))
-#endif
-        {
-#ifndef NDEBUG
-          std::cerr << "NPY non-aligned matrices are not implemented." << std::endl;
-#endif
-          return 0;
-        }
-      
-      return pyArray;
-    }
- 
-    /// \brief Allocate memory and copy pyObj in the new storage
-    static void construct(PyObject* pyObj,
-                          bp::converter::rvalue_from_python_stage1_data* memory)
-    {
-      PyArrayObject * pyArray = reinterpret_cast<PyArrayObject*>(pyObj);
-      assert((PyArray_DIMS(pyArray)[0]<INT_MAX) && (PyArray_DIMS(pyArray)[1]<INT_MAX));
-      
-      void* storage = ((bp::converter::rvalue_from_python_storage<MatType>*)
-                       ((void*)memory))->storage.bytes;
-      
-      EigenObjectAllocator<MatType>::allocate(pyArray,storage);
-
-      memory->convertible = storage;
-    }
-    
-    static void registration()
-    {
-      bp::converter::registry::push_back
-      (reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
-       &EigenFromPy::construct,bp::type_id<MatType>());
-    }
-  };
-  
-  template<typename MatType>
-  struct EigenFromPy< Eigen::MatrixBase<MatType> >
-  {
-    typedef EigenFromPy<MatType> EigenFromPyDerived;
-    typedef Eigen::MatrixBase<MatType> Base;
-    
-    /// \brief Determine if pyObj can be converted into a MatType object
-    static void* convertible(PyArrayObject* pyObj)
-    {
-      return EigenFromPyDerived::convertible(pyObj);
-    }
-    
-    /// \brief Allocate memory and copy pyObj in the new storage
-    static void construct(PyObject* pyObj,
-                          bp::converter::rvalue_from_python_stage1_data* memory)
-    {
-      EigenFromPyDerived::construct(pyObj,memory);
-    }
-    
-    static void registration()
-    {
-      bp::converter::registry::push_back
-      (reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
-       &EigenFromPy::construct,bp::type_id<Base>());
-    }
-  };
-  
-#define numpy_import_array() {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); } }
-  
-  template<typename MatType,typename EigenEquivalentType>
-  void enableEigenPySpecific()
-  {
-    enableEigenPySpecific<MatType>();
+    // from-python
+    EigenFromPyConverter<MatType>::registration();
   }
-  
-  template<typename MatType>
-  struct EigenFromPyConverter
-  {
-    static void registration()
-    {
-      EigenFromPy<MatType>::registration();
-
-      // Add also conversion to Eigen::MatrixBase<MatType>
-      typedef Eigen::MatrixBase<MatType> MatTypeBase;
-      EigenFromPy<MatTypeBase>::registration();
-    }
-  };
-
-#if EIGEN_VERSION_AT_LEAST(3,2,0)
-  /// Template specialization for Eigen::Ref
-  template<typename MatType>
-  struct EigenFromPyConverter< eigenpy::Ref<MatType> >
-  {
-    static void registration()
-    {
-      bp::converter::registry::push_back
-      (reinterpret_cast<void *(*)(_object *)>(&EigenFromPy<MatType>::convertible),
-       &EigenFromPy<MatType>::construct,bp::type_id<MatType>());
-    }
-  };
-#endif
-  
-  template<typename MatType>
-  void enableEigenPySpecific()
-  {
-    numpy_import_array();
-    if(check_registration<MatType>()) return;
-    
-    bp::to_python_converter<MatType,EigenToPy<MatType> >();
+};
+
+template <typename MatType, typename Scalar>
+struct expose_eigen_type_impl<MatType, Eigen::SparseMatrixBase<MatType>,
+                              Scalar> {
+  static void run() {
+    if (check_registration<MatType>()) return;
+
+    // to-python
+    EigenToPyConverter<MatType>::registration();
+    // #if EIGEN_VERSION_AT_LEAST(3, 2, 0)
+    //     EigenToPyConverter<Eigen::Ref<MatType> >::registration();
+    //     EigenToPyConverter<const Eigen::Ref<const MatType> >::registration();
+    // #endif
+
+    // from-python
     EigenFromPyConverter<MatType>::registration();
-   
   }
+};
+
+#ifdef EIGENPY_WITH_TENSOR_SUPPORT
+template <typename TensorType, typename Scalar>
+struct expose_eigen_type_impl<TensorType, Eigen::TensorBase<TensorType>,
+                              Scalar> {
+  static void run() {
+    if (check_registration<TensorType>()) return;
+
+    // to-python
+    EigenToPyConverter<TensorType>::registration();
+    EigenToPyConverter<Eigen::TensorRef<TensorType> >::registration();
+    EigenToPyConverter<
+        const Eigen::TensorRef<const TensorType> >::registration();
+
+    // from-python
+    EigenFromPyConverter<TensorType>::registration();
+  }
+};
+#endif
+
+template <typename MatType>
+void enableEigenPySpecific() {
+  expose_eigen_type_impl<MatType>::run();
+}
 
-} // namespace eigenpy
+}  // namespace eigenpy
 
-#endif // ifndef __eigenpy_details_hpp__
+#endif  // ifndef __eigenpy_details_hpp__

include/eigenpy/details/rvalue_from_python_data.hpp

-#ifndef __eigenpy_details_rvalue_from_python_data_hpp__
-#define __eigenpy_details_rvalue_from_python_data_hpp__
-
-#include <boost/python/converter/rvalue_from_python_data.hpp>
-#include <Eigen/Core>
-
-namespace boost
-{
-  namespace python
-  {
-    namespace converter
-    {
-  
-      /// \brief Template specialization of rvalue_from_python_data
-      template<typename Derived>
-      struct rvalue_from_python_data<Eigen::MatrixBase<Derived> const & >
-      : rvalue_from_python_storage<Eigen::MatrixBase<Derived> const & >
-      {
-        typedef Eigen::MatrixBase<Derived> const & T;
-        
-# if (!defined(__MWERKS__) || __MWERKS__ >= 0x3000) \
-&& (!defined(__EDG_VERSION__) || __EDG_VERSION__ >= 245) \
-&& (!defined(__DECCXX_VER) || __DECCXX_VER > 60590014) \
-&& !defined(BOOST_PYTHON_SYNOPSIS) /* Synopsis' OpenCXX has trouble parsing this */
-        // This must always be a POD struct with m_data its first member.
-        BOOST_STATIC_ASSERT(BOOST_PYTHON_OFFSETOF(rvalue_from_python_storage<T>,stage1) == 0);
-# endif
-        
-        // The usual constructor
-        rvalue_from_python_data(rvalue_from_python_stage1_data const & _stage1)
-        {
-          this->stage1 = _stage1;
-        }
-        
-        // This constructor just sets m_convertible -- used by
-        // implicitly_convertible<> to perform the final step of the
-        // conversion, where the construct() function is already known.
-        rvalue_from_python_data(void* convertible)
-        {
-          this->stage1.convertible = convertible;
-        }
-        
-        // Destroys any object constructed in the storage.
-        ~rvalue_from_python_data()
-        {
-          if (this->stage1.convertible == this->storage.bytes)
-            static_cast<Derived *>((void *)this->storage.bytes)->~Derived();
-        }
-      };
-      
-    }
-  }
-} // namespace boost::python::converter
-
-#endif // ifndef __eigenpy_details_rvalue_from_python_data_hpp__

