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 2018-2023, INRIA
+ * Copyright 2018-2024, INRIA
  */
 
 #ifndef __eigenpy_details_hpp__
@@ -19,11 +19,12 @@
 namespace eigenpy {
 
 template <typename EigenType,
-          typename BaseType = typename get_eigen_base_type<EigenType>::type>
+          typename BaseType = typename get_eigen_base_type<EigenType>::type,
+          typename Scalar = typename EigenType::Scalar>
 struct expose_eigen_type_impl;
 
-template <typename MatType>
-struct expose_eigen_type_impl<MatType, Eigen::MatrixBase<MatType> > {
+template <typename MatType, typename Scalar>
+struct expose_eigen_type_impl<MatType, Eigen::MatrixBase<MatType>, Scalar> {
   static void run() {
     if (check_registration<MatType>()) return;
 
@@ -39,9 +40,28 @@ struct expose_eigen_type_impl<MatType, Eigen::MatrixBase<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>
-struct expose_eigen_type_impl<TensorType, Eigen::TensorBase<TensorType> > {
+template <typename TensorType, typename Scalar>
+struct expose_eigen_type_impl<TensorType, Eigen::TensorBase<TensorType>,
+                              Scalar> {
   static void run() {
     if (check_registration<TensorType>()) return;
 

include/eigenpy/eigen-from-python.hpp

@@ -62,7 +62,7 @@ struct copy_if_non_const<const MatType, true> {
 template <typename _RefType>
 struct referent_storage_eigen_ref {
   typedef _RefType RefType;
-  typedef typename get_eigen_ref_plain_type<RefType>::type PlainObjectType;
+  typedef typename get_eigen_plain_type<RefType>::type PlainObjectType;
   typedef typename ::eigenpy::aligned_storage<
       ::boost::python::detail::referent_size<RefType &>::value>::type
       AlignedStorage;
@@ -290,7 +290,9 @@ struct eigen_from_py_impl<MatType, Eigen::MatrixBase<MatType> > {
   static void registration();
 };
 
-template <typename EigenType, typename _Scalar>
+template <typename EigenType,
+          typename Scalar =
+              typename boost::remove_reference<EigenType>::type::Scalar>
 struct EigenFromPy : eigen_from_py_impl<EigenType> {};
 
 template <typename MatType>
@@ -557,4 +559,6 @@ struct EigenFromPy<const Eigen::Ref<const MatType, Options, Stride> > {
 #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) 2014-2023 CNRS INRIA
+// Copyright (c) 2014-2024 CNRS INRIA
 //
 
 #ifndef __eigenpy_eigen_to_python_hpp__
@@ -11,54 +11,11 @@
 
 #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 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
-
 namespace eigenpy {
 
 EIGENPY_DOCUMENTATION_START_IGNORE
@@ -101,9 +58,8 @@ struct eigen_to_py_impl_matrix {
     PyArrayObject* pyArray;
     // Allocate Python memory
     if ((((!(C == 1) != !(R == 1)) && !MatrixDerived::IsVectorAtCompileTime) ||
-         MatrixDerived::IsVectorAtCompileTime) &&
-        NumpyType::getType() ==
-            ARRAY_TYPE)  // Handle array with a single dimension
+         MatrixDerived::IsVectorAtCompileTime))  // Handle array with a single
+                                                 // dimension
     {
       npy_intp shape[1] = {C == 1 ? R : C};
       pyArray = NumpyAllocator<MatType>::allocate(
@@ -117,6 +73,50 @@ struct eigen_to_py_impl_matrix {
     // 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
@@ -150,15 +150,17 @@ struct eigen_to_py_impl_tensor {
     // 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>
-struct EigenToPy : eigen_to_py_impl<EigenType> {
-  static PyTypeObject const* get_pytype() { return getPyArrayType(); }
-};
+template <typename EigenType,
+          typename Scalar =
+              typename boost::remove_reference<EigenType>::type::Scalar>
+struct EigenToPy : eigen_to_py_impl<EigenType> {};
 
 template <typename MatType>
 struct EigenToPyConverter {
@@ -166,6 +168,52 @@ struct EigenToPyConverter {
     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

