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include/eigenpy/scipy-type.hpp 0 → 100644
View file @ 7102d834
/*
* Copyright 2024 INRIA
*/
#ifndef __eigenpy_scipy_type_hpp__
#define __eigenpy_scipy_type_hpp__
#include "eigenpy/fwd.hpp"
#include "eigenpy/register.hpp"
#include "eigenpy/scalar-conversion.hpp"
#include "eigenpy/numpy-type.hpp"
namespace eigenpy {
struct EIGENPY_DLLAPI ScipyType {
static ScipyType& getInstance();
static void sharedMemory(const bool value);
static bool sharedMemory();
static bp::object getScipyType();
static const PyTypeObject* getScipyCSRMatrixType();
static const PyTypeObject* getScipyCSCMatrixType();
template <typename SparseMatrix>
static bp::object get_pytype_object(
const Eigen::SparseMatrixBase<SparseMatrix>* ptr = nullptr) {
EIGENPY_UNUSED_VARIABLE(ptr);
return SparseMatrix::IsRowMajor ? getInstance().csr_matrix_obj
: getInstance().csc_matrix_obj;
}
template <typename SparseMatrix>
static PyTypeObject const* get_pytype(
const Eigen::SparseMatrixBase<SparseMatrix>* ptr = nullptr) {
EIGENPY_UNUSED_VARIABLE(ptr);
return SparseMatrix::IsRowMajor ? getInstance().csr_matrix_type
: getInstance().csc_matrix_type;
}
static int get_numpy_type_num(const bp::object& obj) {
const PyTypeObject* type = Py_TYPE(obj.ptr());
EIGENPY_USED_VARIABLE_ONLY_IN_DEBUG_MODE(type);
assert(type == getInstance().csr_matrix_type ||
type == getInstance().csc_matrix_type);
bp::object dtype = obj.attr("dtype");
const PyArray_Descr* npy_type =
reinterpret_cast<PyArray_Descr*>(dtype.ptr());
return npy_type->type_num;
}
protected:
ScipyType();
bp::object sparse_module;
// SciPy types
bp::object csr_matrix_obj, csc_matrix_obj;
PyTypeObject *csr_matrix_type, *csc_matrix_type;
bool shared_memory;
};
} // namespace eigenpy
#endif // ifndef __eigenpy_scipy_type_hpp__
include/eigenpy/solvers/BFGSPreconditioners.hpp
View file @ 7102d834
/* /*
* Copyright 2017, Justin Carpentier, LAAS-CNRS * Copyright 2017 CNRS
* * Copyright 2024 Inria
* This file is part of eigenpy.
* eigenpy is free software: you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation, either version 3 of
* the License, or (at your option) any later version.
* eigenpy is distributed in the hope that it will be
* useful, but WITHOUT ANY WARRANTY; without even the implied warranty
* of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details. You should
* have received a copy of the GNU Lesser General Public License along
* with eigenpy. If not, see <http://www.gnu.org/licenses/>.
*/ */
#ifndef __eigenpy_bfgs_preconditioners_hpp__ #ifndef __eigenpy_bfgs_preconditioners_hpp__
#define __eigenpy_bfgs_preconditioners_hpp__ #define __eigenpy_bfgs_preconditioners_hpp__
#include "eigenpy/fwd.hpp"
#include <Eigen/IterativeLinearSolvers> #include <Eigen/IterativeLinearSolvers>
#include "eigenpy/fwd.hpp"
#include "eigenpy/solvers/BasicPreconditioners.hpp" #include "eigenpy/solvers/BasicPreconditioners.hpp"
namespace eigenpy namespace eigenpy {
{
template <typename Preconditioner>
namespace bp = boost::python; struct BFGSPreconditionerBaseVisitor
: public bp::def_visitor<BFGSPreconditionerBaseVisitor<Preconditioner> > {
template<typename Preconditioner> typedef Eigen::VectorXd VectorType;
struct BFGSPreconditionerBaseVisitor template <class PyClass>
: public bp::def_visitor< BFGSPreconditionerBaseVisitor<Preconditioner> > void visit(PyClass& cl) const {
{ cl.def(PreconditionerBaseVisitor<Preconditioner>())
.def("rows", &Preconditioner::rows,
typedef Eigen::VectorXd VectorType; "Returns the number of rows in the preconditioner.")
template<class PyClass> .def("cols", &Preconditioner::cols,
void visit(PyClass& cl) const "Returns the number of cols in the preconditioner.")
{ .def("dim", &Preconditioner::dim,
cl "Returns the dimension of the BFGS preconditioner")
.def(PreconditionerBaseVisitor<Preconditioner>()) .def("update",
.def("rows",&Preconditioner::rows,"Returns the number of rows in the preconditioner.") (const Preconditioner& (Preconditioner::*)(const VectorType&,
.def("cols",&Preconditioner::cols,"Returns the number of cols in the preconditioner.") const VectorType&)
.def("dim",&Preconditioner::dim,"Returns the dimension of the BFGS preconditioner") const) &
.def("update",(const Preconditioner& (Preconditioner::*)(const VectorType &, const VectorType &) const)&Preconditioner::update,bp::args("s","y"),"Update the BFGS estimate of the matrix A.",bp::return_value_policy<bp::reference_existing_object>()) Preconditioner::update,
.def("reset",&Preconditioner::reset,"Reset the BFGS estimate.") bp::args("s", "y"), "Update the BFGS estimate of the matrix A.",
; bp::return_value_policy<bp::reference_existing_object>())
.def("reset", &Preconditioner::reset, "Reset the BFGS estimate.");
} }
static void expose(const std::string & name) static void expose(const std::string& name) {
{ bp::class_<Preconditioner>(name, bp::no_init)
bp::class_<Preconditioner>(name, .def(IdVisitor<Preconditioner>())
bp::no_init) .def(BFGSPreconditionerBaseVisitor<Preconditioner>());
.def(BFGSPreconditionerBaseVisitor<Preconditioner>()) }
; };
} template <typename Preconditioner>
struct LimitedBFGSPreconditionerBaseVisitor
}; : public bp::def_visitor<
LimitedBFGSPreconditionerBaseVisitor<Preconditioner> > {
template<typename Preconditioner> template <class PyClass>
struct LimitedBFGSPreconditionerBaseVisitor void visit(PyClass& cl) const {
: public bp::def_visitor< LimitedBFGSPreconditionerBaseVisitor<Preconditioner> > cl.def(PreconditionerBaseVisitor<Preconditioner>())
{ .def(BFGSPreconditionerBaseVisitor<Preconditioner>())
template<class PyClass> .def("resize", &Preconditioner::resize, bp::arg("dim"),
void visit(PyClass& cl) const "Resizes the preconditionner with size dim.",
{ bp::return_value_policy<bp::reference_existing_object>());
cl }
.def(PreconditionerBaseVisitor<Preconditioner>())
.def(BFGSPreconditionerBaseVisitor<Preconditioner>()) static void expose(const std::string& name) {
.def("resize",&Preconditioner::resize,bp::arg("dim"),"Resizes the preconditionner with size dim.",bp::return_value_policy<bp::reference_existing_object>()) bp::class_<Preconditioner>(name.c_str(), bp::no_init)
; .def(IdVisitor<Preconditioner>())
.def(LimitedBFGSPreconditionerBaseVisitor<Preconditioner>());
} }
};
static void expose(const std::string & name)
{ } // namespace eigenpy
bp::class_<Preconditioner>(name.c_str(),
bp::no_init)
.def(LimitedBFGSPreconditionerBaseVisitor<Preconditioner>())
;
}
};
} // namespace eigenpy
#endif // ifndef __eigenpy_bfgs_preconditioners_hpp__ #endif // ifndef __eigenpy_bfgs_preconditioners_hpp__
include/eigenpy/solvers/BasicPreconditioners.hpp
View file @ 7102d834
/* /*
* Copyright 2017, Justin Carpentier, LAAS-CNRS * Copyright 2017 CNRS
* * Copyright 2024 Inria
* This file is part of eigenpy.
* eigenpy is free software: you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation, either version 3 of
* the License, or (at your option) any later version.
* eigenpy is distributed in the hope that it will be
* useful, but WITHOUT ANY WARRANTY; without even the implied warranty
* of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details. You should
* have received a copy of the GNU Lesser General Public License along
* with eigenpy. If not, see <http://www.gnu.org/licenses/>.
*/ */
#ifndef __eigenpy_basic_preconditioners_hpp__ #ifndef __eigenpy_basic_preconditioners_hpp__
#define __eigenpy_basic_preconditioners_hpp__ #define __eigenpy_basic_preconditioners_hpp__
#include "eigenpy/fwd.hpp"
#include <Eigen/IterativeLinearSolvers> #include <Eigen/IterativeLinearSolvers>
namespace eigenpy #include "eigenpy/fwd.hpp"
{
namespace eigenpy {
namespace bp = boost::python;
template <typename Preconditioner>
template<typename Preconditioner> struct PreconditionerBaseVisitor
struct PreconditionerBaseVisitor : public bp::def_visitor<PreconditionerBaseVisitor<Preconditioner> > {
: public bp::def_visitor< PreconditionerBaseVisitor<Preconditioner> > typedef Eigen::MatrixXd MatrixType;
{ typedef Eigen::VectorXd VectorType;
typedef Eigen::MatrixXd MatrixType;
typedef Eigen::VectorXd VectorType; template <class PyClass>
void visit(PyClass& cl) const {
template<class PyClass> cl.def(bp::init<>("Default constructor"))
void visit(PyClass& cl) const .def(bp::init<MatrixType>(bp::args("self", "A"),
{ "Initialize the preconditioner with matrix A "
cl "for further Az=b solving."))
.def(bp::init<>("Default constructor")) #if EIGEN_VERSION_AT_LEAST(3, 3, 0)
.def(bp::init<MatrixType>(bp::arg("A"),"Initialize the preconditioner with matrix A for further Az=b solving.")) .def("info", &Preconditioner::info,
#if EIGEN_VERSION_AT_LEAST(3,3,0) "Returns success if the Preconditioner has been well initialized.")
.def("info",&Preconditioner::info,
"Returns success if the Preconditioner has been well initialized.")
#endif #endif
.def("solve",&solve,bp::arg("b"), .def("solve", &solve, bp::arg("b"),
"Returns the solution A * z = b where the preconditioner is an estimate of A^-1.") "Returns the solution A * z = b where the preconditioner is an "
"estimate of A^-1.")