include/eigenpy/eigen-allocator.hpp

+//
+// Copyright (c) 2014-2023 CNRS INRIA
+//
+
+#ifndef __eigenpy_eigen_allocator_hpp__
+#define __eigenpy_eigen_allocator_hpp__
+
+#include "eigenpy/fwd.hpp"
+#include "eigenpy/numpy-map.hpp"
+#include "eigenpy/register.hpp"
+#include "eigenpy/scalar-conversion.hpp"
+#include "eigenpy/utils/is-aligned.hpp"
+
+namespace eigenpy {
+
+namespace details {
+template <typename MatType,
+          bool IsVectorAtCompileTime = MatType::IsVectorAtCompileTime>
+struct init_matrix_or_array {
+  static MatType *run(int rows, int cols, void *storage) {
+    if (storage)
+      return new (storage) MatType(rows, cols);
+    else
+      return new MatType(rows, cols);
+  }
+
+  static MatType *run(PyArrayObject *pyArray, void *storage = NULL) {
+    assert(PyArray_NDIM(pyArray) == 1 || PyArray_NDIM(pyArray) == 2);
+
+    int rows = -1, cols = -1;
+    const int ndim = PyArray_NDIM(pyArray);
+    if (ndim == 2) {
+      rows = (int)PyArray_DIMS(pyArray)[0];
+      cols = (int)PyArray_DIMS(pyArray)[1];
+    } else if (ndim == 1) {
+      rows = (int)PyArray_DIMS(pyArray)[0];
+      cols = 1;
+    }
+
+    return run(rows, cols, storage);
+  }
+};
+
+template <typename MatType>
+struct init_matrix_or_array<MatType, true> {
+  static MatType *run(int rows, int cols, void *storage) {
+    if (storage)
+      return new (storage) MatType(rows, cols);
+    else
+      return new MatType(rows, cols);
+  }
+
+  static MatType *run(int size, void *storage) {
+    if (storage)
+      return new (storage) MatType(size);
+    else
+      return new MatType(size);
+  }
+
+  static MatType *run(PyArrayObject *pyArray, void *storage = NULL) {
+    const int ndim = PyArray_NDIM(pyArray);
+    if (ndim == 1) {
+      const int size = (int)PyArray_DIMS(pyArray)[0];
+      return run(size, storage);
+    } else {
+      const int rows = (int)PyArray_DIMS(pyArray)[0];
+      const int cols = (int)PyArray_DIMS(pyArray)[1];
+      return run(rows, cols, storage);
+    }
+  }
+};
+
+#ifdef EIGENPY_WITH_TENSOR_SUPPORT
+template <typename Tensor>
+struct init_tensor {
+  static Tensor *run(PyArrayObject *pyArray, void *storage = NULL) {
+    enum { Rank = Tensor::NumDimensions };
+    assert(PyArray_NDIM(pyArray) == Rank);
+    typedef typename Tensor::Index Index;
+
+    Eigen::array<Index, Rank> dimensions;
+    for (int k = 0; k < PyArray_NDIM(pyArray); ++k)
+      dimensions[k] = PyArray_DIMS(pyArray)[k];
+
+    if (storage)
+      return new (storage) Tensor(dimensions);
+    else
+      return new Tensor(dimensions);
+  }
+};
+#endif
+
+template <typename MatType>
+struct check_swap_impl_matrix;
+
+template <typename EigenType,
+          typename BaseType = typename get_eigen_base_type<EigenType>::type>
+struct check_swap_impl;
+
+template <typename MatType>
+struct check_swap_impl<MatType, Eigen::MatrixBase<MatType> >
+    : check_swap_impl_matrix<MatType> {};
+
+template <typename MatType>
+struct check_swap_impl_matrix {
+  static bool run(PyArrayObject *pyArray,
+                  const Eigen::MatrixBase<MatType> &mat) {
+    if (PyArray_NDIM(pyArray) == 0) return false;
+    if (mat.rows() == PyArray_DIMS(pyArray)[0])
+      return false;
+    else
+      return true;
+  }
+};
+
+template <typename EigenType>
+bool check_swap(PyArrayObject *pyArray, const EigenType &mat) {
+  return check_swap_impl<EigenType>::run(pyArray, mat);
+}
+
+#ifdef EIGENPY_WITH_TENSOR_SUPPORT
+template <typename TensorType>
+struct check_swap_impl_tensor {
+  static bool run(PyArrayObject * /*pyArray*/, const TensorType & /*tensor*/) {
+    return false;
+  }
+};
+
+template <typename TensorType>
+struct check_swap_impl<TensorType, Eigen::TensorBase<TensorType> >
+    : check_swap_impl_tensor<TensorType> {};
+#endif
+
+// template <typename MatType>
+// struct cast_impl_matrix;
+//
+// template <typename EigenType,
+//           typename BaseType = typename get_eigen_base_type<EigenType>::type>
+// struct cast_impl;
+//
+// template <typename MatType>
+// struct cast_impl<MatType, Eigen::MatrixBase<MatType> >
+//     : cast_impl_matrix<MatType> {};
+//
+// template <typename MatType>
+// struct cast_impl_matrix
+//{
+//   template <typename NewScalar, typename MatrixIn, typename MatrixOut>
+//   static void run(const Eigen::MatrixBase<MatrixIn> &input,
+//                   const Eigen::MatrixBase<MatrixOut> &dest) {
+//     dest.const_cast_derived() = input.template cast<NewScalar>();
+//   }
+// };
+
+template <typename Scalar, typename NewScalar,
+          template <typename D> class EigenBase = Eigen::MatrixBase,
+          bool cast_is_valid = FromTypeToType<Scalar, NewScalar>::value>
+struct cast {
+  template <typename MatrixIn, typename MatrixOut>
+  static void run(const Eigen::MatrixBase<MatrixIn> &input,
+                  const Eigen::MatrixBase<MatrixOut> &dest) {
+    dest.const_cast_derived() = input.template cast<NewScalar>();
+  }
+};
+
+#ifdef EIGENPY_WITH_TENSOR_SUPPORT
+template <typename Scalar, typename NewScalar>
+struct cast<Scalar, NewScalar, Eigen::TensorRef, true> {
+  template <typename TensorIn, typename TensorOut>
+  static void run(const TensorIn &input, TensorOut &dest) {
+    dest = input.template cast<NewScalar>();
+  }
+};
+#endif
+
+template <typename Scalar, typename NewScalar,
+          template <typename D> class EigenBase>
+struct cast<Scalar, NewScalar, EigenBase, false> {
+  template <typename MatrixIn, typename MatrixOut>
+  static void run(const MatrixIn /*input*/, const MatrixOut /*dest*/) {
+    // do nothing
+    assert(false && "Must never happened");
+  }
+};
+
+}  // namespace details
+
+#define EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_MATRIX(MatType, Scalar, NewScalar, \
+                                                  pyArray, mat)               \
+  details::cast<Scalar, NewScalar>::run(                                      \
+      NumpyMap<MatType, Scalar>::map(pyArray,                                 \
+                                     details::check_swap(pyArray, mat)),      \
+      mat)
+
+#define EIGENPY_CAST_FROM_EIGEN_MATRIX_TO_PYARRAY(MatType, Scalar, NewScalar, \
+                                                  mat, pyArray)               \
+  details::cast<Scalar, NewScalar>::run(                                      \
+      mat, NumpyMap<MatType, NewScalar>::map(                                 \
+               pyArray, details::check_swap(pyArray, mat)))
+
+// Define specific cast for Windows and Mac
+#if defined _WIN32 || defined __CYGWIN__
+// Manage NPY_INT on Windows (NPY_INT32 is NPY_LONG).
+// See https://github.com/stack-of-tasks/eigenpy/pull/455
+#define EIGENPY_CAST_FROM_NUMPY_TO_EIGEN_SWITCH_OS_SPECIFIC( \
+    MatType, Scalar, pyArray, mat, CAST_MACRO)               \
+  case NPY_INT:                                              \
+    CAST_MACRO(MatType, int32_t, Scalar, pyArray, mat);      \
+    break;                                                   \
+  case NPY_UINT:                                             \
+    CAST_MACRO(MatType, uint32_t, Scalar, pyArray, mat);     \
+    break;
+#elif defined __APPLE__
+// Manage NPY_LONGLONG on Mac (NPY_INT64 is NPY_LONG).
+// long long and long are both the same type
+// but NPY_LONGLONG and NPY_LONG are different dtype.
+// See https://github.com/stack-of-tasks/eigenpy/pull/455
+#define EIGENPY_CAST_FROM_NUMPY_TO_EIGEN_SWITCH_OS_SPECIFIC( \
+    MatType, Scalar, pyArray, mat, CAST_MACRO)               \
+  case NPY_LONGLONG:                                         \
+    CAST_MACRO(MatType, int64_t, Scalar, pyArray, mat);      \
+    break;                                                   \
+  case NPY_ULONGLONG:                                        \
+    CAST_MACRO(MatType, uint64_t, Scalar, pyArray, mat);     \
+    break;
+#else
+#define EIGENPY_CAST_FROM_NUMPY_TO_EIGEN_SWITCH_OS_SPECIFIC( \
+    MatType, Scalar, pyArray, mat, CAST_MACRO)
+#endif
+
+/// Define casting between Numpy matrix type to Eigen type.
+#define EIGENPY_CAST_FROM_NUMPY_TO_EIGEN_SWITCH(                               \
+    pyArray_type_code, MatType, Scalar, pyArray, mat, CAST_MACRO)              \
+  switch (pyArray_type_code) {                                                 \
+    case NPY_BOOL:                                                             \
+      CAST_MACRO(MatType, bool, Scalar, pyArray, mat);                         \
+      break;                                                                   \
+    case NPY_INT8:                                                             \
+      CAST_MACRO(MatType, int8_t, Scalar, pyArray, mat);                       \
+      break;                                                                   \
+    case NPY_INT16:                                                            \
+      CAST_MACRO(MatType, int16_t, Scalar, pyArray, mat);                      \
+      break;                                                                   \
+    case NPY_INT32:                                                            \
+      CAST_MACRO(MatType, int32_t, Scalar, pyArray, mat);                      \
