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__
@@ -40,6 +40,24 @@ struct expose_eigen_type_impl<MatType, Eigen::MatrixBase<MatType>, Scalar> {
   }
 };
 
+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>,

include/eigenpy/eigen-allocator.hpp

@@ -198,6 +198,91 @@ struct cast<Scalar, NewScalar, EigenBase, false> {
       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;
 
@@ -247,43 +332,9 @@ struct eigen_allocator_impl_matrix {
           pyArray, details::check_swap(pyArray, mat));  // avoid useless cast
       return;
     }
-
-    switch (pyArray_type_code) {
-      case NPY_INT:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_MATRIX(MatType, int, Scalar, pyArray,
-                                                  mat);
-        break;
-      case NPY_LONG:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_MATRIX(MatType, long, Scalar,
-                                                  pyArray, mat);
-        break;
-      case NPY_FLOAT:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_MATRIX(MatType, float, Scalar,
-                                                  pyArray, mat);
-        break;
-      case NPY_CFLOAT:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_MATRIX(MatType, std::complex<float>,
-                                                  Scalar, pyArray, mat);
-        break;
-      case NPY_DOUBLE:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_MATRIX(MatType, double, Scalar,
-                                                  pyArray, mat);
-        break;
-      case NPY_CDOUBLE:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_MATRIX(MatType, std::complex<double>,
-                                                  Scalar, pyArray, mat);
-        break;
-      case NPY_LONGDOUBLE:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_MATRIX(MatType, long double, Scalar,
-                                                  pyArray, mat);
-        break;
-      case NPY_CLONGDOUBLE:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_MATRIX(
-            MatType, std::complex<long double>, Scalar, pyArray, mat);
-        break;
-      default:
-        throw Exception("You asked for a conversion which is not implemented.");
-    }
+    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
@@ -301,43 +352,8 @@ struct eigen_allocator_impl_matrix {
                                      details::check_swap(pyArray, mat)) = mat;
       return;
     }
-
-    switch (pyArray_type_code) {
-      case NPY_INT:
-        EIGENPY_CAST_FROM_EIGEN_MATRIX_TO_PYARRAY(MatType, Scalar, int, mat,
-                                                  pyArray);
-        break;
-      case NPY_LONG:
-        EIGENPY_CAST_FROM_EIGEN_MATRIX_TO_PYARRAY(MatType, Scalar, long, mat,
-                                                  pyArray);
-        break;
-      case NPY_FLOAT:
-        EIGENPY_CAST_FROM_EIGEN_MATRIX_TO_PYARRAY(MatType, Scalar, float, mat,
-                                                  pyArray);
-        break;
-      case NPY_CFLOAT:
-        EIGENPY_CAST_FROM_EIGEN_MATRIX_TO_PYARRAY(
-            MatType, Scalar, std::complex<float>, mat, pyArray);
-        break;
-      case NPY_DOUBLE:
-        EIGENPY_CAST_FROM_EIGEN_MATRIX_TO_PYARRAY(MatType, Scalar, double, mat,
-                                                  pyArray);
-        break;
-      case NPY_CDOUBLE:
-        EIGENPY_CAST_FROM_EIGEN_MATRIX_TO_PYARRAY(
-            MatType, Scalar, std::complex<double>, mat, pyArray);
-        break;
-      case NPY_LONGDOUBLE:
-        EIGENPY_CAST_FROM_EIGEN_MATRIX_TO_PYARRAY(MatType, Scalar, long double,
-                                                  mat, pyArray);
-        break;
-      case NPY_CLONGDOUBLE:
-        EIGENPY_CAST_FROM_EIGEN_MATRIX_TO_PYARRAY(
-            MatType, Scalar, std::complex<long double>, mat, pyArray);
-        break;
-      default:
-        throw Exception("You asked for a conversion which is not implemented.");
-    }
+    throw Exception(
+        "Scalar conversion from Eigen to Numpy is not implemented.");
   }
 };
 
@@ -394,42 +410,9 @@ struct eigen_allocator_impl_tensor {
       return;
     }
 
-    switch (pyArray_type_code) {
-      case NPY_INT:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_TENSOR(TensorType, int, Scalar,
-                                                  pyArray, tensor);
-        break;
-      case NPY_LONG:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_TENSOR(TensorType, long, Scalar,
-                                                  pyArray, tensor);
-        break;
-      case NPY_FLOAT:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_TENSOR(TensorType, float, Scalar,
-                                                  pyArray, tensor);
-        break;
-      case NPY_CFLOAT:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_TENSOR(
-            TensorType, std::complex<float>, Scalar, pyArray, tensor);
-        break;
-      case NPY_DOUBLE:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_TENSOR(TensorType, double, Scalar,
-                                                  pyArray, tensor);
-        break;
-      case NPY_CDOUBLE:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_TENSOR(
-            TensorType, std::complex<double>, Scalar, pyArray, tensor);
-        break;
-      case NPY_LONGDOUBLE:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_TENSOR(TensorType, long double,
-                                                  Scalar, pyArray, tensor);
-        break;
-      case NPY_CLONGDOUBLE:
-        EIGENPY_CAST_FROM_PYARRAY_TO_EIGEN_TENSOR(
-            TensorType, std::complex<long double>, Scalar, pyArray, tensor);
-        break;
-      default:
-        throw Exception("You asked for a conversion which is not implemented.");
-    }
+    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,         \
@@ -454,42 +437,8 @@ struct eigen_allocator_impl_tensor {
       return;
     }
 