.def("compute",&Preconditioner::template compute<MatrixType>,bp::arg("mat"),
"Initialize the preconditioner from the matrix value.", .def("compute", &Preconditioner::template compute<MatrixType>,
bp::return_value_policy<bp::reference_existing_object>()) bp::arg("mat"),
.def("factorize",&Preconditioner::template factorize<MatrixType>,bp::arg("mat"), "Initialize the preconditioner from the matrix value.",
"Initialize the preconditioner from the matrix value, i.e factorize the mat given as input to approximate its inverse.", bp::return_value_policy<bp::reference_existing_object>())
bp::return_value_policy<bp::reference_existing_object>()) .def("factorize", &Preconditioner::template factorize<MatrixType>,
; bp::arg("mat"),
"Initialize the preconditioner from the matrix value, i.e "
} "factorize the mat given as input to approximate its inverse.",
bp::return_value_policy<bp::reference_existing_object>());
private: }
static VectorType solve(Preconditioner & self, const VectorType & b) private:
{ static VectorType solve(Preconditioner& self, const VectorType& b) {
return self.solve(b); return self.solve(b);
} }
};
};
template <typename Scalar>
template<typename Scalar> struct DiagonalPreconditionerVisitor
struct DiagonalPreconditionerVisitor : PreconditionerBaseVisitor<Eigen::DiagonalPreconditioner<Scalar> > : PreconditionerBaseVisitor<Eigen::DiagonalPreconditioner<Scalar> > {
{ typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> MatrixType;
typedef Eigen::Matrix<Scalar,Eigen::Dynamic,Eigen::Dynamic> MatrixType; typedef Eigen::DiagonalPreconditioner<Scalar> Preconditioner;
typedef Eigen::DiagonalPreconditioner<Scalar> Preconditioner;
template <class PyClass>
template<class PyClass> void visit(PyClass& cl) const {
void visit(PyClass& cl) const cl.def(PreconditionerBaseVisitor<Preconditioner>())
{ .def("rows", &Preconditioner::rows,
cl "Returns the number of rows in the preconditioner.")
.def(PreconditionerBaseVisitor<Preconditioner>()) .def("cols", &Preconditioner::cols,
.def("rows",&Preconditioner::rows,"Returns the number of rows in the preconditioner.") "Returns the number of cols in the preconditioner.");
.def("cols",&Preconditioner::cols,"Returns the number of cols in the preconditioner.") }
;
static void expose() {
} bp::class_<Preconditioner>(
"DiagonalPreconditioner",
static void expose() "A preconditioner based on the digonal entrie.\n"
{ "This class allows to approximately solve for A.x = b problems "
bp::class_<Preconditioner>("DiagonalPreconditioner", "assuming A is a diagonal matrix.",
"A preconditioner based on the digonal entrie.\n" bp::no_init)
"This class allows to approximately solve for A.x = b problems assuming A is a diagonal matrix.", .def(IdVisitor<Preconditioner>());
bp::no_init) }
; };
} #if EIGEN_VERSION_AT_LEAST(3, 3, 5)
}; template <typename Scalar>
struct LeastSquareDiagonalPreconditionerVisitor
#if EIGEN_VERSION_AT_LEAST(3,3,5) : PreconditionerBaseVisitor<
template<typename Scalar> Eigen::LeastSquareDiagonalPreconditioner<Scalar> > {
struct LeastSquareDiagonalPreconditionerVisitor : PreconditionerBaseVisitor<Eigen::LeastSquareDiagonalPreconditioner<Scalar> > typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> MatrixType;
{ typedef Eigen::LeastSquareDiagonalPreconditioner<Scalar> Preconditioner;
typedef Eigen::Matrix<Scalar,Eigen::Dynamic,Eigen::Dynamic> MatrixType;
typedef Eigen::LeastSquareDiagonalPreconditioner<Scalar> Preconditioner; template <class PyClass>
void visit(PyClass&) const {}
template<class PyClass>
void visit(PyClass&) const static void expose() {
{ bp::class_<Preconditioner>(
} "LeastSquareDiagonalPreconditioner",
"Jacobi preconditioner for LeastSquaresConjugateGradient.\n"
static void expose() "his class allows to approximately solve for A' A x = A' b problems "
{ "assuming A' A is a diagonal matrix.",
bp::class_<Preconditioner>("LeastSquareDiagonalPreconditioner", bp::no_init)
"Jacobi preconditioner for LeastSquaresConjugateGradient.\n" .def(DiagonalPreconditionerVisitor<Scalar>())
"his class allows to approximately solve for A' A x = A' b problems assuming A' A is a diagonal matrix.", .def(IdVisitor<Preconditioner>());
bp::no_init) }
.def(DiagonalPreconditionerVisitor<Scalar>()) };
;
}
};
#endif #endif
struct IdentityPreconditionerVisitor : PreconditionerBaseVisitor<Eigen::IdentityPreconditioner > struct IdentityPreconditionerVisitor
{ : PreconditionerBaseVisitor<Eigen::IdentityPreconditioner> {
typedef Eigen::IdentityPreconditioner Preconditioner; typedef Eigen::IdentityPreconditioner Preconditioner;
template<class PyClass> template <class PyClass>
void visit(PyClass&) const void visit(PyClass&) const {}
{
} static void expose() {
bp::class_<Preconditioner>("IdentityPreconditioner", bp::no_init)
static void expose() .def(PreconditionerBaseVisitor<Preconditioner>())
{ .def(IdVisitor<Preconditioner>());
bp::class_<Preconditioner>("IdentityPreconditioner", }
bp::no_init) };
.def(PreconditionerBaseVisitor<Preconditioner>())
; } // namespace eigenpy
} #endif // ifndef __eigenpy_basic_preconditioners_hpp__
};
} // namespace eigenpy
#endif // ifndef __eigenpy_basic_preconditioners_hpp__
include/eigenpy/solvers/ConjugateGradient.hpp
View file @ 7102d834
/* /*
* Copyright 2017, Justin Carpentier, LAAS-CNRS * Copyright 2017 CNRS
*
* This file is part of eigenpy.
* eigenpy is free software: you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation, either version 3 of
* the License, or (at your option) any later version.
* eigenpy is distributed in the hope that it will be
* useful, but WITHOUT ANY WARRANTY; without even the implied warranty
* of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details. You should
* have received a copy of the GNU Lesser General Public License along
* with eigenpy. If not, see <http://www.gnu.org/licenses/>.
*/ */
#ifndef __eigenpy_conjugate_gradient_hpp__ #ifndef __eigenpy_conjugate_gradient_hpp__
#define __eigenpy_conjugate_gradient_hpp__ #define __eigenpy_conjugate_gradient_hpp__
#include "eigenpy/fwd.hpp"
#include <Eigen/IterativeLinearSolvers> #include <Eigen/IterativeLinearSolvers>
#include "eigenpy/fwd.hpp"
#include "eigenpy/solvers/IterativeSolverBase.hpp" #include "eigenpy/solvers/IterativeSolverBase.hpp"
namespace eigenpy namespace eigenpy {
{
template <typename ConjugateGradient>
namespace bp = boost::python; struct ConjugateGradientVisitor
: public boost::python::def_visitor<
template<typename ConjugateGradient> ConjugateGradientVisitor<ConjugateGradient> > {
struct ConjugateGradientVisitor typedef typename ConjugateGradient::MatrixType MatrixType;
: public boost::python::def_visitor< ConjugateGradientVisitor<ConjugateGradient> >
{ template <class PyClass>
typedef typename ConjugateGradient::MatrixType MatrixType; void visit(PyClass& cl) const {
cl.def(IterativeSolverVisitor<ConjugateGradient>())
template<class PyClass> .def(bp::init<>("Default constructor"))
void visit(PyClass& cl) const .def(bp::init<MatrixType>(
{ bp::arg("A"),
cl "Initialize the solver with matrix A for further Ax=b solving.\n"
.def(IterativeSolverVisitor<ConjugateGradient>()) "This constructor is a shortcut for the default constructor "
.def(bp::init<>("Default constructor")) "followed by a call to compute()."));
.def(bp::init<MatrixType>(bp::arg("A"),"Initialize the solver with matrix A for further Ax=b solving.\n" }
"This constructor is a shortcut for the default constructor followed by a call to compute()."))
; static void expose(const std::string& name = "ConjugateGradient") {
bp::class_<ConjugateGradient, boost::noncopyable>(name.c_str(), bp::no_init)
} .def(ConjugateGradientVisitor<ConjugateGradient>())
.def(IdVisitor<ConjugateGradient>());
static void expose(const std::string & name = "ConjugateGradient") }
{ };
bp::class_<ConjugateGradient,boost::noncopyable>(name.c_str(),
bp::no_init) } // namespace eigenpy
.def(ConjugateGradientVisitor<ConjugateGradient>())
; #endif // ifndef __eigenpy_conjugate_gradient_hpp__
}
};
} // namespace eigenpy
#endif // ifndef __eigenpy_conjugate_gradient_hpp__
include/eigenpy/solvers/IterativeSolverBase.hpp
View file @ 7102d834
/* /*
* Copyright 2017, Justin Carpentier, LAAS-CNRS * Copyright 2017 CNRS
*
* This file is part of eigenpy.
* eigenpy is free software: you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation, either version 3 of
* the License, or (at your option) any later version.
* eigenpy is distributed in the hope that it will be
* useful, but WITHOUT ANY WARRANTY; without even the implied warranty
* of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details. You should
* have received a copy of the GNU Lesser General Public License along
* with eigenpy. If not, see <http://www.gnu.org/licenses/>.
*/ */
#ifndef __eigenpy_iterative_solver_base_hpp__ #ifndef __eigenpy_iterative_solver_base_hpp__
...@@ -20,83 +8,99 @@ ...@@ -20,83 +8,99 @@
#include "eigenpy/fwd.hpp" #include "eigenpy/fwd.hpp"
#include "eigenpy/solvers/SparseSolverBase.hpp" #include "eigenpy/solvers/SparseSolverBase.hpp"
namespace eigenpy namespace eigenpy {
{
namespace bp = boost::python;
template<typename IterativeSolver>
struct IterativeSolverVisitor
: public boost::python::def_visitor< IterativeSolverVisitor<IterativeSolver> >
{
typedef typename IterativeSolver::MatrixType MatrixType;
typedef typename IterativeSolver::Preconditioner Preconditioner;
typedef Eigen::VectorXd VectorType;
template<class PyClass>
void visit(PyClass& cl) const
{
typedef IterativeSolver IS;
cl
.def(SparseSolverVisitor<IS>())
.def("error",&IS::error,"Returns the tolerance error reached during the last solve.\n"
"It is a close approximation of the true relative residual error |Ax-b|/|b|.")
.def("info",&IS::info,"Returns success if the iterations converged, and NoConvergence otherwise.")
.def("iterations",&IS::iterations,"Returns the number of iterations performed during the last solve.")
.def("maxIterations",&IS::maxIterations,"Returns the max number of iterations.\n"
"It is either the value setted by setMaxIterations or, by default, twice the number of columns of the matrix.")
.def("setMaxIterations",&IS::setMaxIterations,"Sets the max number of iterations.\n"
"Default is twice the number of columns of the matrix.",
bp::return_value_policy<bp::reference_existing_object>())
.def("tolerance",&IS::tolerance,"Returns he tolerance threshold used by the stopping criteria.")
.def("setTolerance",&IS::setTolerance,"Sets the tolerance threshold used by the stopping criteria.\n"
"This value is used as an upper bound to the relative residual error: |Ax-b|/|b|. The default value is the machine precision.",
bp::return_value_policy<bp::reference_existing_object>())
.def("analyzePattern",&analyzePattern,bp::arg("A"),"Initializes the iterative solver for the sparsity pattern of the matrix A for further solving Ax=b problems.\n"
"Currently, this function mostly calls analyzePattern on the preconditioner.\n"
"In the future we might, for instance, implement column reordering for faster matrix vector products.",
bp::return_value_policy<bp::reference_existing_object>())
.def("factorize",&factorize,bp::arg("A"),"Initializes the iterative solver with the numerical values of the matrix A for further solving Ax=b problems.\n"
"Currently, this function mostly calls factorize on the preconditioner.",
bp::return_value_policy<bp::reference_existing_object>())
.def("compute",&compute,bp::arg("A"),"Initializes the iterative solver with the numerical values of the matrix A for further solving Ax=b problems.\n"
"Currently, this function mostly calls factorize on the preconditioner.\n"
"In the future we might, for instance, implement column reordering for faster matrix vector products.",
bp::return_value_policy<bp::reference_existing_object>())
.def("solveWithGuess",&solveWithGuess,bp::args("b","x0"),
"Returns the solution x of Ax = b using the current decomposition of A and x0 as an initial solution.")