+      break;                                                                   \
+    case NPY_INT64:                                                            \
+      CAST_MACRO(MatType, int64_t, Scalar, pyArray, mat);                      \
+      break;                                                                   \
+    case NPY_UINT8:                                                            \
+      CAST_MACRO(MatType, uint8_t, Scalar, pyArray, mat);                      \
+      break;                                                                   \
+    case NPY_UINT16:                                                           \
+      CAST_MACRO(MatType, uint16_t, Scalar, pyArray, mat);                     \
+      break;                                                                   \
+    case NPY_UINT32:                                                           \
+      CAST_MACRO(MatType, uint32_t, Scalar, pyArray, mat);                     \
+      break;                                                                   \
+    case NPY_UINT64:                                                           \
+      CAST_MACRO(MatType, uint64_t, Scalar, pyArray, mat);                     \
+      break;                                                                   \
+    case NPY_FLOAT:                                                            \
+      CAST_MACRO(MatType, float, Scalar, pyArray, mat);                        \
+      break;                                                                   \
+    case NPY_CFLOAT:                                                           \
+      CAST_MACRO(MatType, std::complex<float>, Scalar, pyArray, mat);          \
+      break;                                                                   \
+    case NPY_DOUBLE:                                                           \
+      CAST_MACRO(MatType, double, Scalar, pyArray, mat);                       \
+      break;                                                                   \
+    case NPY_CDOUBLE:                                                          \
+      CAST_MACRO(MatType, std::complex<double>, Scalar, pyArray, mat);         \
+      break;                                                                   \
+    case NPY_LONGDOUBLE:                                                       \
+      CAST_MACRO(MatType, long double, Scalar, pyArray, mat);                  \
+      break;                                                                   \
+    case NPY_CLONGDOUBLE:                                                      \
+      CAST_MACRO(MatType, std::complex<long double>, Scalar, pyArray, mat);    \
+      break;                                                                   \
+      EIGENPY_CAST_FROM_NUMPY_TO_EIGEN_SWITCH_OS_SPECIFIC(                     \
+          MatType, Scalar, pyArray, mat, CAST_MACRO)                           \
+    default:                                                                   \
+      throw Exception("You asked for a conversion which is not implemented."); \
+  }
+
+template <typename EigenType>
+struct EigenAllocator;
+
+template <typename EigenType,
+          typename BaseType = typename get_eigen_base_type<EigenType>::type>
+struct eigen_allocator_impl;
+
+template <typename MatType>
+struct eigen_allocator_impl_matrix;
+
+template <typename MatType>
+struct eigen_allocator_impl<MatType, Eigen::MatrixBase<MatType> >
+    : eigen_allocator_impl_matrix<MatType> {};
+
+template <typename MatType>
+struct eigen_allocator_impl<const MatType, const Eigen::MatrixBase<MatType> >
+    : eigen_allocator_impl_matrix<const MatType> {};
+
+template <typename MatType>
+struct eigen_allocator_impl_matrix {
+  typedef MatType Type;
+  typedef typename MatType::Scalar Scalar;
+
+  static void allocate(
+      PyArrayObject *pyArray,
+      boost::python::converter::rvalue_from_python_storage<MatType> *storage) {
+    void *raw_ptr = storage->storage.bytes;
+    assert(is_aligned(raw_ptr, EIGENPY_DEFAULT_ALIGN_BYTES) &&
+           "The pointer is not aligned.");
+
+    Type *mat_ptr = details::init_matrix_or_array<Type>::run(pyArray, raw_ptr);
+    Type &mat = *mat_ptr;
+
+    copy(pyArray, mat);
+  }
+
+  /// \brief Copy Python array into the input matrix mat.
+  template <typename MatrixDerived>
+  static void copy(PyArrayObject *pyArray,
+                   const Eigen::MatrixBase<MatrixDerived> &mat_) {
+    MatrixDerived &mat = mat_.const_cast_derived();
+    const int pyArray_type_code = EIGENPY_GET_PY_ARRAY_TYPE(pyArray);
+    const int Scalar_type_code = Register::getTypeCode<Scalar>();
+
+    if (pyArray_type_code == Scalar_type_code) {
+      mat = NumpyMap<MatType, Scalar>::map(
+          pyArray, details::check_swap(pyArray, mat));  // avoid useless cast
+      return;
+    }
+    EIGENPY_CAST_FROM_NUMPY_TO_EIGEN_SWITCH(
+        pyArray_type_code, MatType, Scalar, pyArray, mat,
+        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_MATRIX);
+  }
+
+  /// \brief Copy mat into the Python array using Eigen::Map
+  template <typename MatrixDerived>
+  static void copy(const Eigen::MatrixBase<MatrixDerived> &mat_,
+                   PyArrayObject *pyArray) {
+    const MatrixDerived &mat =
+        const_cast<const MatrixDerived &>(mat_.derived());
+    const int pyArray_type_code = EIGENPY_GET_PY_ARRAY_TYPE(pyArray);
+    const int Scalar_type_code = Register::getTypeCode<Scalar>();
+
+    if (pyArray_type_code == Scalar_type_code)  // no cast needed
+    {
+      NumpyMap<MatType, Scalar>::map(pyArray,
+                                     details::check_swap(pyArray, mat)) = mat;
+      return;
+    }
+    throw Exception(
+        "Scalar conversion from Eigen to Numpy is not implemented.");
+  }
+};
+
+#ifdef EIGENPY_WITH_TENSOR_SUPPORT
+template <typename TensorType>
+struct eigen_allocator_impl_tensor;
+
+template <typename TensorType>
+struct eigen_allocator_impl<TensorType, Eigen::TensorBase<TensorType> >
+    : eigen_allocator_impl_tensor<TensorType> {};
+
+template <typename TensorType>
+struct eigen_allocator_impl<const TensorType,
+                            const Eigen::TensorBase<TensorType> >
+    : eigen_allocator_impl_tensor<const TensorType> {};
+
+template <typename TensorType>
+struct eigen_allocator_impl_tensor {
+  typedef typename TensorType::Scalar Scalar;
+  static void allocate(
+      PyArrayObject *pyArray,
+      boost::python::converter::rvalue_from_python_storage<TensorType>
+          *storage) {
+    void *raw_ptr = storage->storage.bytes;
+    assert(is_aligned(raw_ptr, EIGENPY_DEFAULT_ALIGN_BYTES) &&
+           "The pointer is not aligned.");
+
+    TensorType *tensor_ptr =
+        details::init_tensor<TensorType>::run(pyArray, raw_ptr);
+    TensorType &tensor = *tensor_ptr;
+
+    copy(pyArray, tensor);
+  }
+
+#define EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_TENSOR(TensorType, Scalar,         \
+                                                  NewScalar, pyArray, tensor) \
+  {                                                                           \
+    typename NumpyMap<TensorType, Scalar>::EigenMap pyArray_map =             \
+        NumpyMap<TensorType, Scalar>::map(                                    \
+            pyArray, details::check_swap(pyArray, tensor));                   \
+    details::cast<Scalar, NewScalar, Eigen::TensorRef>::run(pyArray_map,      \
+                                                            tensor);          \
+  }
+
+  /// \brief Copy Python array into the input matrix mat.
+  template <typename TensorDerived>
+  static void copy(PyArrayObject *pyArray, TensorDerived &tensor) {
+    const int pyArray_type_code = EIGENPY_GET_PY_ARRAY_TYPE(pyArray);
+    const int Scalar_type_code = Register::getTypeCode<Scalar>();
+
+    if (pyArray_type_code == Scalar_type_code) {
+      tensor = NumpyMap<TensorType, Scalar>::map(
+          pyArray, details::check_swap(pyArray, tensor));  // avoid useless cast
+      return;
+    }
+
+    EIGENPY_CAST_FROM_NUMPY_TO_EIGEN_SWITCH(
+        pyArray_type_code, TensorType, Scalar, pyArray, tensor,
+        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_TENSOR);
+  }
+
+#define EIGENPY_CAST_FROM_EIGEN_TENSOR_TO_PYARRAY(TensorType, Scalar,         \
+                                                  NewScalar, tensor, pyArray) \
+  {                                                                           \
+    typename NumpyMap<TensorType, NewScalar>::EigenMap pyArray_map =          \
+        NumpyMap<TensorType, NewScalar>::map(                                 \
+            pyArray, details::check_swap(pyArray, tensor));                   \
+    details::cast<Scalar, NewScalar, Eigen::TensorRef>::run(tensor,           \
+                                                            pyArray_map);     \
+  }
+
+  /// \brief Copy mat into the Python array using Eigen::Map
+  static void copy(const TensorType &tensor, PyArrayObject *pyArray) {
+    const int pyArray_type_code = EIGENPY_GET_PY_ARRAY_TYPE(pyArray);
+    const int Scalar_type_code = Register::getTypeCode<Scalar>();