-    switch (pyArray_type_code) {
-      case NPY_INT:
-        EIGENPY_CAST_FROM_EIGEN_TENSOR_TO_PYARRAY(TensorType, Scalar, int,
-                                                  tensor, pyArray);
-        break;
-      case NPY_LONG:
-        EIGENPY_CAST_FROM_EIGEN_TENSOR_TO_PYARRAY(TensorType, Scalar, long,
-                                                  tensor, pyArray);
-        break;
-      case NPY_FLOAT:
-        EIGENPY_CAST_FROM_EIGEN_TENSOR_TO_PYARRAY(TensorType, Scalar, float,
-                                                  tensor, pyArray);
-        break;
-      case NPY_CFLOAT:
-        EIGENPY_CAST_FROM_EIGEN_TENSOR_TO_PYARRAY(
-            TensorType, Scalar, std::complex<float>, tensor, pyArray);
-        break;
-      case NPY_DOUBLE:
-        EIGENPY_CAST_FROM_EIGEN_TENSOR_TO_PYARRAY(TensorType, Scalar, double,
-                                                  tensor, pyArray);
-        break;
-      case NPY_CDOUBLE:
-        EIGENPY_CAST_FROM_EIGEN_TENSOR_TO_PYARRAY(
-            TensorType, Scalar, std::complex<double>, tensor, pyArray);
-        break;
-      case NPY_LONGDOUBLE:
-        EIGENPY_CAST_FROM_EIGEN_TENSOR_TO_PYARRAY(TensorType, Scalar,
-                                                  long double, tensor, pyArray);
-        break;
-      case NPY_CLONGDOUBLE:
-        EIGENPY_CAST_FROM_EIGEN_TENSOR_TO_PYARRAY(
-            TensorType, Scalar, std::complex<long double>, tensor, pyArray);
-        break;
-      default:
-        throw Exception("You asked for a conversion which is not implemented.");
-    }
+    throw Exception(
+        "Scalar conversion from Eigen to Numpy is not implemented.");
   }
 };
 #endif

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;
@@ -290,16 +290,10 @@ struct eigen_from_py_impl<MatType, Eigen::MatrixBase<MatType> > {
   static void registration();
 };
 
-#ifdef EIGENPY_MSVC_COMPILER
-template <typename EigenType>
-struct EigenFromPy<EigenType,
-                   typename boost::remove_reference<EigenType>::type::Scalar>
-#else
-template <typename EigenType, typename _Scalar>
-struct EigenFromPy
-#endif
-    : eigen_from_py_impl<EigenType> {
-};
+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(
@@ -565,4 +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
@@ -150,22 +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
 
-#ifdef EIGENPY_MSVC_COMPILER
-template <typename EigenType>
-struct EigenToPy<EigenType,
-                 typename boost::remove_reference<EigenType>::type::Scalar>
-#else
-template <typename EigenType, typename _Scalar>
-struct EigenToPy
-#endif
-    : 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 {
@@ -173,6 +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/numpy-allocator.hpp

@@ -138,7 +138,12 @@ struct numpy_allocator_impl_matrix<Eigen::Ref<MatType, Options, Stride> > {
                         outer_stride = reverse_strides ? mat.innerStride()
                                                        : mat.outerStride();
 
+#if NPY_ABI_VERSION < 0x02000000
       const int elsize = call_PyArray_DescrFromType(Scalar_type_code)->elsize;
+#else
+      const int elsize =
+          PyDataType_ELSIZE(call_PyArray_DescrFromType(Scalar_type_code));
+#endif
       npy_intp strides[2] = {elsize * inner_stride, elsize * outer_stride};
 
       PyArrayObject *pyArray = (PyArrayObject *)call_PyArray_New(
@@ -204,7 +209,12 @@ struct numpy_allocator_impl_matrix<
                         outer_stride = reverse_strides ? mat.innerStride()
                                                        : mat.outerStride();
 
+#if NPY_ABI_VERSION < 0x02000000
       const int elsize = call_PyArray_DescrFromType(Scalar_type_code)->elsize;
+#else
+      const int elsize =
+          PyDataType_ELSIZE(call_PyArray_DescrFromType(Scalar_type_code));
+#endif
       npy_intp strides[2] = {elsize * inner_stride, elsize * outer_stride};
 
       PyArrayObject *pyArray = (PyArrayObject *)call_PyArray_New(