.def("preconditioner",(Preconditioner & (IS::*)(void))&IS::preconditioner,"Returns a read-write reference to the preconditioner for custom configuration.",bp::return_internal_reference<>())
;
}
private:
static IterativeSolver & factorize(IterativeSolver & self, const MatrixType & m)
{
return self.factorize(m);
}
static IterativeSolver & compute(IterativeSolver & self, const MatrixType & m)
{
return self.compute(m);
}
static IterativeSolver & analyzePattern(IterativeSolver & self, const MatrixType & m)
{
return self.analyzePattern(m);
}
static VectorType solveWithGuess(IterativeSolver & self, const Eigen::VectorXd & b, const Eigen::VectorXd & x0)
{
return self.solveWithGuess(b,x0);
}
};
} // namespace eigenpy template <typename IterativeSolver>
struct IterativeSolverVisitor : public boost::python::def_visitor<
IterativeSolverVisitor<IterativeSolver> > {
typedef typename IterativeSolver::MatrixType MatrixType;
typedef typename IterativeSolver::Preconditioner Preconditioner;
typedef Eigen::VectorXd VectorType;
#endif // ifndef __eigenpy_iterative_solver_base_hpp__ template <class PyClass>
void visit(PyClass& cl) const {
typedef IterativeSolver IS;
cl.def(SparseSolverVisitor<IS>())
.def("error", &IS::error,
"Returns the tolerance error reached during the last solve.\n"
"It is a close approximation of the true relative residual error "
"|Ax-b|/|b|.")
.def("info", &IS::info,
"Returns success if the iterations converged, and NoConvergence "
"otherwise.")
.def(
"iterations", &IS::iterations,
"Returns the number of iterations performed during the last solve.")
.def("maxIterations", &IS::maxIterations,
"Returns the max number of iterations.\n"
"It is either the value setted by setMaxIterations or, by "
"default, twice the number of columns of the matrix.")
.def("setMaxIterations", &IS::setMaxIterations,
"Sets the max number of iterations.\n"
"Default is twice the number of columns of the matrix.",
bp::return_value_policy<bp::reference_existing_object>())
.def("tolerance", &IS::tolerance,
"Returns he tolerance threshold used by the stopping criteria.")
.def("setTolerance", &IS::setTolerance,
"Sets the tolerance threshold used by the stopping criteria.\n"
"This value is used as an upper bound to the relative residual "
"error: |Ax-b|/|b|. The default value is the machine precision.",
bp::return_value_policy<bp::reference_existing_object>())
.def("analyzePattern", &analyzePattern, bp::arg("A"),
"Initializes the iterative solver for the sparsity pattern of the "
"matrix A for further solving Ax=b problems.\n"
"Currently, this function mostly calls analyzePattern on the "
"preconditioner.\n"
"In the future we might, for instance, implement column "
"reordering for faster matrix vector products.",
bp::return_value_policy<bp::reference_existing_object>())
.def("factorize", &factorize, bp::arg("A"),
"Initializes the iterative solver with the numerical values of "
"the matrix A for further solving Ax=b problems.\n"
"Currently, this function mostly calls factorize on the "
"preconditioner.",
bp::return_value_policy<bp::reference_existing_object>())
.def("compute", &compute, bp::arg("A"),
"Initializes the iterative solver with the numerical values of "
"the matrix A for further solving Ax=b problems.\n"
"Currently, this function mostly calls factorize on the "
"preconditioner.\n"
"In the future we might, for instance, implement column "
"reordering for faster matrix vector products.",
bp::return_value_policy<bp::reference_existing_object>())
.def("solveWithGuess", &solveWithGuess, bp::args("b", "x0"),
"Returns the solution x of Ax = b using the current decomposition "
"of A and x0 as an initial solution.")
.def("preconditioner",
(Preconditioner & (IS::*)(void)) & IS::preconditioner,
"Returns a read-write reference to the preconditioner for custom "
"configuration.",
bp::return_internal_reference<>());
}
private:
static IterativeSolver& factorize(IterativeSolver& self,
const MatrixType& m) {
return self.factorize(m);
}
static IterativeSolver& compute(IterativeSolver& self, const MatrixType& m) {
return self.compute(m);
}
static IterativeSolver& analyzePattern(IterativeSolver& self,
const MatrixType& m) {
return self.analyzePattern(m);
}
static VectorType solveWithGuess(IterativeSolver& self,
const Eigen::VectorXd& b,
const Eigen::VectorXd& x0) {
return self.solveWithGuess(b, x0);
}
};
} // namespace eigenpy
#endif // ifndef __eigenpy_iterative_solver_base_hpp__
include/eigenpy/solvers/LeastSquaresConjugateGradient.hpp
View file @ 7102d834
/* /*
* Copyright 2017-2018, Justin Carpentier, LAAS-CNRS * Copyright 2017-2018 CNRS
*
* This file is part of eigenpy.
* eigenpy is free software: you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation, either version 3 of
* the License, or (at your option) any later version.
* eigenpy is distributed in the hope that it will be
* useful, but WITHOUT ANY WARRANTY; without even the implied warranty
* of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details. You should
* have received a copy of the GNU Lesser General Public License along
* with eigenpy. If not, see <http://www.gnu.org/licenses/>.
*/ */
#ifndef __eigenpy_least_square_conjugate_gradient_hpp__ #ifndef __eigenpy_least_square_conjugate_gradient_hpp__
#define __eigenpy_least_square_conjugate_gradient_hpp__ #define __eigenpy_least_square_conjugate_gradient_hpp__
#include "eigenpy/fwd.hpp"
#include <Eigen/IterativeLinearSolvers> #include <Eigen/IterativeLinearSolvers>
#include "eigenpy/fwd.hpp"
#include "eigenpy/solvers/IterativeSolverBase.hpp" #include "eigenpy/solvers/IterativeSolverBase.hpp"
namespace eigenpy namespace eigenpy {
{
template <typename LeastSquaresConjugateGradient>
namespace bp = boost::python; struct LeastSquaresConjugateGradientVisitor
: public boost::python::def_visitor<LeastSquaresConjugateGradientVisitor<
template<typename LeastSquaresConjugateGradient> LeastSquaresConjugateGradient> > {
struct LeastSquaresConjugateGradientVisitor typedef Eigen::MatrixXd MatrixType;
: public boost::python::def_visitor< LeastSquaresConjugateGradientVisitor<LeastSquaresConjugateGradient> >
{ template <class PyClass>
typedef Eigen::MatrixXd MatrixType; void visit(PyClass& cl) const {
cl.def(bp::init<>("Default constructor"))
template<class PyClass> .def(bp::init<MatrixType>(
void visit(PyClass& cl) const bp::arg("A"),
{ "Initialize the solver with matrix A for further || Ax - b || "
cl "solving.\n"
.def(bp::init<>("Default constructor")) "This constructor is a shortcut for the default constructor "
.def(bp::init<MatrixType>(bp::arg("A"),"Initialize the solver with matrix A for further || Ax - b || solving.\n" "followed by a call to compute()."));
"This constructor is a shortcut for the default constructor followed by a call to compute().")) }
;
static void expose() {
} bp::class_<LeastSquaresConjugateGradient, boost::noncopyable>(
"LeastSquaresConjugateGradient", bp::no_init)
static void expose() .def(IterativeSolverVisitor<LeastSquaresConjugateGradient>())
{ .def(LeastSquaresConjugateGradientVisitor<
bp::class_<LeastSquaresConjugateGradient,boost::noncopyable>("LeastSquaresConjugateGradient", LeastSquaresConjugateGradient>())
bp::no_init) .def(IdVisitor<LeastSquaresConjugateGradient>());
.def(IterativeSolverVisitor<LeastSquaresConjugateGradient>()) }
.def(LeastSquaresConjugateGradientVisitor<LeastSquaresConjugateGradient>()) };
;
} // namespace eigenpy
}
#endif // ifndef __eigenpy_least_square_conjugate_gradient_hpp__
};
} // namespace eigenpy
#endif // ifndef __eigenpy_least_square_conjugate_gradient_hpp__
include/eigenpy/solvers/SparseSolverBase.hpp
View file @ 7102d834
/* /*
* Copyright 2017, Justin Carpentier, LAAS-CNRS * Copyright 2017 CNRS
*
* This file is part of eigenpy.
* eigenpy is free software: you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation, either version 3 of
* the License, or (at your option) any later version.
* eigenpy is distributed in the hope that it will be
* useful, but WITHOUT ANY WARRANTY; without even the implied warranty
* of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details. You should
* have received a copy of the GNU Lesser General Public License along
* with eigenpy. If not, see <http://www.gnu.org/licenses/>.
*/ */
#ifndef __eigenpy_sparse_solver_base_hpp__ #ifndef __eigenpy_sparse_solver_base_hpp__
...@@ -19,39 +7,26 @@ ...@@ -19,39 +7,26 @@
#include "eigenpy/fwd.hpp" #include "eigenpy/fwd.hpp"
namespace eigenpy namespace eigenpy {
{
template <typename SparseSolver>
namespace bp = boost::python; struct SparseSolverVisitor
: public bp::def_visitor<SparseSolverVisitor<SparseSolver> > {
template<typename SparseSolver> typedef Eigen::VectorXd VectorType;
struct SparseSolverVisitor
: public bp::def_visitor< SparseSolverVisitor<SparseSolver> > template <class PyClass>
{ void visit(PyClass& cl) const {
cl.def("solve", &solve, bp::arg("b"),
typedef Eigen::VectorXd VectorType; "Returns the solution x of Ax = b using the current decomposition "
"of A.");
template<class PyClass> }
void visit(PyClass& cl) const
{ private:
cl static VectorType solve(SparseSolver& self, const VectorType& b) {
.def("solve",&solve,bp::arg("b"), return self.solve(b);
"Returns the solution x of Ax = b using the current decomposition of A.") }
; };
} } // namespace eigenpy
private: #endif // ifndef __eigenpy_sparse_solver_base_hpp__
static VectorType solve(SparseSolver & self, const VectorType & b)
{
return self.solve(b);
}
};
} // namespace eigenpy
#endif // ifndef __eigenpy_sparse_solver_base_hpp__
include/eigenpy/solvers/preconditioners.hpp
View file @ 7102d834
/* /*
* Copyright 2017, Justin Carpentier, LAAS-CNRS * Copyright 2017 CNRS
*
* This file is part of eigenpy.
* eigenpy is free software: you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation, either version 3 of
* the License, or (at your option) any later version.
* eigenpy is distributed in the hope that it will be
* useful, but WITHOUT ANY WARRANTY; without even the implied warranty
* of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details. You should
* have received a copy of the GNU Lesser General Public License along
* with eigenpy. If not, see <http://www.gnu.org/licenses/>.