+
+    if (pyArray_type_code == Scalar_type_code)  // no cast needed
+    {
+      NumpyMap<TensorType, Scalar>::map(
+          pyArray, details::check_swap(pyArray, tensor)) = tensor;
+      return;
+    }
+
+    throw Exception(
+        "Scalar conversion from Eigen to Numpy is not implemented.");
+  }
+};
+#endif
+
+#if EIGEN_VERSION_AT_LEAST(3, 2, 0)
+/// @brief Check if we need to allocate @tparam MatType to convert @param
+/// pyArray.
+/// @details do not allocate if:
+/// want row-major & data C-contiguous OR
+/// want col-major & data F-contiguous OR
+/// you want a compile-time vector
+/// in these cases, data layout fits desired view layout
+template <typename MatType>
+inline bool is_arr_layout_compatible_with_mat_type(PyArrayObject *pyArray) {
+  bool is_array_C_cont = PyArray_IS_C_CONTIGUOUS(pyArray);
+  bool is_array_F_cont = PyArray_IS_F_CONTIGUOUS(pyArray);
+  return (MatType::IsRowMajor && is_array_C_cont) ||
+         (!MatType::IsRowMajor && is_array_F_cont) ||
+         (MatType::IsVectorAtCompileTime &&
+          (is_array_C_cont || is_array_F_cont));
+}
+
+template <typename MatType, int Options, typename Stride>
+struct eigen_allocator_impl_matrix<Eigen::Ref<MatType, Options, Stride> > {
+  typedef Eigen::Ref<MatType, Options, Stride> RefType;
+  typedef typename MatType::Scalar Scalar;
+
+  typedef
+      typename ::boost::python::detail::referent_storage<RefType &>::StorageType
+          StorageType;
+
+  static void allocate(
+      PyArrayObject *pyArray,
+      ::boost::python::converter::rvalue_from_python_storage<RefType>
+          *storage) {
+    typedef typename StrideType<
+        MatType,
+        Eigen::internal::traits<RefType>::StrideType::InnerStrideAtCompileTime,
+        Eigen::internal::traits<RefType>::StrideType::
+            OuterStrideAtCompileTime>::type NumpyMapStride;
+
+    bool need_to_allocate = false;
+    const int pyArray_type_code = EIGENPY_GET_PY_ARRAY_TYPE(pyArray);
+    const int Scalar_type_code = Register::getTypeCode<Scalar>();
+    if (pyArray_type_code != Scalar_type_code) need_to_allocate |= true;
+    bool incompatible_layout =
+        !is_arr_layout_compatible_with_mat_type<MatType>(pyArray);
+    need_to_allocate |= incompatible_layout;
+    if (Options !=
+        Eigen::Unaligned)  // we need to check whether the memory is correctly
+                           // aligned and composed of a continuous segment
+    {
+      void *data_ptr = PyArray_DATA(pyArray);
+      if (!PyArray_ISONESEGMENT(pyArray) || !is_aligned(data_ptr, Options))
+        need_to_allocate |= true;
+    }
+
+    void *raw_ptr = storage->storage.bytes;
+    if (need_to_allocate) {
+      MatType *mat_ptr;
+      mat_ptr = details::init_matrix_or_array<MatType>::run(pyArray);
+      RefType mat_ref(*mat_ptr);
+
+      new (raw_ptr) StorageType(mat_ref, pyArray, mat_ptr);
+
+      RefType &mat = *reinterpret_cast<RefType *>(raw_ptr);
+      EigenAllocator<MatType>::copy(pyArray, mat);
+    } else {
+      assert(pyArray_type_code == Scalar_type_code);
+      typename NumpyMap<MatType, Scalar, Options, NumpyMapStride>::EigenMap
+          numpyMap =
+              NumpyMap<MatType, Scalar, Options, NumpyMapStride>::map(pyArray);
+      RefType mat_ref(numpyMap);
+      new (raw_ptr) StorageType(mat_ref, pyArray);
+    }
+  }
+
+  static void copy(RefType const &ref, PyArrayObject *pyArray) {
+    EigenAllocator<MatType>::copy(ref, pyArray);
+  }
+};
+
+template <typename MatType, int Options, typename Stride>
+struct eigen_allocator_impl_matrix<
+    const Eigen::Ref<const MatType, Options, Stride> > {
+  typedef const Eigen::Ref<const MatType, Options, Stride> RefType;
+  typedef typename MatType::Scalar Scalar;
+
+  typedef
+      typename ::boost::python::detail::referent_storage<RefType &>::StorageType
+          StorageType;
+
+  static void allocate(
+      PyArrayObject *pyArray,
+      ::boost::python::converter::rvalue_from_python_storage<RefType>
+          *storage) {
+    typedef typename StrideType<
+        MatType,
+        Eigen::internal::traits<RefType>::StrideType::InnerStrideAtCompileTime,
+        Eigen::internal::traits<RefType>::StrideType::
+            OuterStrideAtCompileTime>::type NumpyMapStride;
+
+    bool need_to_allocate = false;
+    const int pyArray_type_code = EIGENPY_GET_PY_ARRAY_TYPE(pyArray);
+    const int Scalar_type_code = Register::getTypeCode<Scalar>();
+
+    if (pyArray_type_code != Scalar_type_code) need_to_allocate |= true;
+    bool incompatible_layout =
+        !is_arr_layout_compatible_with_mat_type<MatType>(pyArray);
+    need_to_allocate |= incompatible_layout;
+    if (Options !=
+        Eigen::Unaligned)  // we need to check whether the memory is correctly
+                           // aligned and composed of a continuous segment
+    {
+      void *data_ptr = PyArray_DATA(pyArray);
+      if (!PyArray_ISONESEGMENT(pyArray) || !is_aligned(data_ptr, Options))
+        need_to_allocate |= true;
+    }
+
+    void *raw_ptr = storage->storage.bytes;
+    if (need_to_allocate) {
+      MatType *mat_ptr;
+      mat_ptr = details::init_matrix_or_array<MatType>::run(pyArray);
+      RefType mat_ref(*mat_ptr);
+
+      new (raw_ptr) StorageType(mat_ref, pyArray, mat_ptr);
+
+      MatType &mat = *mat_ptr;
+      EigenAllocator<MatType>::copy(pyArray, mat);
+    } else {
+      assert(pyArray_type_code == Scalar_type_code);
+      typename NumpyMap<MatType, Scalar, Options, NumpyMapStride>::EigenMap
+          numpyMap =
+              NumpyMap<MatType, Scalar, Options, NumpyMapStride>::map(pyArray);
+      RefType mat_ref(numpyMap);
+      new (raw_ptr) StorageType(mat_ref, pyArray);
+    }
+  }
+
+  static void copy(RefType const &ref, PyArrayObject *pyArray) {
+    EigenAllocator<MatType>::copy(ref, pyArray);
+  }
+};
+#endif
+
+#ifdef EIGENPY_WITH_TENSOR_SUPPORT
+
+template <typename TensorType, typename TensorRef>
+struct eigen_allocator_impl_tensor_ref;
+
+template <typename TensorType>
+struct eigen_allocator_impl_tensor<Eigen::TensorRef<TensorType> >
+    : eigen_allocator_impl_tensor_ref<TensorType,
+                                      Eigen::TensorRef<TensorType> > {};
+
+template <typename TensorType>
+struct eigen_allocator_impl_tensor<const Eigen::TensorRef<const TensorType> >
+    : eigen_allocator_impl_tensor_ref<
+          const TensorType, const Eigen::TensorRef<const TensorType> > {};
+
+template <typename TensorType, typename RefType>
+struct eigen_allocator_impl_tensor_ref {
+  typedef typename TensorType::Scalar Scalar;
+
+  typedef
+      typename ::boost::python::detail::referent_storage<RefType &>::StorageType
+          StorageType;
+
+  static void allocate(
+      PyArrayObject *pyArray,
+      ::boost::python::converter::rvalue_from_python_storage<RefType>
+          *storage) {
+    //    typedef typename StrideType<
+    //        MatType,
+    //        Eigen::internal::traits<RefType>::StrideType::InnerStrideAtCompileTime,
+    //        Eigen::internal::traits<RefType>::StrideType::
+    //            OuterStrideAtCompileTime>::type NumpyMapStride;
+
+    static const int Options = Eigen::internal::traits<TensorType>::Options;
+
+    bool need_to_allocate = false;
+    const int pyArray_type_code = EIGENPY_GET_PY_ARRAY_TYPE(pyArray);
+    const int Scalar_type_code = Register::getTypeCode<Scalar>();
+    if (pyArray_type_code != Scalar_type_code) need_to_allocate |= true;
+    //    bool incompatible_layout =
+    //        !is_arr_layout_compatible_with_mat_type<MatType>(pyArray);
+    //    need_to_allocate |= incompatible_layout;
+    //    if (Options !=
+    //        Eigen::Unaligned)  // we need to check whether the memory is
+    //        correctly
+    //                           // aligned and composed of a continuous segment
+    //    {
+    //      void *data_ptr = PyArray_DATA(pyArray);
+    //      if (!PyArray_ISONESEGMENT(pyArray) || !is_aligned(data_ptr,
+    //      Options))
+    //        need_to_allocate |= true;
+    //    }
+
+    void *raw_ptr = storage->storage.bytes;
+    if (need_to_allocate) {
+      typedef typename boost::remove_const<TensorType>::type TensorTypeNonConst;
+      TensorTypeNonConst *tensor_ptr;
+      tensor_ptr = details::init_tensor<TensorTypeNonConst>::run(pyArray);
+      RefType tensor_ref(*tensor_ptr);
+
+      new (raw_ptr) StorageType(tensor_ref, pyArray, tensor_ptr);
+
+      TensorTypeNonConst &tensor = *tensor_ptr;
+      EigenAllocator<TensorTypeNonConst>::copy(pyArray, tensor);
+    } else {
+      assert(pyArray_type_code == Scalar_type_code);
+      typename NumpyMap<TensorType, Scalar, Options>::EigenMap numpyMap =
+          NumpyMap<TensorType, Scalar, Options>::map(pyArray);
+      RefType tensor_ref(numpyMap);
+      new (raw_ptr) StorageType(tensor_ref, pyArray);
+    }
+  }
+
+  static void copy(RefType const &ref, PyArrayObject *pyArray) {
+    EigenAllocator<TensorType>::copy(ref, pyArray);
+  }
+};
+
+#endif
+
+template <typename EigenType>
+struct EigenAllocator : eigen_allocator_impl<EigenType> {};
+
+}  // namespace eigenpy
+
+#endif  // __eigenpy_eigen_allocator_hpp__