@@ -32,6 +32,7 @@
   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)
+  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 2018-2023, INRIA
+ * Copyright 2018-2024, INRIA
  */
 
 #ifndef __eigenpy_eigenpy_hpp__
@@ -10,6 +10,9 @@
 #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>();
 
@@ -19,6 +22,8 @@ namespace eigenpy {
  */
 void EIGENPY_DLLAPI enableEigenPy();
 
+bool EIGENPY_DLLAPI withTensorSupport();
+
 /* Enable the Eigen--Numpy serialization for the templated MatType class.*/
 template <typename MatType>
 void enableEigenPySpecific();
@@ -52,15 +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/fwd.hpp

 /*
- * Copyright 2014-2023 CNRS INRIA
+ * Copyright 2014-2024 CNRS INRIA
  */
 
 #ifndef __eigenpy_fwd_hpp__
@@ -43,9 +43,8 @@
   EIGENPY_PRAGMA_WARNING(Deprecated : the_message)
 #define EIGENPY_PRAGMA_DEPRECATED_HEADER(old_header, new_header) \
   EIGENPY_PRAGMA_WARNING(                                        \
-      Deprecated header file                                     \
-      : #old_header has been replaced                            \
-            by #new_header.\n Please use #new_header instead of #old_header.)
+      Deprecated header file : #old_header has been replaced     \
+          by #new_header.\n Please use #new_header instead of #old_header.)
 #elif defined(WIN32)
 #define EIGENPY_PRAGMA(x) __pragma(#x)
 #define EIGENPY_PRAGMA_MESSAGE(the_message) \
@@ -65,6 +64,7 @@
 #define EIGENPY_DOCUMENTATION_END_IGNORE    /// \endcond
 
 #include "eigenpy/config.hpp"
+#include <boost/type_traits/is_base_of.hpp>
 
 // Silence a warning about a deprecated use of boost bind by boost python
 // at least fo boost 1.73 to 1.75
@@ -73,6 +73,9 @@
 #include <boost/python.hpp>
 #include <boost/python/scope.hpp>
 
+#include <type_traits>
+#include <utility>
+
 namespace eigenpy {
 
 namespace bp = boost::python;
@@ -86,6 +89,7 @@ namespace bp = boost::python;
 #undef BOOST_BIND_GLOBAL_PLACEHOLDERS
 
 #include <Eigen/Core>
+#include <Eigen/Sparse>
 #include <Eigen/Geometry>
 
 #ifdef EIGENPY_WITH_CXX11_SUPPORT
@@ -104,7 +108,13 @@ namespace bp = boost::python;
 #define EIGENPY_NO_ALIGNMENT_VALUE Eigen::Unaligned
 
 #define EIGENPY_UNUSED_VARIABLE(var) (void)(var)
-#define EIGENPY_UNUSED_TYPE(type) (type *)(NULL)
+#define EIGENPY_UNUSED_TYPE(type) EIGENPY_UNUSED_VARIABLE((type *)(NULL))
+#ifndef NDEBUG
+#define EIGENPY_USED_VARIABLE_ONLY_IN_DEBUG_MODE(var)
+#else
+#define EIGENPY_USED_VARIABLE_ONLY_IN_DEBUG_MODE(var) \
+  EIGENPY_UNUSED_VARIABLE(var)
+#endif
 
 #ifdef EIGENPY_WITH_CXX11_SUPPORT
 #include <memory>
@@ -115,13 +125,13 @@ namespace bp = boost::python;
 #endif
 
 namespace eigenpy {
-template <typename MatType,
-          typename Scalar =
-              typename boost::remove_reference<MatType>::type::Scalar>
+
+// Default Scalar value can't be defined in the declaration
+// because of a CL bug.
+// See https://github.com/stack-of-tasks/eigenpy/pull/462
+template <typename MatType, typename Scalar>
 struct EigenToPy;
-template <typename MatType,
-          typename Scalar =
-              typename boost::remove_reference<MatType>::type::Scalar>
+template <typename MatType, typename Scalar>
 struct EigenFromPy;
 