*/ */
#ifndef __eigenpy_preconditioners_hpp__ #ifndef __eigenpy_preconditioners_hpp__
...@@ -19,11 +7,10 @@ ...@@ -19,11 +7,10 @@
#include "eigenpy/config.hpp" #include "eigenpy/config.hpp"
namespace eigenpy namespace eigenpy {
{
void EIGENPY_DLLAPI exposePreconditioners();
} // namespace eigenpy
#endif // define __eigenpy_preconditioners_hpp__ void EIGENPY_DLLAPI exposePreconditioners();
} // namespace eigenpy
#endif // define __eigenpy_preconditioners_hpp__
include/eigenpy/solvers/solvers.hpp
View file @ 7102d834
...@@ -7,12 +7,11 @@ ...@@ -7,12 +7,11 @@
#include "eigenpy/config.hpp" #include "eigenpy/config.hpp"
namespace eigenpy namespace eigenpy {
{ struct SolversScope {};
struct SolversScope {};
void EIGENPY_DLLAPI exposeSolvers();
} // namespace eigenpy
#endif // define __eigenpy_solvers_hpp__ void EIGENPY_DLLAPI exposeSolvers();
} // namespace eigenpy
#endif // define __eigenpy_solvers_hpp__
include/eigenpy/sparse/eigen-from-python.hpp 0 → 100644
View file @ 7102d834
//
// Copyright (c) 2024 INRIA
//
#ifndef __eigenpy_sparse_eigen_from_python_hpp__
#define __eigenpy_sparse_eigen_from_python_hpp__
#include "eigenpy/fwd.hpp"
#include "eigenpy/eigen-allocator.hpp"
#include "eigenpy/scipy-type.hpp"
#include "eigenpy/scalar-conversion.hpp"
namespace eigenpy {
template <typename SparseMatrixType>
struct expected_pytype_for_arg<SparseMatrixType,
Eigen::SparseMatrixBase<SparseMatrixType> > {
static PyTypeObject const *get_pytype() {
PyTypeObject const *py_type = ScipyType::get_pytype<SparseMatrixType>();
return py_type;
}
};
} // namespace eigenpy
namespace boost {
namespace python {
namespace converter {
template <typename Scalar, int Options, typename StorageIndex>
struct expected_pytype_for_arg<
Eigen::SparseMatrix<Scalar, Options, StorageIndex> >
: eigenpy::expected_pytype_for_arg<
Eigen::SparseMatrix<Scalar, Options, StorageIndex> > {};
template <typename Scalar, int Options, typename StorageIndex>
struct rvalue_from_python_data<
Eigen::SparseMatrix<Scalar, Options, StorageIndex> const &>
: ::eigenpy::rvalue_from_python_data<
Eigen::SparseMatrix<Scalar, Options, StorageIndex> const &> {
typedef Eigen::SparseMatrix<Scalar, Options, StorageIndex> T;
EIGENPY_RVALUE_FROM_PYTHON_DATA_INIT(T const &)
};
template <typename Derived>
struct rvalue_from_python_data<Eigen::SparseMatrixBase<Derived> const &>
: ::eigenpy::rvalue_from_python_data<Derived const &> {
EIGENPY_RVALUE_FROM_PYTHON_DATA_INIT(Derived const &)
};
} // namespace converter
} // namespace python
} // namespace boost
namespace boost {
namespace python {
namespace detail {
// template <typename TensorType>
// struct referent_storage<Eigen::TensorRef<TensorType> &> {
// typedef Eigen::TensorRef<TensorType> RefType;
// typedef ::eigenpy::details::referent_storage_eigen_ref<RefType>
// StorageType; typedef typename ::eigenpy::aligned_storage<
// referent_size<StorageType &>::value>::type type;
// };
// template <typename TensorType>
// struct referent_storage<const Eigen::TensorRef<const TensorType> &> {
// typedef Eigen::TensorRef<const TensorType> RefType;
// typedef ::eigenpy::details::referent_storage_eigen_ref<RefType>
// StorageType; typedef typename ::eigenpy::aligned_storage<
// referent_size<StorageType &>::value>::type type;
// };
} // namespace detail
} // namespace python
} // namespace boost
namespace eigenpy {
template <typename SparseMatrixType>
struct eigen_from_py_impl<SparseMatrixType,
Eigen::SparseMatrixBase<SparseMatrixType> > {
typedef typename SparseMatrixType::Scalar Scalar;
/// \brief Determine if pyObj can be converted into a MatType object
static void *convertible(PyObject *pyObj);
/// \brief Allocate memory and copy pyObj in the new storage
static void construct(PyObject *pyObj,
bp::converter::rvalue_from_python_stage1_data *memory);
static void registration();
};
template <typename SparseMatrixType>
void *eigen_from_py_impl<
SparseMatrixType,
Eigen::SparseMatrixBase<SparseMatrixType> >::convertible(PyObject *pyObj) {
const PyTypeObject *type = Py_TYPE(pyObj);
const PyTypeObject *sparse_matrix_py_type =
ScipyType::get_pytype<SparseMatrixType>();
typedef typename SparseMatrixType::Scalar Scalar;
if (type != sparse_matrix_py_type) return 0;
bp::object obj(bp::handle<>(bp::borrowed(pyObj)));
const int type_num = ScipyType::get_numpy_type_num(obj);
if (!np_type_is_convertible_into_scalar<Scalar>(type_num)) return 0;
return pyObj;
}
template <typename MatOrRefType>
void eigen_sparse_matrix_from_py_construct(
PyObject *pyObj, bp::converter::rvalue_from_python_stage1_data *memory) {
typedef typename MatOrRefType::Scalar Scalar;
typedef typename MatOrRefType::StorageIndex StorageIndex;
typedef Eigen::Map<MatOrRefType> MapMatOrRefType;
bp::converter::rvalue_from_python_storage<MatOrRefType> *storage =
reinterpret_cast<
bp::converter::rvalue_from_python_storage<MatOrRefType> *>(
reinterpret_cast<void *>(memory));
void *raw_ptr = storage->storage.bytes;
bp::object obj(bp::handle<>(bp::borrowed(pyObj)));
const int type_num_python_sparse_matrix = ScipyType::get_numpy_type_num(obj);
const int type_num_eigen_sparse_matrix = Register::getTypeCode<Scalar>();
if (type_num_eigen_sparse_matrix == type_num_python_sparse_matrix) {
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1> DataVector;
// typedef const Eigen::Ref<const DataVector> RefDataVector;
DataVector data = bp::extract<DataVector>(obj.attr("data"));
bp::tuple shape = bp::extract<bp::tuple>(obj.attr("shape"));
typedef Eigen::Matrix<StorageIndex, Eigen::Dynamic, 1> StorageIndexVector;
// typedef const Eigen::Ref<const StorageIndexVector>
// RefStorageIndexVector;
StorageIndexVector indices =
bp::extract<StorageIndexVector>(obj.attr("indices"));
StorageIndexVector indptr =
bp::extract<StorageIndexVector>(obj.attr("indptr"));
const Eigen::Index m = bp::extract<Eigen::Index>(shape[0]),
n = bp::extract<Eigen::Index>(shape[1]),
nnz = bp::extract<Eigen::Index>(obj.attr("nnz"));
// Handle the specific case of the null matrix
Scalar *data_ptr = nullptr;
StorageIndex *indices_ptr = nullptr;
if (nnz > 0) {
data_ptr = data.data();
indices_ptr = indices.data();
}
MapMatOrRefType sparse_map(m, n, nnz, indptr.data(), indices_ptr, data_ptr);
new (raw_ptr) MatOrRefType(sparse_map);
}
memory->convertible = storage->storage.bytes;
}
template <typename SparseMatrixType>
void eigen_from_py_impl<SparseMatrixType,
Eigen::SparseMatrixBase<SparseMatrixType> >::
construct(PyObject *pyObj,
bp::converter::rvalue_from_python_stage1_data *memory) {
eigen_sparse_matrix_from_py_construct<SparseMatrixType>(pyObj, memory);
}
template <typename SparseMatrixType>
void eigen_from_py_impl<
SparseMatrixType,
Eigen::SparseMatrixBase<SparseMatrixType> >::registration() {
bp::converter::registry::push_back(
reinterpret_cast<void *(*)(_object *)>(&eigen_from_py_impl::convertible),
&eigen_from_py_impl::construct, bp::type_id<SparseMatrixType>()
#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
,
&eigenpy::expected_pytype_for_arg<SparseMatrixType>::get_pytype
#endif
);
}
template <typename SparseMatrixType>
struct eigen_from_py_converter_impl<
SparseMatrixType, Eigen::SparseMatrixBase<SparseMatrixType> > {
static void registration() {
EigenFromPy<SparseMatrixType>::registration();
// Add conversion to Eigen::SparseMatrixBase<SparseMatrixType>
typedef Eigen::SparseMatrixBase<SparseMatrixType> SparseMatrixBase;
EigenFromPy<SparseMatrixBase>::registration();
// // Add conversion to Eigen::Ref<SparseMatrixType>
// typedef Eigen::Ref<SparseMatrixType> RefType;
// EigenFromPy<SparseMatrixType>::registration();
//
// // Add conversion to Eigen::Ref<const SparseMatrixType>
// typedef const Eigen::Ref<const SparseMatrixType> ConstRefType;
// EigenFromPy<ConstRefType>::registration();
}
};
template <typename SparseMatrixType>
struct EigenFromPy<Eigen::SparseMatrixBase<SparseMatrixType> >
: EigenFromPy<SparseMatrixType> {
typedef EigenFromPy<SparseMatrixType> EigenFromPyDerived;
typedef Eigen::SparseMatrixBase<SparseMatrixType> Base;
static void registration() {
bp::converter::registry::push_back(
reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
&EigenFromPy::construct, bp::type_id<Base>()
#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
,
&eigenpy::expected_pytype_for_arg<SparseMatrixType>::get_pytype
#endif
);
}
};
//
// template <typename TensorType>
// struct EigenFromPy<Eigen::TensorRef<TensorType> > {
// typedef Eigen::TensorRef<TensorType> RefType;
// typedef typename TensorType::Scalar Scalar;
//
// /// \brief Determine if pyObj can be converted into a MatType object
// static void *convertible(PyObject *pyObj) {
// if (!call_PyArray_Check(pyObj)) return 0;
// PyArrayObject *pyArray = reinterpret_cast<PyArrayObject *>(pyObj);
// if (!PyArray_ISWRITEABLE(pyArray)) return 0;
// return EigenFromPy<TensorType>::convertible(pyObj);
// }
//
// static void registration() {
// bp::converter::registry::push_back(
// reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
// &eigen_from_py_construct<RefType>, bp::type_id<RefType>()
// #ifndef BOOST_PYTHON_NO_PY_SIGNATURES
// ,
// &eigenpy::expected_pytype_for_arg<TensorType>::get_pytype
// #endif
// );
// }
//};
// template <typename TensorType>
// struct EigenFromPy<const Eigen::TensorRef<const TensorType> > {
// typedef const Eigen::TensorRef<const TensorType> ConstRefType;
// typedef typename TensorType::Scalar Scalar;
//
// /// \brief Determine if pyObj can be converted into a MatType object
// static void *convertible(PyObject *pyObj) {
// return EigenFromPy<TensorType>::convertible(pyObj);
// }
//
// static void registration() {
// bp::converter::registry::push_back(
// reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
// &eigen_from_py_construct<ConstRefType>, bp::type_id<ConstRefType>()
// #ifndef BOOST_PYTHON_NO_PY_SIGNATURES
// ,
// &eigenpy::expected_pytype_for_arg<TensorType>::get_pytype
// #endif
// );
// }
// };
} // namespace eigenpy
#endif // __eigenpy_sparse_eigen_from_python_hpp__
include/eigenpy/std-array.hpp 0 → 100644
View file @ 7102d834
///
/// Copyright (c) 2023-2024 CNRS INRIA
///
#ifndef __eigenpy_utils_std_array_hpp__
#define __eigenpy_utils_std_array_hpp__
#include <boost/python/suite/indexing/indexing_suite.hpp>
#include "eigenpy/std-vector.hpp"
#include <array>
namespace eigenpy {
template <typename Container, bool NoProxy, class SliceAllocator,
class DerivedPolicies>
class array_indexing_suite;
namespace details {
template <typename Container, bool NoProxy, class SliceAllocator>
class final_array_derived_policies
: public array_indexing_suite<
Container, NoProxy, SliceAllocator,