include/eigenpy/eigen-from-python.hpp

+//
+// Copyright (c) 2014-2023 CNRS INRIA
+//
+
+#ifndef __eigenpy_eigen_from_python_hpp__
+#define __eigenpy_eigen_from_python_hpp__
+
+#include "eigenpy/fwd.hpp"
+#include "eigenpy/eigen-allocator.hpp"
+#include "eigenpy/numpy-type.hpp"
+#include "eigenpy/scalar-conversion.hpp"
+
+namespace eigenpy {
+
+template <typename EigenType,
+          typename BaseType = typename get_eigen_base_type<EigenType>::type>
+struct expected_pytype_for_arg {};
+
+template <typename MatType>
+struct expected_pytype_for_arg<MatType, Eigen::MatrixBase<MatType> > {
+  static PyTypeObject const *get_pytype() {
+    PyTypeObject const *py_type = eigenpy::getPyArrayType();
+    return py_type;
+  }
+};
+
+}  // namespace eigenpy
+
+namespace boost {
+namespace python {
+namespace converter {
+
+template <typename Scalar, int Rows, int Cols, int Options, int MaxRows,
+          int MaxCols>
+struct expected_pytype_for_arg<
+    Eigen::Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
+    : eigenpy::expected_pytype_for_arg<
+          Eigen::Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > {};
+
+}  // namespace converter
+}  // namespace python
+}  // namespace boost
+
+namespace eigenpy {
+namespace details {
+template <typename MatType, bool is_const = boost::is_const<MatType>::value>
+struct copy_if_non_const {
+  static void run(const Eigen::MatrixBase<MatType> &input,
+                  PyArrayObject *pyArray) {
+    EigenAllocator<MatType>::copy(input, pyArray);
+  }
+};
+
+template <typename MatType>
+struct copy_if_non_const<const MatType, true> {
+  static void run(const Eigen::MatrixBase<MatType> & /*input*/,
+                  PyArrayObject * /*pyArray*/) {}
+};
+
+#if EIGEN_VERSION_AT_LEAST(3, 2, 0)
+
+template <typename _RefType>
+struct referent_storage_eigen_ref {
+  typedef _RefType RefType;
+  typedef typename get_eigen_plain_type<RefType>::type PlainObjectType;
+  typedef typename ::eigenpy::aligned_storage<
+      ::boost::python::detail::referent_size<RefType &>::value>::type
+      AlignedStorage;
+
+  referent_storage_eigen_ref()
+      : pyArray(NULL),
+        plain_ptr(NULL),
+        ref_ptr(reinterpret_cast<RefType *>(ref_storage.bytes)) {}
+
+  referent_storage_eigen_ref(const RefType &ref, PyArrayObject *pyArray,
+                             PlainObjectType *plain_ptr = NULL)
+      : pyArray(pyArray),
+        plain_ptr(plain_ptr),
+        ref_ptr(reinterpret_cast<RefType *>(ref_storage.bytes)) {
+    Py_INCREF(pyArray);
+    new (ref_storage.bytes) RefType(ref);
+  }
+
+  ~referent_storage_eigen_ref() {
+    if (plain_ptr != NULL && PyArray_ISWRITEABLE(pyArray))
+      copy_if_non_const<PlainObjectType>::run(*plain_ptr, pyArray);
+
+    Py_DECREF(pyArray);
+
+    if (plain_ptr != NULL) plain_ptr->~PlainObjectType();
+
+    ref_ptr->~RefType();
+  }
+
+  AlignedStorage ref_storage;
+  PyArrayObject *pyArray;
+  PlainObjectType *plain_ptr;
+  RefType *ref_ptr;
+};
+#endif
+
+}  // namespace details
+}  // namespace eigenpy
+
+namespace boost {
+namespace python {
+namespace detail {
+#if EIGEN_VERSION_AT_LEAST(3, 2, 0)
+template <typename MatType, int Options, typename Stride>
+struct referent_storage<Eigen::Ref<MatType, Options, Stride> &> {
+  typedef Eigen::Ref<MatType, Options, Stride> RefType;
+  typedef ::eigenpy::details::referent_storage_eigen_ref<RefType> StorageType;
+  typedef typename ::eigenpy::aligned_storage<
+      referent_size<StorageType &>::value>::type type;
+};
+
+template <typename MatType, int Options, typename Stride>
+struct referent_storage<const Eigen::Ref<const MatType, Options, Stride> &> {
+  typedef Eigen::Ref<const MatType, Options, Stride> RefType;
+  typedef ::eigenpy::details::referent_storage_eigen_ref<RefType> StorageType;
+  typedef typename ::eigenpy::aligned_storage<
+      referent_size<StorageType &>::value>::type type;
+};
+#endif
+}  // namespace detail
+}  // namespace python
+}  // namespace boost
+
+namespace boost {
+namespace python {
+namespace converter {
+
+#define EIGENPY_RVALUE_FROM_PYTHON_DATA_INIT(type)                       \
+  typedef ::eigenpy::rvalue_from_python_data<type> Base;                 \
+                                                                         \
+  rvalue_from_python_data(rvalue_from_python_stage1_data const &_stage1) \
+      : Base(_stage1) {}                                                 \
+                                                                         \
+  rvalue_from_python_data(void *convertible) : Base(convertible){};
+
+template <typename Scalar, int Rows, int Cols, int Options, int MaxRows,
+          int MaxCols>
+struct rvalue_from_python_data<
+    Eigen::Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> const &>
+    : ::eigenpy::rvalue_from_python_data<Eigen::Matrix<
+          Scalar, Rows, Cols, Options, MaxRows, MaxCols> const &> {
+  typedef Eigen::Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> T;
+  EIGENPY_RVALUE_FROM_PYTHON_DATA_INIT(T const &)
+};
+
+template <typename Derived>
+struct rvalue_from_python_data<Eigen::MatrixBase<Derived> const &>
+    : ::eigenpy::rvalue_from_python_data<Derived const &> {
+  EIGENPY_RVALUE_FROM_PYTHON_DATA_INIT(Derived const &)
+};
+
+template <typename Derived>
+struct rvalue_from_python_data<Eigen::EigenBase<Derived> const &>
+    : ::eigenpy::rvalue_from_python_data<Derived const &> {
+  EIGENPY_RVALUE_FROM_PYTHON_DATA_INIT(Derived const &)
+};
+
+template <typename Derived>
+struct rvalue_from_python_data<Eigen::PlainObjectBase<Derived> const &>
+    : ::eigenpy::rvalue_from_python_data<Derived const &> {
+  EIGENPY_RVALUE_FROM_PYTHON_DATA_INIT(Derived const &)
+};
+
+template <typename MatType, int Options, typename Stride>
+struct rvalue_from_python_data<Eigen::Ref<MatType, Options, Stride> &>
+    : rvalue_from_python_storage<Eigen::Ref<MatType, Options, Stride> &> {
+  typedef Eigen::Ref<MatType, Options, Stride> RefType;
+
+#if (!defined(__MWERKS__) || __MWERKS__ >= 0x3000) &&                        \
+    (!defined(__EDG_VERSION__) || __EDG_VERSION__ >= 245) &&                 \
+    (!defined(__DECCXX_VER) || __DECCXX_VER > 60590014) &&                   \
+    !defined(BOOST_PYTHON_SYNOPSIS) /* Synopsis' OpenCXX has trouble parsing \
+                                       this */
+  // This must always be a POD struct with m_data its first member.
+  BOOST_STATIC_ASSERT(BOOST_PYTHON_OFFSETOF(rvalue_from_python_storage<RefType>,
+                                            stage1) == 0);
+#endif
+
+  // The usual constructor
+  rvalue_from_python_data(rvalue_from_python_stage1_data const &_stage1) {
+    this->stage1 = _stage1;
+  }
+
+  // This constructor just sets m_convertible -- used by
+  // implicitly_convertible<> to perform the final step of the
+  // conversion, where the construct() function is already known.
+  rvalue_from_python_data(void *convertible) {
+    this->stage1.convertible = convertible;
+  }
+
+  // Destroys any object constructed in the storage.
+  ~rvalue_from_python_data() {
+    typedef ::eigenpy::details::referent_storage_eigen_ref<RefType> StorageType;
+    if (this->stage1.convertible == this->storage.bytes)
+      static_cast<StorageType *>((void *)this->storage.bytes)->~StorageType();
+  }
+};
+
+template <typename MatType, int Options, typename Stride>
+struct rvalue_from_python_data<
+    const Eigen::Ref<const MatType, Options, Stride> &>
+    : rvalue_from_python_storage<
+          const Eigen::Ref<const MatType, Options, Stride> &> {
+  typedef Eigen::Ref<const MatType, Options, Stride> RefType;
+
+#if (!defined(__MWERKS__) || __MWERKS__ >= 0x3000) &&                        \
+    (!defined(__EDG_VERSION__) || __EDG_VERSION__ >= 245) &&                 \
+    (!defined(__DECCXX_VER) || __DECCXX_VER > 60590014) &&                   \
+    !defined(BOOST_PYTHON_SYNOPSIS) /* Synopsis' OpenCXX has trouble parsing \
+                                       this */
+  // This must always be a POD struct with m_data its first member.
+  BOOST_STATIC_ASSERT(BOOST_PYTHON_OFFSETOF(rvalue_from_python_storage<RefType>,
+                                            stage1) == 0);
+#endif
+
+  // The usual constructor
+  rvalue_from_python_data(rvalue_from_python_stage1_data const &_stage1) {
+    this->stage1 = _stage1;
+  }
+
+  // This constructor just sets m_convertible -- used by
+  // implicitly_convertible<> to perform the final step of the
+  // conversion, where the construct() function is already known.
+  rvalue_from_python_data(void *convertible) {
+    this->stage1.convertible = convertible;
+  }
+
+  // Destroys any object constructed in the storage.
+  ~rvalue_from_python_data() {
+    typedef ::eigenpy::details::referent_storage_eigen_ref<RefType> StorageType;
+    if (this->stage1.convertible == this->storage.bytes)
+      static_cast<StorageType *>((void *)this->storage.bytes)->~StorageType();
+  }
+};
+
+}  // namespace converter
+}  // namespace python
+}  // namespace boost
+
+namespace eigenpy {
+
+template <typename MatOrRefType>
+void eigen_from_py_construct(
+    PyObject *pyObj, bp::converter::rvalue_from_python_stage1_data *memory) {
+  PyArrayObject *pyArray = reinterpret_cast<PyArrayObject *>(pyObj);
+  assert((PyArray_DIMS(pyArray)[0] < INT_MAX) &&
+         (PyArray_DIMS(pyArray)[1] < INT_MAX));
+
+  bp::converter::rvalue_from_python_storage<MatOrRefType> *storage =
+      reinterpret_cast<
+          bp::converter::rvalue_from_python_storage<MatOrRefType> *>(
+          reinterpret_cast<void *>(memory));
+
+  EigenAllocator<MatOrRefType>::allocate(pyArray, storage);
+
+  memory->convertible = storage->storage.bytes;
+}
+
+template <typename EigenType,
+          typename BaseType = typename get_eigen_base_type<EigenType>::type>
+struct eigen_from_py_impl {
+  typedef typename EigenType::Scalar Scalar;
+
+  /// \brief Determine if pyObj can be converted into a MatType object