 template <typename T>
@@ -135,17 +145,20 @@ struct get_eigen_base_type {
   typedef typename remove_const_reference<EigenType>::type EigenType_;
   typedef typename boost::mpl::if_<
       boost::is_base_of<Eigen::MatrixBase<EigenType_>, EigenType_>,
-      Eigen::MatrixBase<EigenType_>
-#ifdef EIGENPY_WITH_TENSOR_SUPPORT
-      ,
+      Eigen::MatrixBase<EigenType_>,
       typename boost::mpl::if_<
-          boost::is_base_of<Eigen::TensorBase<EigenType_>, EigenType_>,
-          Eigen::TensorBase<EigenType_>, void>::type
+          boost::is_base_of<Eigen::SparseMatrixBase<EigenType_>, EigenType_>,
+          Eigen::SparseMatrixBase<EigenType_>
+#ifdef EIGENPY_WITH_TENSOR_SUPPORT
+          ,
+          typename boost::mpl::if_<
+              boost::is_base_of<Eigen::TensorBase<EigenType_>, EigenType_>,
+              Eigen::TensorBase<EigenType_>, void>::type
 #else
-      ,
-      void
+          ,
+          void
 #endif
-      >::type _type;
+          >::type>::type _type;
 
   typedef typename boost::mpl::if_<
       boost::is_const<typename boost::remove_reference<EigenType>::type>,
@@ -153,22 +166,39 @@ struct get_eigen_base_type {
 };
 
 template <typename EigenType>
-struct get_eigen_ref_plain_type;
+struct get_eigen_plain_type;
 
 template <typename MatType, int Options, typename Stride>
-struct get_eigen_ref_plain_type<Eigen::Ref<MatType, Options, Stride> > {
+struct get_eigen_plain_type<Eigen::Ref<MatType, Options, Stride> > {
   typedef typename Eigen::internal::traits<
       Eigen::Ref<MatType, Options, Stride> >::PlainObjectType type;
 };
 
 #ifdef EIGENPY_WITH_TENSOR_SUPPORT
 template <typename TensorType>
-struct get_eigen_ref_plain_type<Eigen::TensorRef<TensorType> > {
+struct get_eigen_plain_type<Eigen::TensorRef<TensorType> > {
   typedef TensorType type;
 };
 #endif
+
+namespace internal {
+template <class T1, class T2>
+struct has_operator_equal_impl {
+  template <class U, class V>
+  static auto check(U *) -> decltype(std::declval<U>() == std::declval<V>());
+  template <typename, typename>
+  static auto check(...) -> std::false_type;
+
+  using type = typename std::is_same<bool, decltype(check<T1, T2>(0))>::type;
+};
+}  // namespace internal
+
+template <class T1, class T2 = T1>
+struct has_operator_equal : internal::has_operator_equal_impl<T1, T2>::type {};
+
 }  // namespace eigenpy
 
 #include "eigenpy/alignment.hpp"
+#include "eigenpy/id.hpp"
 
 #endif  // ifndef __eigenpy_fwd_hpp__

include/eigenpy/id.hpp

+//
+// Copyright (c) 2024 INRIA
+//
+
+#ifndef __eigenpy_id_hpp__
+#define __eigenpy_id_hpp__
+
+#include <boost/python.hpp>
+#include <boost/cstdint.hpp>
+
+namespace eigenpy {
+
+///
+/// \brief Add the Python method id to retrieving a unique id for a given object
+/// exposed with Boost.Python
+///
+template <class C>
+struct IdVisitor : public bp::def_visitor<IdVisitor<C> > {
+  template <class PyClass>
+  void visit(PyClass& cl) const {
+    cl.def("id", &id, bp::arg("self"),
+           "Returns the unique identity of an object.\n"
+           "For object held in C++, it corresponds to its memory address.");
+  }
+
+ private:
+  static boost::int64_t id(const C& self) {
+    return boost::int64_t(reinterpret_cast<const void*>(&self));
+  }
+};
+}  // namespace eigenpy
+
+#endif  // ifndef __eigenpy_id_hpp__