final_array_derived_policies<Container, NoProxy, SliceAllocator> > {};
} // namespace details
template <typename Container, bool NoProxy = false,
class SliceAllocator = std::allocator<typename Container::value_type>,
class DerivedPolicies = details::final_array_derived_policies<
Container, NoProxy, SliceAllocator> >
class array_indexing_suite
: public bp::vector_indexing_suite<Container, NoProxy, DerivedPolicies> {
public:
typedef typename Container::value_type data_type;
typedef typename Container::value_type key_type;
typedef typename Container::size_type index_type;
typedef typename Container::size_type size_type;
typedef typename Container::difference_type difference_type;
typedef std::vector<data_type, SliceAllocator> slice_vector_type;
static constexpr std::size_t Size = std::tuple_size<Container>{};
template <class Class>
static void extension_def(Class &) {}
// throws exception
static void delete_item(Container &, index_type) {
PyErr_SetString(PyExc_NotImplementedError,
"Cannot delete item from std::array type.");
bp::throw_error_already_set();
}
// throws exception
static void delete_slice(Container &, index_type, index_type) {
PyErr_SetString(PyExc_NotImplementedError,
"Cannot delete slice from std::array type.");
bp::throw_error_already_set();
}
static void set_slice(Container &container, index_type from, index_type to,
data_type const &v) {
if (from >= to) {
PyErr_SetString(PyExc_NotImplementedError,
"Setting this slice would insert into an std::array, "
"which is not supported.");
bp::throw_error_already_set();
} else {
std::fill(container.begin() + from, container.begin() + to, v);
}
}
template <class Iter>
static void set_slice(Container &container, index_type from, index_type to,
Iter first, Iter last) {
if (from >= to) {
PyErr_SetString(PyExc_NotImplementedError,
"Setting this slice would insert into an std::array, "
"which is not supported.");
bp::throw_error_already_set();
} else {
if (long(to - from) == std::distance(first, last)) {
std::copy(first, last, container.begin() + from);
} else {
PyErr_SetString(PyExc_NotImplementedError,
"Size of std::array slice and size of right-hand side "
"iterator are incompatible.");
bp::throw_error_already_set();
}
}
}
static bp::object get_slice(Container &container, index_type from,
index_type to) {
if (from > to) return bp::object(slice_vector_type());
slice_vector_type out;
for (size_t i = from; i < to; i++) {
out.push_back(container[i]);
}
return bp::object(std::move(out));
}
};
/// \brief Expose an std::array (a C++11 fixed-size array) from a given type
/// \tparam array_type std::array type to expose
/// \tparam NoProxy When set to false, the elements will be copied when
/// returned to Python.
/// \tparam SliceAllocator Allocator type to use for slices of std::array type
/// accessed using e.g. __getitem__[0:4] in Python. These slices are returned as
/// std::vector (dynamic size).
template <typename array_type, bool NoProxy = false,
class SliceAllocator =
std::allocator<typename array_type::value_type> >
struct StdArrayPythonVisitor {
typedef typename array_type::value_type value_type;
static ::boost::python::list tolist(array_type &self, const bool deep_copy) {
return details::build_list<array_type, NoProxy>::run(self, deep_copy);
}
static void expose(const std::string &class_name,
const std::string &doc_string = "") {
expose(class_name, doc_string, EmptyPythonVisitor());
}
template <typename DerivedVisitor>
static void expose(const std::string &class_name,
const bp::def_visitor<DerivedVisitor> &visitor) {
expose(class_name, "", visitor);
}
template <typename DerivedVisitor>
static void expose(const std::string &class_name,
const std::string &doc_string,
const bp::def_visitor<DerivedVisitor> &visitor) {
if (!register_symbolic_link_to_registered_type<array_type>()) {
bp::class_<array_type> cl(class_name.c_str(), doc_string.c_str());
cl.def(bp::init<const array_type &>(bp::args("self", "other"),
"Copy constructor"));
cl.def(IdVisitor<array_type>());
array_indexing_suite<array_type, NoProxy, SliceAllocator> indexing_suite;
cl.def(indexing_suite)
.def(visitor)
.def("tolist", tolist,
(bp::arg("self"), bp::arg("deep_copy") = false),
"Returns the std::array as a Python list.");
}
}
};
/// Exposes std::array<MatrixType, Size>
template <typename MatrixType, std::size_t Size>
void exposeStdArrayEigenSpecificType(const char *name) {
std::ostringstream oss;
oss << "StdArr";
oss << Size << "_" << name;
typedef std::array<MatrixType, Size> array_type;
StdArrayPythonVisitor<array_type, false,
Eigen::aligned_allocator<MatrixType> >::
expose(oss.str(),
details::overload_base_get_item_for_std_vector<array_type>());
}
} // namespace eigenpy
#endif // ifndef __eigenpy_utils_std_array_hpp__
include/eigenpy/std-map.hpp 0 → 100644
View file @ 7102d834
/// Copyright (c) 2024, INRIA
///
#ifndef __eigenpy_std_map_hpp__
#define __eigenpy_std_map_hpp__
#include "eigenpy/map.hpp"
#include "eigenpy/deprecated.hpp"
#include <map>
namespace eigenpy {
template <typename Container>
using overload_base_get_item_for_std_map EIGENPY_DEPRECATED_MESSAGE(
"Use overload_base_get_item_for_map<> instead.") =
overload_base_get_item_for_map<Container>;
namespace details {
using ::eigenpy::overload_base_get_item_for_std_map;
} // namespace details
/**
* @brief Expose an std::map from a type given as template argument.
*
* @param[in] T Type to expose as std::map<T>.
* @param[in] Compare Type for the Compare in std::map<T,Compare,Allocator>.
* @param[in] Allocator Type for the Allocator in
* std::map<T,Compare,Allocator>.
* @param[in] NoProxy When set to false, the elements will be copied when
* returned to Python.
*/
template <class Key, class T, class Compare = std::less<Key>,
class Allocator = std::allocator<std::pair<const Key, T> >,
bool NoProxy = false>
struct StdMapPythonVisitor
: GenericMapVisitor<std::map<Key, T, Compare, Allocator>, NoProxy> {};
namespace python {
// fix previous mistake
using ::eigenpy::StdMapPythonVisitor;
} // namespace python
} // namespace eigenpy
#endif // ifndef __eigenpy_std_map_hpp__
include/eigenpy/std-pair.hpp 0 → 100644
View file @ 7102d834
//
// Copyright (c) 2023 INRIA
//
#ifndef __eigenpy_utils_std_pair_hpp__
#define __eigenpy_utils_std_pair_hpp__
#include <boost/python.hpp>
#include <utility>
namespace eigenpy {
template <typename pair_type>
struct StdPairConverter {
typedef typename pair_type::first_type T1;
typedef typename pair_type::second_type T2;
static PyObject* convert(const pair_type& pair) {
return boost::python::incref(
boost::python::make_tuple(pair.first, pair.second).ptr());
}
static void* convertible(PyObject* obj) {
if (!PyTuple_CheckExact(obj)) return 0;
if (PyTuple_Size(obj) != 2) return 0;
{
boost::python::tuple tuple(boost::python::borrowed(obj));
boost::python::extract<T1> elt1(tuple[0]);
if (!elt1.check()) return 0;
boost::python::extract<T2> elt2(tuple[1]);
if (!elt2.check()) return 0;
}
return obj;
}
static void construct(
PyObject* obj,
boost::python::converter::rvalue_from_python_stage1_data* memory) {
boost::python::tuple tuple(boost::python::borrowed(obj));
void* storage =
reinterpret_cast<
boost::python::converter::rvalue_from_python_storage<pair_type>*>(
reinterpret_cast<void*>(memory))
->storage.bytes;
new (storage) pair_type(boost::python::extract<T1>(tuple[0]),
boost::python::extract<T2>(tuple[1]));
memory->convertible = storage;
}
static PyTypeObject const* get_pytype() {
PyTypeObject const* py_type = &PyTuple_Type;
return py_type;
}
static void registration() {
boost::python::converter::registry::push_back(
&convertible, &construct, boost::python::type_id<pair_type>()
#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
,
get_pytype
#endif
);
boost::python::to_python_converter<pair_type, StdPairConverter, true>();
}
};
} // namespace eigenpy
#endif // ifndef __eigenpy_utils_std_pair_hpp__
include/eigenpy/std-unique-ptr.hpp 0 → 100644
View file @ 7102d834
//
// Copyright (c) 2024 INRIA
//
#ifndef __eigenpy_utils_std_unique_ptr_hpp__
#define __eigenpy_utils_std_unique_ptr_hpp__
#include "eigenpy/fwd.hpp"
#include "eigenpy/utils/traits.hpp"
#include "eigenpy/utils/python-compat.hpp"
#include <boost/python.hpp>
#include <memory>
#include <type_traits>
namespace eigenpy {
namespace details {
/// Transfer std::unique_ptr ownership to an owning holder
template <typename T>
typename std::enable_if<!is_python_primitive_type<T>::value, PyObject*>::type
unique_ptr_to_python(std::unique_ptr<T>&& x) {
typedef bp::objects::pointer_holder<std::unique_ptr<T>, T> holder_t;
if (!x) {
return bp::detail::none();
} else {
return bp::objects::make_ptr_instance<T, holder_t>::execute(x);
}
}
/// Convert and copy the primitive value to python
template <typename T>
typename std::enable_if<is_python_primitive_type<T>::value, PyObject*>::type
unique_ptr_to_python(std::unique_ptr<T>&& x) {
if (!x) {
return bp::detail::none();
} else {
return bp::to_python_value<const T&>()(*x);
}
}
/// std::unique_ptr keep the ownership but a reference to the std::unique_ptr
/// value is created
template <typename T>
typename std::enable_if<!is_python_primitive_type<T>::value, PyObject*>::type
internal_unique_ptr_to_python(std::unique_ptr<T>& x) {
if (!x) {
return bp::detail::none();
} else {
return bp::detail::make_reference_holder::execute(x.get());
}
}
/// Convert and copy the primitive value to python
template <typename T>
typename std::enable_if<is_python_primitive_type<T>::value, PyObject*>::type
internal_unique_ptr_to_python(std::unique_ptr<T>& x) {
if (!x) {
return bp::detail::none();
} else {
return bp::to_python_value<const T&>()(*x);
}
}
/// result_converter of StdUniquePtrCallPolicies
struct StdUniquePtrResultConverter {
template <typename T>
struct apply {
struct type {
typedef typename T::element_type element_type;
PyObject* operator()(T&& x) const {
return unique_ptr_to_python(std::forward<T>(x));
}
#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
PyTypeObject const* get_pytype() const {
return bp::to_python_value<const element_type&>().get_pytype();
}
#endif
};
};
};
/// result_converter of ReturnInternalStdUniquePtr
struct InternalStdUniquePtrConverter {
template <typename T>
struct apply {
struct type {
typedef typename remove_cvref<T>::type::element_type element_type;
PyObject* operator()(T x) const {
return internal_unique_ptr_to_python(x);
}
#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
PyTypeObject const* get_pytype() const {
return bp::to_python_value<const element_type&>().get_pytype();
}
#endif
};
};
};
} // namespace details
/// CallPolicies to get std::unique_ptr value from a function
/// that return an std::unique_ptr.