+  static void *convertible(PyObject *pyObj);
+
+  /// \brief Allocate memory and copy pyObj in the new storage
+  static void construct(PyObject *pyObj,
+                        bp::converter::rvalue_from_python_stage1_data *memory);
+
+  static void registration();
+};
+
+template <typename MatType>
+struct eigen_from_py_impl<MatType, Eigen::MatrixBase<MatType> > {
+  typedef typename MatType::Scalar Scalar;
+
+  /// \brief Determine if pyObj can be converted into a MatType object
+  static void *convertible(PyObject *pyObj);
+
+  /// \brief Allocate memory and copy pyObj in the new storage
+  static void construct(PyObject *pyObj,
+                        bp::converter::rvalue_from_python_stage1_data *memory);
+
+  static void registration();
+};
+
+template <typename EigenType,
+          typename Scalar =
+              typename boost::remove_reference<EigenType>::type::Scalar>
+struct EigenFromPy : eigen_from_py_impl<EigenType> {};
+
+template <typename MatType>
+void *eigen_from_py_impl<MatType, Eigen::MatrixBase<MatType> >::convertible(
+    PyObject *pyObj) {
+  if (!call_PyArray_Check(reinterpret_cast<PyObject *>(pyObj))) return 0;
+
+  PyArrayObject *pyArray = reinterpret_cast<PyArrayObject *>(pyObj);
+
+  if (!np_type_is_convertible_into_scalar<Scalar>(
+          EIGENPY_GET_PY_ARRAY_TYPE(pyArray)))
+    return 0;
+
+  if (MatType::IsVectorAtCompileTime) {
+    const Eigen::DenseIndex size_at_compile_time =
+        MatType::IsRowMajor ? MatType::ColsAtCompileTime
+                            : MatType::RowsAtCompileTime;
+
+    switch (PyArray_NDIM(pyArray)) {
+      case 0:
+        return 0;
+      case 1: {
+        if (size_at_compile_time != Eigen::Dynamic) {
+          // check that the sizes at compile time matche
+          if (PyArray_DIMS(pyArray)[0] == size_at_compile_time)
+            return pyArray;
+          else
+            return 0;
+        } else  // This is a dynamic MatType
+          return pyArray;
+      }
+      case 2: {
+        // Special care of scalar matrix of dimension 1x1.
+        if (PyArray_DIMS(pyArray)[0] == 1 && PyArray_DIMS(pyArray)[1] == 1) {
+          if (size_at_compile_time != Eigen::Dynamic) {
+            if (size_at_compile_time == 1)
+              return pyArray;
+            else
+              return 0;
+          } else  // This is a dynamic MatType
+            return pyArray;
+        }
+
+        if (PyArray_DIMS(pyArray)[0] > 1 && PyArray_DIMS(pyArray)[1] > 1) {
+          return 0;
+        }
+
+        if (((PyArray_DIMS(pyArray)[0] == 1) &&
+             (MatType::ColsAtCompileTime == 1)) ||
+            ((PyArray_DIMS(pyArray)[1] == 1) &&
+             (MatType::RowsAtCompileTime == 1))) {
+          return 0;
+        }
+
+        if (size_at_compile_time !=
+            Eigen::Dynamic) {  // This is a fixe size vector
+          const Eigen::DenseIndex pyArray_size =
+              PyArray_DIMS(pyArray)[0] > PyArray_DIMS(pyArray)[1]
+                  ? PyArray_DIMS(pyArray)[0]
+                  : PyArray_DIMS(pyArray)[1];
+          if (size_at_compile_time != pyArray_size) return 0;
+        }
+        break;
+      }
+      default:
+        return 0;
+    }
+  } else  // this is a matrix
+  {
+    if (PyArray_NDIM(pyArray) ==
+        1)  // We can always convert a vector into a matrix
+    {
+      return pyArray;
+    }
+
+    if (PyArray_NDIM(pyArray) != 2) {
+      return 0;
+    }
+
+    if (PyArray_NDIM(pyArray) == 2) {
+      const int R = (int)PyArray_DIMS(pyArray)[0];
+      const int C = (int)PyArray_DIMS(pyArray)[1];
+
+      if ((MatType::RowsAtCompileTime != R) &&
+          (MatType::RowsAtCompileTime != Eigen::Dynamic))
+        return 0;
+      if ((MatType::ColsAtCompileTime != C) &&
+          (MatType::ColsAtCompileTime != Eigen::Dynamic))
+        return 0;
+    }
+  }
+
+#ifdef NPY_1_8_API_VERSION
+  if (!(PyArray_FLAGS(pyArray)))
+#else
+  if (!(PyArray_FLAGS(pyArray) & NPY_ALIGNED))
+#endif
+  {
+    return 0;
+  }
+
+  return pyArray;
+}
+
+template <typename MatType>
+void eigen_from_py_impl<MatType, Eigen::MatrixBase<MatType> >::construct(
+    PyObject *pyObj, bp::converter::rvalue_from_python_stage1_data *memory) {
+  eigen_from_py_construct<MatType>(pyObj, memory);
+}
+
+template <typename MatType>
+void eigen_from_py_impl<MatType, Eigen::MatrixBase<MatType> >::registration() {
+  bp::converter::registry::push_back(
+      reinterpret_cast<void *(*)(_object *)>(&eigen_from_py_impl::convertible),
+      &eigen_from_py_impl::construct, bp::type_id<MatType>()
+#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
+                                          ,
+      &eigenpy::expected_pytype_for_arg<MatType>::get_pytype
+#endif
+  );
+}
+
+template <typename EigenType,
+          typename BaseType = typename get_eigen_base_type<EigenType>::type>
+struct eigen_from_py_converter_impl;
+
+template <typename EigenType>
+struct EigenFromPyConverter : eigen_from_py_converter_impl<EigenType> {};
+
+template <typename MatType>
+struct eigen_from_py_converter_impl<MatType, Eigen::MatrixBase<MatType> > {
+  static void registration() {
+    EigenFromPy<MatType>::registration();
+
+    // Add conversion to Eigen::MatrixBase<MatType>
+    typedef Eigen::MatrixBase<MatType> MatrixBase;
+    EigenFromPy<MatrixBase>::registration();
+
+    // Add conversion to Eigen::EigenBase<MatType>
+    typedef Eigen::EigenBase<MatType> EigenBase;
+    EigenFromPy<EigenBase, typename MatType::Scalar>::registration();
+
+    // Add conversion to Eigen::PlainObjectBase<MatType>
+    typedef Eigen::PlainObjectBase<MatType> PlainObjectBase;
+    EigenFromPy<PlainObjectBase>::registration();
+
+#if EIGEN_VERSION_AT_LEAST(3, 2, 0)
+    // Add conversion to Eigen::Ref<MatType>
+    typedef Eigen::Ref<MatType> RefType;
+    EigenFromPy<RefType>::registration();
+
+    // Add conversion to Eigen::Ref<MatType>
+    typedef const Eigen::Ref<const MatType> ConstRefType;
+    EigenFromPy<ConstRefType>::registration();
+#endif
+  }
+};
+
+template <typename MatType>
+struct EigenFromPy<Eigen::MatrixBase<MatType> > : EigenFromPy<MatType> {
+  typedef EigenFromPy<MatType> EigenFromPyDerived;
+  typedef Eigen::MatrixBase<MatType> Base;
+
+  static void registration() {
+    bp::converter::registry::push_back(
+        reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
+        &EigenFromPy::construct, bp::type_id<Base>()
+#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
+                                     ,
+        &eigenpy::expected_pytype_for_arg<MatType>::get_pytype
+#endif
+    );
+  }
+};
+
+template <typename MatType>
+struct EigenFromPy<Eigen::EigenBase<MatType>, typename MatType::Scalar>
+    : EigenFromPy<MatType> {
+  typedef EigenFromPy<MatType> EigenFromPyDerived;
+  typedef Eigen::EigenBase<MatType> Base;
+
+  static void registration() {
+    bp::converter::registry::push_back(
+        reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
+        &EigenFromPy::construct, bp::type_id<Base>()
+#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
+                                     ,
+        &eigenpy::expected_pytype_for_arg<MatType>::get_pytype
+#endif
+    );
+  }
+};
+
+template <typename MatType>
+struct EigenFromPy<Eigen::PlainObjectBase<MatType> > : EigenFromPy<MatType> {
+  typedef EigenFromPy<MatType> EigenFromPyDerived;
+  typedef Eigen::PlainObjectBase<MatType> Base;
+
+  static void registration() {
+    bp::converter::registry::push_back(
+        reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
+        &EigenFromPy::construct, bp::type_id<Base>()
+#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
+                                     ,
+        &eigenpy::expected_pytype_for_arg<MatType>::get_pytype
+#endif
+    );
+  }
+};
+
+#if EIGEN_VERSION_AT_LEAST(3, 2, 0)
+
+template <typename MatType, int Options, typename Stride>
+struct EigenFromPy<Eigen::Ref<MatType, Options, Stride> > {
+  typedef Eigen::Ref<MatType, Options, Stride> RefType;
+  typedef typename MatType::Scalar Scalar;
+
+  /// \brief Determine if pyObj can be converted into a MatType object
+  static void *convertible(PyObject *pyObj) {
+    if (!call_PyArray_Check(pyObj)) return 0;
+    PyArrayObject *pyArray = reinterpret_cast<PyArrayObject *>(pyObj);
+    if (!PyArray_ISWRITEABLE(pyArray)) return 0;
+    return EigenFromPy<MatType>::convertible(pyObj);
+  }
+
+  static void registration() {
+    bp::converter::registry::push_back(
+        reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
+        &eigen_from_py_construct<RefType>, bp::type_id<RefType>()
+#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
+                                               ,
+        &eigenpy::expected_pytype_for_arg<MatType>::get_pytype
+#endif
+    );
+  }
+};
+
+template <typename MatType, int Options, typename Stride>
+struct EigenFromPy<const Eigen::Ref<const MatType, Options, Stride> > {
+  typedef const Eigen::Ref<const MatType, Options, Stride> ConstRefType;
+  typedef typename MatType::Scalar Scalar;
+
+  /// \brief Determine if pyObj can be converted into a MatType object
+  static void *convertible(PyObject *pyObj) {
+    return EigenFromPy<MatType>::convertible(pyObj);
+  }
+
+  static void registration() {
+    bp::converter::registry::push_back(
+        reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
+        &eigen_from_py_construct<ConstRefType>, bp::type_id<ConstRefType>()
+#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
+                                                    ,
+        &eigenpy::expected_pytype_for_arg<MatType>::get_pytype
+#endif
+    );
+  }
+};
+#endif
+
+}  // namespace eigenpy
+
+#ifdef EIGENPY_WITH_TENSOR_SUPPORT
+#include "eigenpy/tensor/eigen-from-python.hpp"
+#endif
+
+#include "eigenpy/sparse/eigen-from-python.hpp"
+
+#endif  // __eigenpy_eigen_from_python_hpp__

