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 2018-2020, INRIA
+ * Copyright 2018-2024, INRIA
  */
 
 #ifndef __eigenpy_details_hpp__
 #define __eigenpy_details_hpp__
 
+#include "eigenpy/fwd.hpp"
 #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/fwd.hpp"
 #include "eigenpy/numpy-type.hpp"
 #include "eigenpy/registration.hpp"
 #include "eigenpy/scalar-conversion.hpp"
 
-namespace boost {
-namespace python {
-namespace detail {
+namespace eigenpy {
 
-template <class MatType>
-struct referent_size<Eigen::MatrixBase<MatType>&> {
-  BOOST_STATIC_CONSTANT(std::size_t, value = sizeof(MatType));
-};
+template <typename EigenType,
+          typename BaseType = typename get_eigen_base_type<EigenType>::type,
+          typename Scalar = typename EigenType::Scalar>
+struct expose_eigen_type_impl;
 
-template <class MatType>
-struct referent_size<Eigen::MatrixBase<MatType> > {
-  BOOST_STATIC_CONSTANT(std::size_t, value = sizeof(MatType));
-};
+template <typename MatType, typename Scalar>
+struct expose_eigen_type_impl<MatType, Eigen::MatrixBase<MatType>, Scalar> {
+  static void run() {
+    if (check_registration<MatType>()) return;
 
-template <class MatType>
-struct referent_size<Eigen::EigenBase<MatType>&> {
-  BOOST_STATIC_CONSTANT(std::size_t, value = sizeof(MatType));
-};
+    // 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
 
-template <class MatType>
-struct referent_size<Eigen::EigenBase<MatType> > {
-  BOOST_STATIC_CONSTANT(std::size_t, value = sizeof(MatType));
+    // from-python
+    EigenFromPyConverter<MatType>::registration();
+  }
 };
 
-template <class MatType>
-struct referent_size<Eigen::PlainObjectBase<MatType>&> {
-  BOOST_STATIC_CONSTANT(std::size_t, value = sizeof(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();
+  }
 };
 
-template <class MatType>
-struct referent_size<Eigen::PlainObjectBase<MatType> > {
-  BOOST_STATIC_CONSTANT(std::size_t, value = sizeof(MatType));
+#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();
+  }
 };
-
-}  // namespace detail
-}  // namespace python
-}  // namespace boost
-
-namespace eigenpy {
-template <typename MatType, typename EigenEquivalentType>
-EIGENPY_DEPRECATED void enableEigenPySpecific() {
-  enableEigenPySpecific<MatType>();
-}
+#endif
 
 template <typename MatType>
 void enableEigenPySpecific() {
-  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();
+  expose_eigen_type_impl<MatType>::run();
 }
 
 }  // namespace eigenpy

include/eigenpy/eigen-typedef.hpp

 //
-// Copyright (c) 2020 INRIA
+// Copyright (c) 2020-2023 INRIA
 //
 
 #ifndef __eigenpy_eigen_typedef_hpp__
@@ -31,6 +31,8 @@
   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_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 2018-2020, INRIA
+ * Copyright 2018-2024, INRIA
  */
 
 #ifndef __eigenpy_eigenpy_hpp__
 #define __eigenpy_eigenpy_hpp__
 
-#include "eigenpy/deprecated.hpp"
-#include "eigenpy/eigen-typedef.hpp"
 #include "eigenpy/fwd.hpp"
+#include "eigenpy/eigen-typedef.hpp"
+#include "eigenpy/expose.hpp"
+
+/// Custom CallPolicies
+#include "eigenpy/std-unique-ptr.hpp"
 
 #define ENABLE_SPECIFIC_MATRIX_TYPE(TYPE) \
   ::eigenpy::enableEigenPySpecific<TYPE>();
@@ -19,22 +22,20 @@ namespace eigenpy {
  */
 void EIGENPY_DLLAPI enableEigenPy();
 
-/* 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. */
+bool EIGENPY_DLLAPI withTensorSupport();
+
+/* Enable the Eigen--Numpy serialization for the templated MatType class.*/
 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);
+
   ENABLE_SPECIFIC_MATRIX_TYPE(Vector2s);
   ENABLE_SPECIFIC_MATRIX_TYPE(RowVector2s);
   ENABLE_SPECIFIC_MATRIX_TYPE(Matrix2s);
@@ -56,11 +57,19 @@ EIGEN_DONT_INLINE void exposeType() {
   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/geometry-conversion.hpp

 /*
  * Copyright 2014-2019, CNRS
- * Copyright 2018-2021, INRIA
+ * Copyright 2018-2023, INRIA
  */
 
 #ifndef __eigenpy_geometry_conversion_hpp__
 #define __eigenpy_geometry_conversion_hpp__
 
-#include <Eigen/Geometry>
-
 #include "eigenpy/fwd.hpp"
 
 namespace eigenpy {
 
-namespace bp = boost::python;
-
 template <typename Scalar, int Options = 0>
 struct EulerAnglesConvertor {
   typedef typename Eigen::Matrix<Scalar, 3, 1, Options> Vector3;