/// If the object inside the std::unique_ptr is a class or an union
/// it will be moved. In other case, it will be copied.
struct StdUniquePtrCallPolicies : bp::default_call_policies {
typedef details::StdUniquePtrResultConverter result_converter;
};
/// Variant of \see bp::return_internal_reference that extract std::unique_ptr
/// content reference before converting it into a PyObject
struct ReturnInternalStdUniquePtr : bp::return_internal_reference<> {
typedef details::InternalStdUniquePtrConverter result_converter;
template <class ArgumentPackage>
static PyObject* postcall(ArgumentPackage const& args_, PyObject* result) {
// Don't run return_internal_reference postcall on primitive type
if (PyInt_Check(result) || PyBool_Check(result) || PyFloat_Check(result) ||
PyStr_Check(result) || PyComplex_Check(result)) {
return result;
}
return bp::return_internal_reference<>::postcall(args_, result);
}
};
} // namespace eigenpy
namespace boost {
namespace python {
/// Specialize to_python_value for std::unique_ptr
template <typename T>
struct to_python_value<const std::unique_ptr<T>&>
: eigenpy::details::StdUniquePtrResultConverter::apply<
std::unique_ptr<T> >::type {};
} // namespace python
} // namespace boost
#endif // ifndef __eigenpy_utils_std_unique_ptr_hpp__
include/eigenpy/std-vector.hpp 0 → 100644
View file @ 7102d834
///
/// Copyright (c) 2016-2024 CNRS INRIA
/// This file was taken from Pinocchio (header
/// <pinocchio/bindings/python/utils/std-vector.hpp>)
///
#ifndef __eigenpy_utils_std_vector_hpp__
#define __eigenpy_utils_std_vector_hpp__
#include <boost/mpl/if.hpp>
#include <boost/python.hpp>
#include <boost/python/stl_iterator.hpp>
#include <boost/python/suite/indexing/vector_indexing_suite.hpp>
#include <iterator>
#include <string>
#include <vector>
#include "eigenpy/eigenpy.hpp"
#include "eigenpy/config.hpp"
#include "eigenpy/copyable.hpp"
#include "eigenpy/eigen-to-python.hpp"
#include "eigenpy/pickle-vector.hpp"
#include "eigenpy/registration.hpp"
#include "eigenpy/utils/empty-visitor.hpp"
namespace eigenpy {
// Forward declaration
template <typename vector_type, bool NoProxy = false>
struct StdContainerFromPythonList;
namespace details {
/// \brief Check if a PyObject can be converted to an std::vector<T>.
template <typename T>
bool from_python_list(PyObject *obj_ptr, T *) {
// Check if it is a list
if (!PyList_Check(obj_ptr)) return false;
// Retrieve the underlying list
bp::object bp_obj(bp::handle<>(bp::borrowed(obj_ptr)));
bp::list bp_list(bp_obj);
bp::ssize_t list_size = bp::len(bp_list);
// Check if all the elements contained in the current vector is of type T
for (bp::ssize_t k = 0; k < list_size; ++k) {
bp::extract<T> elt(bp_list[k]);
if (!elt.check()) return false;
}
return true;
}
template <typename vector_type, bool NoProxy>
struct build_list {
static ::boost::python::list run(vector_type &vec, const bool deep_copy) {
if (deep_copy) return build_list<vector_type, true>::run(vec, true);
bp::list bp_list;
for (size_t k = 0; k < vec.size(); ++k) {
bp_list.append(boost::ref(vec[k]));
}
return bp_list;
}
};
template <typename vector_type>
struct build_list<vector_type, true> {
static ::boost::python::list run(vector_type &vec, const bool) {
typedef bp::iterator<vector_type> iterator;
return bp::list(iterator()(vec));
}
};
/// \brief Change the behavior of indexing (method __getitem__ in Python).
/// This is suitable for container of Eigen matrix objects if you want to mutate
/// them.
template <typename Container>
struct overload_base_get_item_for_std_vector
: public boost::python::def_visitor<
overload_base_get_item_for_std_vector<Container> > {
typedef typename Container::value_type value_type;
typedef typename Container::value_type data_type;
typedef size_t index_type;
template <class Class>
void visit(Class &cl) const {
cl.def("__getitem__", &base_get_item);
}
private:
static boost::python::object base_get_item(
boost::python::back_reference<Container &> container, PyObject *i_) {
index_type idx = convert_index(container.get(), i_);
typename Container::iterator i = container.get().begin();
std::advance(i, idx);
if (i == container.get().end()) {
PyErr_SetString(PyExc_KeyError, "Invalid index");
bp::throw_error_already_set();
}
typename bp::to_python_indirect<data_type &,
bp::detail::make_reference_holder>
convert;
return bp::object(bp::handle<>(convert(*i)));
}
static index_type convert_index(Container &container, PyObject *i_) {
bp::extract<long> i(i_);
if (i.check()) {
long index = i();
if (index < 0) index += (long)container.size();
if (index >= long(container.size()) || index < 0) {
PyErr_SetString(PyExc_IndexError, "Index out of range");
bp::throw_error_already_set();
}
return (index_type)index;
}
PyErr_SetString(PyExc_TypeError, "Invalid index type");
bp::throw_error_already_set();
return index_type();
}
};
} // namespace details
} // namespace eigenpy
namespace boost {
namespace python {
template <typename MatrixType>
struct extract_to_eigen_ref
: converter::extract_rvalue<Eigen::Ref<MatrixType> > {
typedef Eigen::Ref<MatrixType> RefType;
protected:
typedef converter::extract_rvalue<RefType> base;
public:
typedef RefType result_type;
operator result_type() const { return (*this)(); }
extract_to_eigen_ref(PyObject *o) : base(o) {}
extract_to_eigen_ref(api::object const &o) : base(o.ptr()) {}
};
/// \brief Specialization of the boost::python::extract struct for references to
/// Eigen matrix objects.
template <typename Scalar, int Rows, int Cols, int Options, int MaxRows,
int MaxCols>
struct extract<Eigen::Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> &>
: extract_to_eigen_ref<
Eigen::Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > {
typedef Eigen::Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols>
MatrixType;
typedef extract_to_eigen_ref<MatrixType> base;
extract(PyObject *o) : base(o) {}
extract(api::object const &o) : base(o.ptr()) {}
};
template <typename Derived>
struct extract<Eigen::MatrixBase<Derived> &>
: extract_to_eigen_ref<Eigen::MatrixBase<Derived> > {
typedef Eigen::MatrixBase<Derived> MatrixType;
typedef extract_to_eigen_ref<MatrixType> base;
extract(PyObject *o) : base(o) {}
extract(api::object const &o) : base(o.ptr()) {}
};
template <typename Derived>
struct extract<Eigen::RefBase<Derived> &>
: extract_to_eigen_ref<Eigen::RefBase<Derived> > {
typedef Eigen::RefBase<Derived> MatrixType;
typedef extract_to_eigen_ref<MatrixType> base;
extract(PyObject *o) : base(o) {}
extract(api::object const &o) : base(o.ptr()) {}
};
namespace converter {
template <typename Type, class Allocator>
struct reference_arg_from_python<std::vector<Type, Allocator> &>
: arg_lvalue_from_python_base {
typedef std::vector<Type, Allocator> vector_type;
typedef vector_type &ref_vector_type;
typedef ref_vector_type result_type;
typedef extract<Type &> extract_type;
reference_arg_from_python(PyObject *py_obj)
: arg_lvalue_from_python_base(converter::get_lvalue_from_python(
py_obj, registered<vector_type>::converters)),
m_data(NULL),
m_source(py_obj),
vec_ptr(NULL) {
if (result() != 0) // we have found a lvalue converter
return;
// Check if py_obj is a py_list, which can then be converted to an
// std::vector
bool is_convertible =
::eigenpy::details::from_python_list(py_obj, (Type *)(0));
if (!is_convertible) return;
typedef ::eigenpy::StdContainerFromPythonList<vector_type> Constructor;
Constructor::construct(py_obj, &m_data.stage1);
void *&m_result = const_cast<void *&>(result());
m_result = m_data.stage1.convertible;
vec_ptr = reinterpret_cast<vector_type *>(m_data.storage.bytes);
}
result_type operator()() const {
return ::boost::python::detail::void_ptr_to_reference(result(),
(result_type(*)())0);
}
~reference_arg_from_python() {
if (m_data.stage1.convertible == m_data.storage.bytes) {
// Copy back the reference
const vector_type &vec = *vec_ptr;
list bp_list(handle<>(borrowed(m_source)));
for (size_t i = 0; i < vec.size(); ++i) {
typename extract_type::result_type elt = extract_type(bp_list[i]);
elt = vec[i];
}
}
}
private:
rvalue_from_python_data<ref_vector_type> m_data;
PyObject *m_source;
vector_type *vec_ptr;
};
} // namespace converter
} // namespace python
} // namespace boost
namespace eigenpy {
namespace details {
/// Defines traits for the container, used in \struct StdContainerFromPythonList
template <class Container>
struct container_traits {
// default behavior expects allocators
typedef typename Container::allocator_type Allocator;
};
template <typename _Tp, std::size_t Size>
struct container_traits<std::array<_Tp, Size> > {
typedef void Allocator;
};
}; // namespace details
///
/// \brief Register the conversion from a Python list to a std::vector
///
/// \tparam vector_type A std container (e.g. std::vector or std::list)
///
template <typename vector_type, bool NoProxy>
struct StdContainerFromPythonList {
typedef typename vector_type::value_type T;
typedef typename details::container_traits<vector_type>::Allocator Allocator;
/// \brief Check if obj_ptr can be converted
static void *convertible(PyObject *obj_ptr) {
namespace bp = boost::python;
// Check if it is a list
if (!PyList_Check(obj_ptr)) return 0;
// Retrieve the underlying list
bp::object bp_obj(bp::handle<>(bp::borrowed(obj_ptr)));
bp::list bp_list(bp_obj);
bp::ssize_t list_size = bp::len(bp_list);
// Check if all the elements contained in the current vector is of type T
for (bp::ssize_t k = 0; k < list_size; ++k) {
bp::extract<T> elt(bp_list[k]);
if (!elt.check()) return 0;
}
return obj_ptr;
}
/// \brief Allocate the std::vector and fill it with the element contained in
/// the list
static void construct(
PyObject *obj_ptr,
boost::python::converter::rvalue_from_python_stage1_data *memory) {
// Extract the list
bp::object bp_obj(bp::handle<>(bp::borrowed(obj_ptr)));
bp::list bp_list(bp_obj);
void *storage =
reinterpret_cast<
bp::converter::rvalue_from_python_storage<vector_type> *>(
reinterpret_cast<void *>(memory))
->storage.bytes;
typedef bp::stl_input_iterator<T> iterator;
// Build the std::vector
new (storage) vector_type(iterator(bp_list), iterator());
// Validate the construction
memory->convertible = storage;
}
static void register_converter() {
::boost::python::converter::registry::push_back(
&convertible, &construct, ::boost::python::type_id<vector_type>());
}
static ::boost::python::list tolist(vector_type &self,
const bool deep_copy = false) {
return details::build_list<vector_type, NoProxy>::run(self, deep_copy);
}
};
namespace internal {
template <typename T,
bool has_operator_equal_value =
std::is_base_of<std::true_type, has_operator_equal<T> >::value>
struct contains_algo;
template <typename T>
struct contains_algo<T, true> {
template <class Container, typename key_type>
static bool run(const Container &container, key_type const &key) {
return std::find(container.begin(), container.end(), key) !=
container.end();
}
};
template <typename T>
struct contains_algo<T, false> {
template <class Container, typename key_type>
static bool run(const Container &container, key_type const &key) {
for (size_t k = 0; k < container.size(); ++k) {
if (&container[k] == &key) return true;
}
return false;
}
};
template <class Container, bool NoProxy>
struct contains_vector_derived_policies
: public ::boost::python::vector_indexing_suite<
Container, NoProxy,
contains_vector_derived_policies<Container, NoProxy> > {
typedef typename Container::value_type key_type;
static bool contains(Container &container, key_type const &key) {
return contains_algo<key_type>::run(container, key);
}
};
///
/// \brief Add standard method to a std::vector.