include/eigenpy/eigen-to-python.hpp

+//
+// Copyright (c) 2014-2024 CNRS INRIA
+//
+
+#ifndef __eigenpy_eigen_to_python_hpp__
+#define __eigenpy_eigen_to_python_hpp__
+
+#include <boost/type_traits.hpp>
+
+#include "eigenpy/fwd.hpp"
+
+#include "eigenpy/eigen-allocator.hpp"
+#include "eigenpy/numpy-allocator.hpp"
+#include "eigenpy/scipy-allocator.hpp"
+#include "eigenpy/numpy-type.hpp"
+#include "eigenpy/scipy-type.hpp"
+#include "eigenpy/registration.hpp"
+
+namespace eigenpy {
+
+EIGENPY_DOCUMENTATION_START_IGNORE
+
+template <typename EigenType,
+          typename BaseType = typename get_eigen_base_type<EigenType>::type>
+struct eigen_to_py_impl;
+
+template <typename MatType>
+struct eigen_to_py_impl_matrix;
+
+template <typename MatType>
+struct eigen_to_py_impl<MatType, Eigen::MatrixBase<MatType> >
+    : eigen_to_py_impl_matrix<MatType> {};
+
+template <typename MatType>
+struct eigen_to_py_impl<MatType&, Eigen::MatrixBase<MatType> >
+    : eigen_to_py_impl_matrix<MatType&> {};
+
+template <typename MatType>
+struct eigen_to_py_impl<const MatType, const Eigen::MatrixBase<MatType> >
+    : eigen_to_py_impl_matrix<const MatType> {};
+
+template <typename MatType>
+struct eigen_to_py_impl<const MatType&, const Eigen::MatrixBase<MatType> >
+    : eigen_to_py_impl_matrix<const MatType&> {};
+
+template <typename MatType>
+struct eigen_to_py_impl_matrix {
+  static PyObject* convert(
+      typename boost::add_reference<
+          typename boost::add_const<MatType>::type>::type mat) {
+    typedef typename boost::remove_const<
+        typename boost::remove_reference<MatType>::type>::type MatrixDerived;
+
+    assert((mat.rows() < INT_MAX) && (mat.cols() < INT_MAX) &&
+           "Matrix range larger than int ... should never happen.");
+    const npy_intp R = (npy_intp)mat.rows(), C = (npy_intp)mat.cols();
+
+    PyArrayObject* pyArray;
+    // Allocate Python memory
+    if ((((!(C == 1) != !(R == 1)) && !MatrixDerived::IsVectorAtCompileTime) ||
+         MatrixDerived::IsVectorAtCompileTime))  // Handle array with a single
+                                                 // dimension
+    {
+      npy_intp shape[1] = {C == 1 ? R : C};
+      pyArray = NumpyAllocator<MatType>::allocate(
+          const_cast<MatrixDerived&>(mat.derived()), 1, shape);
+    } else {
+      npy_intp shape[2] = {R, C};
+      pyArray = NumpyAllocator<MatType>::allocate(
+          const_cast<MatrixDerived&>(mat.derived()), 2, shape);
+    }
+
+    // Create an instance (either np.array or np.matrix)
+    return NumpyType::make(pyArray).ptr();
+  }
+
+  static PyTypeObject const* get_pytype() { return getPyArrayType(); }
+};
+
+template <typename MatType>
+struct eigen_to_py_impl_sparse_matrix;
+
+template <typename MatType>
+struct eigen_to_py_impl<MatType, Eigen::SparseMatrixBase<MatType> >
+    : eigen_to_py_impl_sparse_matrix<MatType> {};
+
+template <typename MatType>
+struct eigen_to_py_impl<MatType&, Eigen::SparseMatrixBase<MatType> >
+    : eigen_to_py_impl_sparse_matrix<MatType&> {};
+
+template <typename MatType>
+struct eigen_to_py_impl<const MatType, const Eigen::SparseMatrixBase<MatType> >
+    : eigen_to_py_impl_sparse_matrix<const MatType> {};
+
+template <typename MatType>
+struct eigen_to_py_impl<const MatType&, const Eigen::SparseMatrixBase<MatType> >
+    : eigen_to_py_impl_sparse_matrix<const MatType&> {};
+
+template <typename MatType>
+struct eigen_to_py_impl_sparse_matrix {
+  enum { IsRowMajor = MatType::IsRowMajor };
+
+  static PyObject* convert(
+      typename boost::add_reference<
+          typename boost::add_const<MatType>::type>::type mat) {
+    typedef typename boost::remove_const<
+        typename boost::remove_reference<MatType>::type>::type MatrixDerived;
+
+    // Allocate and perform the copy
+    PyObject* pyArray =
+        ScipyAllocator<MatType>::allocate(const_cast<MatrixDerived&>(mat));
+
+    return pyArray;
+  }
+
+  static PyTypeObject const* get_pytype() {
+    return IsRowMajor ? ScipyType::getScipyCSRMatrixType()
+                      : ScipyType::getScipyCSCMatrixType();
+  }
+};
+
+#ifdef EIGENPY_WITH_TENSOR_SUPPORT
+template <typename TensorType>
+struct eigen_to_py_impl_tensor;
+
+template <typename TensorType>
+struct eigen_to_py_impl<TensorType, Eigen::TensorBase<TensorType> >
+    : eigen_to_py_impl_tensor<TensorType> {};
+
+template <typename TensorType>
+struct eigen_to_py_impl<const TensorType, const Eigen::TensorBase<TensorType> >
+    : eigen_to_py_impl_tensor<const TensorType> {};
+
+template <typename TensorType>
+struct eigen_to_py_impl_tensor {
+  static PyObject* convert(
+      typename boost::add_reference<
+          typename boost::add_const<TensorType>::type>::type tensor) {
+    //    typedef typename boost::remove_const<
+    //        typename boost::remove_reference<Tensor>::type>::type
+    //        TensorDerived;
+
+    static const int NumIndices = TensorType::NumIndices;
+    npy_intp shape[NumIndices];
+    for (int k = 0; k < NumIndices; ++k) shape[k] = tensor.dimension(k);
+
+    PyArrayObject* pyArray = NumpyAllocator<TensorType>::allocate(
+        const_cast<TensorType&>(tensor), NumIndices, shape);
+
+    // Create an instance (either np.array or np.matrix)
+    return NumpyType::make(pyArray).ptr();
+  }
+
+  static PyTypeObject const* get_pytype() { return getPyArrayType(); }
+};
+#endif
+
+EIGENPY_DOCUMENTATION_END_IGNORE
+
+template <typename EigenType,
+          typename Scalar =
+              typename boost::remove_reference<EigenType>::type::Scalar>
+struct EigenToPy : eigen_to_py_impl<EigenType> {};
+
+template <typename MatType>
+struct EigenToPyConverter {
+  static void registration() {
+    bp::to_python_converter<MatType, EigenToPy<MatType>, true>();
+  }
+};
+
+}  // namespace eigenpy
+
+namespace boost {
+namespace python {
+
+template <typename MatrixRef, class MakeHolder>
+struct to_python_indirect_eigen {
+  template <class U>
+  inline PyObject* operator()(U const& mat) const {
+    return eigenpy::EigenToPy<MatrixRef>::convert(const_cast<U&>(mat));
+  }
+
+#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
+  inline PyTypeObject const* get_pytype() const {
+    return converter::registered_pytype<MatrixRef>::get_pytype();
+  }
+#endif
+};
+
+template <typename Scalar, int RowsAtCompileTime, int ColsAtCompileTime,
+          int Options, int MaxRowsAtCompileTime, int MaxColsAtCompileTime,
+          class MakeHolder>
+struct to_python_indirect<
+    Eigen::Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, Options,
+                  MaxRowsAtCompileTime, MaxColsAtCompileTime>&,
+    MakeHolder>
+    : to_python_indirect_eigen<
+          Eigen::Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, Options,
+                        MaxRowsAtCompileTime, MaxColsAtCompileTime>&,
+          MakeHolder> {};
+
+template <typename Scalar, int RowsAtCompileTime, int ColsAtCompileTime,
+          int Options, int MaxRowsAtCompileTime, int MaxColsAtCompileTime,
+          class MakeHolder>
+struct to_python_indirect<
+    const Eigen::Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, Options,
+                        MaxRowsAtCompileTime, MaxColsAtCompileTime>&,
+    MakeHolder>
+    : to_python_indirect_eigen<
+          const Eigen::Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime,
+                              Options, MaxRowsAtCompileTime,
+                              MaxColsAtCompileTime>&,
+          MakeHolder> {};
+
+}  // namespace python
+}  // namespace boost
+
+#endif  // __eigenpy_eigen_to_python_hpp__

include/eigenpy/eigen-typedef.hpp

+//
+// Copyright (c) 2020-2023 INRIA
+//
+
+#ifndef __eigenpy_eigen_typedef_hpp__
+#define __eigenpy_eigen_typedef_hpp__
+
+#include "eigenpy/fwd.hpp"
+
+#define EIGENPY_MAKE_TYPEDEFS(Type, Options, TypeSuffix, Size, SizeSuffix) \
+  /** \ingroup matrixtypedefs */                                           \
+  typedef Eigen::Matrix<Type, Size, Size, Options>                         \
+      Matrix##SizeSuffix##TypeSuffix;                                      \
+  /** \ingroup matrixtypedefs */                                           \
+  typedef Eigen::Matrix<Type, Size, 1> Vector##SizeSuffix##TypeSuffix;     \
+  /** \ingroup matrixtypedefs */                                           \
+  typedef Eigen::Matrix<Type, 1, Size> RowVector##SizeSuffix##TypeSuffix;
+
+#define EIGENPY_MAKE_FIXED_TYPEDEFS(Type, Options, TypeSuffix, Size) \
+  /** \ingroup matrixtypedefs */                                     \
+  typedef Eigen::Matrix<Type, Size, Eigen::Dynamic, Options>         \
+      Matrix##Size##X##TypeSuffix;                                   \
+  /** \ingroup matrixtypedefs */                                     \
+  typedef Eigen::Matrix<Type, Eigen::Dynamic, Size, Options>         \
+      Matrix##X##Size##TypeSuffix;
+
+#define EIGENPY_MAKE_TYPEDEFS_ALL_SIZES(Type, Options, TypeSuffix)    \
+  EIGENPY_MAKE_TYPEDEFS(Type, Options, TypeSuffix, 2, 2)              \
+  EIGENPY_MAKE_TYPEDEFS(Type, Options, TypeSuffix, 3, 3)              \
+  EIGENPY_MAKE_TYPEDEFS(Type, Options, TypeSuffix, 4, 4)              \
+  EIGENPY_MAKE_TYPEDEFS(Type, Options, TypeSuffix, Eigen::Dynamic, X) \
+  EIGENPY_MAKE_FIXED_TYPEDEFS(Type, Options, TypeSuffix, 2)           \
+  EIGENPY_MAKE_FIXED_TYPEDEFS(Type, Options, TypeSuffix, 3)           \
+  EIGENPY_MAKE_FIXED_TYPEDEFS(Type, Options, TypeSuffix, 4)           \
+  EIGENPY_MAKE_TYPEDEFS(Type, Options, TypeSuffix, 1, 1)              \
+  typedef Eigen::SparseMatrix<Scalar, Options> SparseMatrixX##TypeSuffix
+
+#endif  // ifndef __eigenpy_eigen_typedef_hpp__

include/eigenpy/eigen/EigenBase.hpp

+/*
+ * Copyright 2024 INRIA
+ */
+
+#ifndef __eigenpy_eigen_eigen_base_hpp__
+#define __eigenpy_eigen_eigen_base_hpp__
+
+#include "eigenpy/eigenpy.hpp"
+
+namespace eigenpy {
+
+template <typename Derived>
+struct EigenBaseVisitor
+    : public boost::python::def_visitor<EigenBaseVisitor<Derived> > {
+  template <class PyClass>
+  void visit(PyClass &cl) const {
+    cl.def("cols", &Derived::cols, bp::arg("self"),
+           "Returns the number of columns.")
+        .def("rows", &Derived::rows, bp::arg("self"),
+             "Returns the number of rows.")
+        .def("size", &Derived::rows, bp::arg("self"),
+             "Returns the number of coefficients, which is rows()*cols().");
+  }
+};
+
+}  // namespace eigenpy
+
+#endif  // ifndef __eigenpy_eigen_eigen_base_hpp__

include/eigenpy/eigenpy.hpp

 /*
  * Copyright 2014-2019, CNRS
- * Copyright 2018-2019, INRIA
+ * Copyright 2018-2024, INRIA
  */
 
 #ifndef __eigenpy_eigenpy_hpp__
 #define __eigenpy_eigenpy_hpp__
 
 #include "eigenpy/fwd.hpp"
-#include "eigenpy/deprecated.hh"
-#include "eigenpy/eigenpy_export.h"
+#include "eigenpy/eigen-typedef.hpp"
+#include "eigenpy/expose.hpp"
 
-#if EIGEN_VERSION_AT_LEAST(3,2,0)
-#include "eigenpy/ref.hpp"
+/// Custom CallPolicies
+#include "eigenpy/std-unique-ptr.hpp"
 
 #define ENABLE_SPECIFIC_MATRIX_TYPE(TYPE) \
-  ::eigenpy::enableEigenPySpecific<TYPE>(); \
-  ::eigenpy::enableEigenPySpecific< eigenpy::Ref<TYPE> >();
+  ::eigenpy::enableEigenPySpecific<TYPE>();
 
-#else // if EIGEN_VERSION_AT_LEAST(3,2,0)
+namespace eigenpy {
 
-#define ENABLE_SPECIFIC_MATRIX_TYPE(TYPE) \
-  ::eigenpy::enableEigenPySpecific<TYPE>();
+/* Enable Eigen-Numpy serialization for a set of standard MatrixBase instance.
+ */
+void EIGENPY_DLLAPI enableEigenPy();
 
-#endif // if EIGEN_VERSION_AT_LEAST(3,2,0)
+bool EIGENPY_DLLAPI withTensorSupport();
 
-namespace eigenpy
-{
-  /* Enable Eigen-Numpy serialization for a set of standard MatrixBase instance. */
-  void EIGENPY_EXPORT enableEigenPy();
+/* Enable the Eigen--Numpy serialization for the templated MatType class.*/
+template <typename MatType>
+void enableEigenPySpecific();
 
-  template<typename MatType>
-  void enableEigenPySpecific();
-  
-  /* Enable the Eigen--Numpy serialization for the templated MatrixBase class.
-   * The second template argument is used for inheritance of Eigen classes. If
-   * using a native Eigen::MatrixBase, simply repeat the same arg twice. */
-  template<typename MatType,typename EigenEquivalentType>
-  EIGENPY_DEPRECATED void enableEigenPySpecific();
+template <typename Scalar, int Options>
+EIGEN_DONT_INLINE void exposeType() {
+  EIGENPY_MAKE_TYPEDEFS_ALL_SIZES(Scalar, Options, s);
 
+  EIGENPY_UNUSED_TYPE(Vector1s);
+  EIGENPY_UNUSED_TYPE(RowVector1s);
+  ENABLE_SPECIFIC_MATRIX_TYPE(Matrix1s);
 
-} // namespace eigenpy
+  ENABLE_SPECIFIC_MATRIX_TYPE(Vector2s);
+  ENABLE_SPECIFIC_MATRIX_TYPE(RowVector2s);
+  ENABLE_SPECIFIC_MATRIX_TYPE(Matrix2s);
+  ENABLE_SPECIFIC_MATRIX_TYPE(Matrix2Xs);
+  ENABLE_SPECIFIC_MATRIX_TYPE(MatrixX2s);
 
-#include "eigenpy/details.hpp"
+  ENABLE_SPECIFIC_MATRIX_TYPE(Vector3s);
+  ENABLE_SPECIFIC_MATRIX_TYPE(RowVector3s);
+  ENABLE_SPECIFIC_MATRIX_TYPE(Matrix3s);
+  ENABLE_SPECIFIC_MATRIX_TYPE(Matrix3Xs);
+  ENABLE_SPECIFIC_MATRIX_TYPE(MatrixX3s);
+
+  ENABLE_SPECIFIC_MATRIX_TYPE(Vector4s);
+  ENABLE_SPECIFIC_MATRIX_TYPE(RowVector4s);
+  ENABLE_SPECIFIC_MATRIX_TYPE(Matrix4s);
+  ENABLE_SPECIFIC_MATRIX_TYPE(Matrix4Xs);
+  ENABLE_SPECIFIC_MATRIX_TYPE(MatrixX4s);
 
-#endif // ifndef __eigenpy_eigenpy_hpp__
+  ENABLE_SPECIFIC_MATRIX_TYPE(VectorXs);
+  ENABLE_SPECIFIC_MATRIX_TYPE(RowVectorXs);
+  ENABLE_SPECIFIC_MATRIX_TYPE(MatrixXs);
+
+  enableEigenPySpecific<SparseMatrixXs>();
+}
+
+template <typename Scalar>
+EIGEN_DONT_INLINE void exposeType() {
+  exposeType<Scalar, 0>();
+
+#ifdef EIGENPY_WITH_TENSOR_SUPPORT
+  enableEigenPySpecific<Eigen::Tensor<Scalar, 1> >();
+  enableEigenPySpecific<Eigen::Tensor<Scalar, 2> >();
+  enableEigenPySpecific<Eigen::Tensor<Scalar, 3> >();
+#endif
+}
+
+}  // namespace eigenpy
+
+#include "eigenpy/details.hpp"
 
+#endif  // ifndef __eigenpy_eigenpy_hpp__

include/eigenpy/exception.hpp

@@ -3,41 +3,40 @@
  * Copyright 2018-2019, INRIA
  */
 
-#include <boost/python.hpp>
+#ifndef __eigenpy_exception_hpp__
+#define __eigenpy_exception_hpp__
+
 #include <exception>
 #include <string>
 
-#ifndef __eigenpy_exception_hpp__
-#define __eigenpy_exception_hpp__
+#include "eigenpy/fwd.hpp"
+
+namespace eigenpy {
+/*
+ * Eigenpy exception. They can be catch with python (equivalent
+ * eigenpy.exception class).
+ */
+class Exception : public std::exception {
+ public:
+  Exception() : message() {}
+  Exception(const std::string &msg) : message(msg) {}
+  const char *what() const throw() { return this->getMessage().c_str(); }
+  ~Exception() throw() {}
+  virtual const std::string &getMessage() const { return message; }
+  std::string copyMessage() const { return getMessage(); }
+
+  /* Call this static function to "enable" the translation of this C++ exception
+   * in Python. */
+  static void registerException();
+
+ private:
+  static void translateException(Exception const &e);
+  static PyObject *pyType;
+
+ protected:
+  std::string message;
+};
+
+}  // namespace eigenpy
 
-namespace eigenpy
-{
-  /*
-   * Eigenpy exception. They can be catch with python (equivalent eigenpy.exception class).
-   */
-  class Exception : public std::exception
-  {
-  public:
-    Exception() : message() {}
-    Exception(const std::string & msg) : message(msg) {}
-    const char *what() const throw()
-    {
-      return this->getMessage().c_str();
-    }
-    ~Exception() throw() {}
-    virtual const std::string & getMessage() const { return message; }
-    std::string copyMessage() const { return getMessage(); }
-
-    /* Call this static function to "enable" the translation of this C++ exception in Python. */
-    static void registerException();
-
-  private:
-    static void translateException( Exception const & e );
-    static PyObject * pyType;
-  protected:
-    std::string message;
-   };
-
-} // namespace eigenpy
-
-#endif // ifndef __eigenpy_exception_hpp__
+#endif  // ifndef __eigenpy_exception_hpp__