/// \tparam NoProxy When set to false, the elements will be copied when
/// returned to Python.
///
template <typename Container, bool NoProxy, typename CoVisitor>
struct ExposeStdMethodToStdVector
: public boost::python::def_visitor<
ExposeStdMethodToStdVector<Container, NoProxy, CoVisitor> > {
typedef StdContainerFromPythonList<Container, NoProxy>
FromPythonListConverter;
ExposeStdMethodToStdVector(const CoVisitor &co_visitor)
: m_co_visitor(co_visitor) {}
template <class Class>
void visit(Class &cl) const {
cl.def(m_co_visitor)
.def("tolist", &FromPythonListConverter::tolist,
(bp::arg("self"), bp::arg("deep_copy") = false),
"Returns the std::vector as a Python list.")
.def("reserve", &Container::reserve,
(bp::arg("self"), bp::arg("new_cap")),
"Increase the capacity of the vector to a value that's greater "
"or equal to new_cap.")
.def(CopyableVisitor<Container>());
}
const CoVisitor &m_co_visitor;
};
/// Helper to ease ExposeStdMethodToStdVector construction
template <typename Container, bool NoProxy, typename CoVisitor>
static ExposeStdMethodToStdVector<Container, NoProxy, CoVisitor>
createExposeStdMethodToStdVector(const CoVisitor &co_visitor) {
return ExposeStdMethodToStdVector<Container, NoProxy, CoVisitor>(co_visitor);
}
} // namespace internal
namespace internal {
template <typename vector_type, bool T_picklable = false>
struct def_pickle_std_vector {
static void run(bp::class_<vector_type> &) {}
};
template <typename vector_type>
struct def_pickle_std_vector<vector_type, true> {
static void run(bp::class_<vector_type> &cl) {
cl.def_pickle(PickleVector<vector_type>());
}
};
} // namespace internal
///
/// \brief Expose an std::vector from a type given as template argument.
/// \tparam vector_type std::vector type to expose
/// \tparam NoProxy When set to false, the elements will be copied when
/// returned to Python.
/// \tparam EnableFromPythonListConverter Enables the
/// conversion from a Python list to a std::vector<T,Allocator>
///
template <class vector_type, bool NoProxy = false,
bool EnableFromPythonListConverter = true, bool pickable = true>
struct StdVectorPythonVisitor {
typedef typename vector_type::value_type value_type;
typedef StdContainerFromPythonList<vector_type, NoProxy>
FromPythonListConverter;
static void expose(const std::string &class_name,
const std::string &doc_string = "") {
expose(class_name, doc_string, EmptyPythonVisitor());
}
template <typename DerivedVisitor>
static void expose(const std::string &class_name,
const bp::def_visitor<DerivedVisitor> &visitor) {
expose(class_name, "", visitor);
}
template <typename DerivedVisitor>
static void expose(const std::string &class_name,
const std::string &doc_string,
const bp::def_visitor<DerivedVisitor> &visitor) {
// Apply visitor on already registered type or if type is not already
// registered, we define and apply the visitor on it
auto add_std_visitor =
internal::createExposeStdMethodToStdVector<vector_type, NoProxy>(
visitor);
if (!register_symbolic_link_to_registered_type<vector_type>(
add_std_visitor)) {
bp::class_<vector_type> cl(class_name.c_str(), doc_string.c_str());
cl.def(IdVisitor<vector_type>());
// Standard vector indexing definition
boost::python::vector_indexing_suite<
vector_type, NoProxy,
internal::contains_vector_derived_policies<vector_type, NoProxy> >
vector_indexing;
cl.def(bp::init<size_t, const value_type &>(
bp::args("self", "size", "value"),
"Constructor from a given size and a given value."))
.def(bp::init<const vector_type &>(bp::args("self", "other"),
"Copy constructor"))
.def(vector_indexing)
.def(add_std_visitor);
internal::def_pickle_std_vector<vector_type, pickable>::run(cl);
}
if (EnableFromPythonListConverter) {
// Register conversion
FromPythonListConverter::register_converter();
}
}
};
/**
* Expose std::vector for given matrix or vector sizes.
*/
void EIGENPY_DLLAPI exposeStdVector();
template <typename MatType, typename Alloc = Eigen::aligned_allocator<MatType> >
void exposeStdVectorEigenSpecificType(const char *name) {
typedef std::vector<MatType, Alloc> VecMatType;
std::string full_name = "StdVec_";
full_name += name;
StdVectorPythonVisitor<VecMatType>::expose(
full_name.c_str(),
details::overload_base_get_item_for_std_vector<VecMatType>());
}
} // namespace eigenpy
#endif // ifndef __eigenpy_utils_std_vector_hpp__
include/eigenpy/stride.hpp
View file @ 7102d834
/* /*
* Copyright 2014-2019, CNRS * Copyright 2014-2019, CNRS
* Copyright 2018-2019, INRIA * Copyright 2018-2023, INRIA
*/ */
#ifndef __eigenpy_stride_hpp__ #ifndef __eigenpy_stride_hpp__
#define __eigenpy_stride_hpp__ #define __eigenpy_stride_hpp__
#include <Eigen/Core> #include <eigenpy/fwd.hpp>
namespace eigenpy namespace eigenpy {
{
template<typename MatType, int InnerStride = Eigen::Dynamic, int OuterStride = Eigen::Dynamic, bool IsVectorAtCompileTime = MatType::IsVectorAtCompileTime> template <typename MatType, int InnerStride, int OuterStride,
struct StrideType bool IsVectorAtCompileTime = MatType::IsVectorAtCompileTime>
{ struct stride_type_matrix {
typedef Eigen::Stride<OuterStride,InnerStride> type; typedef Eigen::Stride<OuterStride, InnerStride> type;
}; };
template<typename MatType, int InnerStride, int OuterStride> template <typename MatType, int InnerStride, int OuterStride>
struct StrideType<MatType,InnerStride,OuterStride,true> struct stride_type_matrix<MatType, InnerStride, OuterStride, true> {
{ typedef Eigen::InnerStride<InnerStride> type;
typedef Eigen::InnerStride<InnerStride> type; };
};
template <typename EigenType, int InnerStride, int OuterStride,
} typename BaseType = typename get_eigen_base_type<EigenType>::type>
struct stride_type;
#endif // ifndef __eigenpy_stride_hpp__
template <typename MatrixType, int InnerStride, int OuterStride>
struct stride_type<MatrixType, InnerStride, OuterStride,
Eigen::MatrixBase<MatrixType> > {
typedef
typename stride_type_matrix<MatrixType, InnerStride, OuterStride>::type
type;
};
template <typename MatrixType, int InnerStride, int OuterStride>
struct stride_type<const MatrixType, InnerStride, OuterStride,
const Eigen::MatrixBase<MatrixType> > {
typedef typename stride_type_matrix<const MatrixType, InnerStride,
OuterStride>::type type;
};
#ifdef EIGENPY_WITH_TENSOR_SUPPORT
template <typename TensorType, int InnerStride, int OuterStride>
struct stride_type<TensorType, InnerStride, OuterStride,
Eigen::TensorBase<TensorType> > {
typedef Eigen::Stride<OuterStride, InnerStride> type;
};
template <typename TensorType, int InnerStride, int OuterStride>
struct stride_type<const TensorType, InnerStride, OuterStride,
const Eigen::TensorBase<TensorType> > {
typedef Eigen::Stride<OuterStride, InnerStride> type;
};
#endif
template <typename EigenType, int InnerStride = Eigen::Dynamic,
int OuterStride = Eigen::Dynamic>
struct StrideType {
typedef typename stride_type<EigenType, InnerStride, OuterStride>::type type;
};
} // namespace eigenpy
#endif // ifndef __eigenpy_stride_hpp__
include/eigenpy/swig.hpp
View file @ 7102d834
...@@ -7,27 +7,21 @@ ...@@ -7,27 +7,21 @@
#include "eigenpy/fwd.hpp" #include "eigenpy/fwd.hpp"
namespace eigenpy namespace eigenpy {
{ struct PySwigObject {
struct PySwigObject PyObject_HEAD void* ptr;
{ const char* desc;
PyObject_HEAD };
void * ptr;
const char * desc;
};
inline PySwigObject * get_PySwigObject(PyObject * pyObj) inline PySwigObject* get_PySwigObject(PyObject* pyObj) {
{ if (!PyObject_HasAttrString(pyObj, "this")) return NULL;
if(!PyObject_HasAttrString(pyObj,"this"))
return NULL; PyObject* this_ptr = PyObject_GetAttrString(pyObj, "this");
if (this_ptr == NULL) return NULL;
PyObject * this_ptr = PyObject_GetAttrString(pyObj,"this"); PySwigObject* swig_obj = reinterpret_cast<PySwigObject*>(this_ptr);
if(this_ptr == NULL)
return NULL; return swig_obj;
PySwigObject * swig_obj = reinterpret_cast<PySwigObject*>(this_ptr);
return swig_obj;
}
} }
} // namespace eigenpy
#endif // ifndef __eigenpy_swig_hpp__ #endif // ifndef __eigenpy_swig_hpp__
include/eigenpy/tensor/eigen-from-python.hpp 0 → 100644
View file @ 7102d834
//
// Copyright (c) 2023 INRIA
//
#ifndef __eigenpy_tensor_eigen_from_python_hpp__
#define __eigenpy_tensor_eigen_from_python_hpp__
#include "eigenpy/fwd.hpp"
#include "eigenpy/eigen-allocator.hpp"
#include "eigenpy/numpy-type.hpp"
#include "eigenpy/scalar-conversion.hpp"
namespace eigenpy {
template <typename TensorType>
struct expected_pytype_for_arg<TensorType, Eigen::TensorBase<TensorType> > {
static PyTypeObject const *get_pytype() {
PyTypeObject const *py_type = eigenpy::getPyArrayType();
return py_type;
}
};
} // namespace eigenpy
namespace boost {
namespace python {
namespace converter {
template <typename Scalar, int Rank, int Options, typename IndexType>
struct expected_pytype_for_arg<Eigen::Tensor<Scalar, Rank, Options, IndexType> >
: eigenpy::expected_pytype_for_arg<
Eigen::Tensor<Scalar, Rank, Options, IndexType> > {};
template <typename Scalar, int Rank, int Options, typename IndexType>
struct rvalue_from_python_data<
Eigen::Tensor<Scalar, Rank, Options, IndexType> const &>
: ::eigenpy::rvalue_from_python_data<
Eigen::Tensor<Scalar, Rank, Options, IndexType> const &> {
typedef Eigen::Tensor<Scalar, Rank, Options, IndexType> T;
EIGENPY_RVALUE_FROM_PYTHON_DATA_INIT(T const &)
};
template <typename Derived>
struct rvalue_from_python_data<Eigen::TensorBase<Derived> const &>
: ::eigenpy::rvalue_from_python_data<Derived const &> {
EIGENPY_RVALUE_FROM_PYTHON_DATA_INIT(Derived const &)
};
} // namespace converter
} // namespace python
} // namespace boost
namespace boost {
namespace python {
namespace detail {
template <typename TensorType>
struct referent_storage<Eigen::TensorRef<TensorType> &> {
typedef Eigen::TensorRef<TensorType> RefType;
typedef ::eigenpy::details::referent_storage_eigen_ref<RefType> StorageType;
typedef typename ::eigenpy::aligned_storage<
referent_size<StorageType &>::value>::type type;
};
template <typename TensorType>
struct referent_storage<const Eigen::TensorRef<const TensorType> &> {
typedef Eigen::TensorRef<const TensorType> RefType;
typedef ::eigenpy::details::referent_storage_eigen_ref<RefType> StorageType;
typedef typename ::eigenpy::aligned_storage<
referent_size<StorageType &>::value>::type type;
};
} // namespace detail
} // namespace python
} // namespace boost
namespace eigenpy {
template <typename TensorType>
struct eigen_from_py_impl<TensorType, Eigen::TensorBase<TensorType> > {
typedef typename TensorType::Scalar Scalar;
/// \brief Determine if pyObj can be converted into a MatType object
static void *convertible(PyObject *pyObj);
/// \brief Allocate memory and copy pyObj in the new storage
static void construct(PyObject *pyObj,
bp::converter::rvalue_from_python_stage1_data *memory);
static void registration();
};
template <typename TensorType>
void *
eigen_from_py_impl<TensorType, Eigen::TensorBase<TensorType> >::convertible(
PyObject *pyObj) {
if (!call_PyArray_Check(reinterpret_cast<PyObject *>(pyObj))) return 0;
typedef typename Eigen::internal::traits<TensorType>::Index Index;
static const Index NumIndices = TensorType::NumIndices;
PyArrayObject *pyArray = reinterpret_cast<PyArrayObject *>(pyObj);
if (!np_type_is_convertible_into_scalar<Scalar>(
EIGENPY_GET_PY_ARRAY_TYPE(pyArray)))
return 0;
if (!(PyArray_NDIM(pyArray) == NumIndices || NumIndices == Eigen::Dynamic))
return 0;
#ifdef NPY_1_8_API_VERSION
if (!(PyArray_FLAGS(pyArray)))
#else
if (!(PyArray_FLAGS(pyArray) & NPY_ALIGNED))
#endif
{
return 0;
}
return pyArray;
}
template <typename TensorType>
void eigen_from_py_impl<TensorType, Eigen::TensorBase<TensorType> >::construct(
PyObject *pyObj, bp::converter::rvalue_from_python_stage1_data *memory) {
eigen_from_py_construct<TensorType>(pyObj, memory);
}
template <typename TensorType>
void eigen_from_py_impl<TensorType,
Eigen::TensorBase<TensorType> >::registration() {
bp::converter::registry::push_back(
reinterpret_cast<void *(*)(_object *)>(&eigen_from_py_impl::convertible),
&eigen_from_py_impl::construct, bp::type_id<TensorType>()
#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
,
&eigenpy::expected_pytype_for_arg<TensorType>::get_pytype
#endif
);
}
template <typename TensorType>
struct eigen_from_py_converter_impl<TensorType,
Eigen::TensorBase<TensorType> > {
static void registration() {
EigenFromPy<TensorType>::registration();
// Add conversion to Eigen::TensorBase<TensorType>
typedef Eigen::TensorBase<TensorType> TensorBase;
EigenFromPy<TensorBase>::registration();
// Add conversion to Eigen::TensorRef<TensorType>
typedef Eigen::TensorRef<TensorType> RefType;
EigenFromPy<RefType>::registration();
// Add conversion to Eigen::TensorRef<const TensorType>
typedef const Eigen::TensorRef<const TensorType> ConstRefType;
EigenFromPy<ConstRefType>::registration();
}
};
template <typename TensorType>
struct EigenFromPy<Eigen::TensorBase<TensorType> > : EigenFromPy<TensorType> {
typedef EigenFromPy<TensorType> EigenFromPyDerived;
typedef Eigen::TensorBase<TensorType> Base;
static void registration() {
bp::converter::registry::push_back(
reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
&EigenFromPy::construct, bp::type_id<Base>()
#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
,
&eigenpy::expected_pytype_for_arg<TensorType>::get_pytype
#endif
);
}
};
template <typename TensorType>
struct EigenFromPy<Eigen::TensorRef<TensorType> > {
typedef Eigen::TensorRef<TensorType> RefType;
typedef typename TensorType::Scalar Scalar;
/// \brief Determine if pyObj can be converted into a MatType object
static void *convertible(PyObject *pyObj) {
if (!call_PyArray_Check(pyObj)) return 0;
PyArrayObject *pyArray = reinterpret_cast<PyArrayObject *>(pyObj);
if (!PyArray_ISWRITEABLE(pyArray)) return 0;
return EigenFromPy<TensorType>::convertible(pyObj);
}
static void registration() {
bp::converter::registry::push_back(
reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
&eigen_from_py_construct<RefType>, bp::type_id<RefType>()
#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
,
&eigenpy::expected_pytype_for_arg<TensorType>::get_pytype
#endif
);
}
};
template <typename TensorType>
struct EigenFromPy<const Eigen::TensorRef<const TensorType> > {
typedef const Eigen::TensorRef<const TensorType> ConstRefType;
typedef typename TensorType::Scalar Scalar;
/// \brief Determine if pyObj can be converted into a MatType object
static void *convertible(PyObject *pyObj) {
return EigenFromPy<TensorType>::convertible(pyObj);
}
static void registration() {
bp::converter::registry::push_back(
reinterpret_cast<void *(*)(_object *)>(&EigenFromPy::convertible),
&eigen_from_py_construct<ConstRefType>, bp::type_id<ConstRefType>()
#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
,
&eigenpy::expected_pytype_for_arg<TensorType>::get_pytype
#endif
);
}
};
} // namespace eigenpy
#endif // __eigenpy_tensor_eigen_from_python_hpp__
include/eigenpy/type_info.hpp 0 → 100644
View file @ 7102d834
///
/// Copyright (c) 2024 INRIA
///
#ifndef __eigenpy_type_info_hpp__
#define __eigenpy_type_info_hpp__
#include "eigenpy/fwd.hpp"
#include <boost/type_index.hpp>
#include <typeinfo>
#include <typeindex>
namespace eigenpy {
template <typename T>
boost::typeindex::type_index type_info(const T& value) {
return boost::typeindex::type_id_runtime(value);
}
template <typename T>
void expose_boost_type_info() {
boost::python::def(
"type_info",
+[](const T& value) -> boost::typeindex::type_index {
return boost::typeindex::type_id_runtime(value);
},
bp::arg("value"),
"Returns information of the type of value as a "
"boost::typeindex::type_index (can work without RTTI).");
boost::python::def(
"boost_type_info",
+[](const T& value) -> boost::typeindex::type_index {
return boost::typeindex::type_id_runtime(value);
},
bp::arg("value"),
"Returns information of the type of value as a "
"boost::typeindex::type_index (can work without RTTI).");
}
template <typename T>
void expose_std_type_info() {
boost::python::def(
"std_type_info",
+[](const T& value) -> std::type_index { return typeid(value); },
bp::arg("value"),
"Returns information of the type of value as a std::type_index.");
}
///
/// \brief Add the Python method type_info to query information of a type.
///
template <class C>
struct TypeInfoVisitor : public bp::def_visitor<TypeInfoVisitor<C> > {
template <class PyClass>
void visit(PyClass& cl) const {
cl.def("type_info", &boost_type_info, bp::arg("self"),
"Queries information of the type of *this as a "
"boost::typeindex::type_index (can work without RTTI).");
cl.def("boost_type_info", &boost_type_info, bp::arg("self"),
"Queries information of the type of *this as a "
"boost::typeindex::type_index (can work without RTTI).");
cl.def("std_type_info", &std_type_info, bp::arg("self"),
"Queries information of the type of *this as a std::type_index.");
}
private:
static boost::typeindex::type_index boost_type_info(const C& self) {
return boost::typeindex::type_id_runtime(self);
}
static std::type_index std_type_info(const C& self) { return typeid(self); }
};
} // namespace eigenpy
#endif // __eigenpy_type_info_hpp__
include/eigenpy/ufunc.hpp
View file @ 7102d834
// //
// Copyright (c) 2020-2021 INRIA // Copyright (c) 2020-2025 INRIA
// code aptapted from https://github.com/numpy/numpy/blob/41977b24ae011a51f64faa75cb524c7350fdedd9/numpy/core/src/umath/_rational_tests.c.src // code aptapted from
// https://github.com/numpy/numpy/blob/41977b24ae011a51f64faa75cb524c7350fdedd9/numpy/core/src/umath/_rational_tests.c.src
// //
#ifndef __eigenpy_ufunc_hpp__ #ifndef __eigenpy_ufunc_hpp__
...@@ -8,257 +9,261 @@ ...@@ -8,257 +9,261 @@
#include "eigenpy/register.hpp" #include "eigenpy/register.hpp"
#include "eigenpy/user-type.hpp" #include "eigenpy/user-type.hpp"
#include "eigenpy/utils/python-compat.hpp"
namespace eigenpy {
namespace internal {
namespace eigenpy
{
namespace internal
{
#ifdef NPY_1_19_API_VERSION #ifdef NPY_1_19_API_VERSION
#define EIGENPY_NPY_CONST_UFUNC_ARG const #define EIGENPY_NPY_CONST_UFUNC_ARG const
#else #else
#define EIGENPY_NPY_CONST_UFUNC_ARG #define EIGENPY_NPY_CONST_UFUNC_ARG
#endif #endif
template<typename T> template <typename T>
void matrix_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps) void matrix_multiply(char **args, npy_intp const *dimensions,
{ npy_intp const *steps) {
/* pointers to data for input and output arrays */ /* pointers to data for input and output arrays */
char *ip1 = args[0]; char *ip1 = args[0];
char *ip2 = args[1]; char *ip2 = args[1];
char *op = args[2]; char *op = args[2];
/* lengths of core dimensions */ /* lengths of core dimensions */
npy_intp dm = dimensions[0]; npy_intp dm = dimensions[0];
npy_intp dn = dimensions[1]; npy_intp dn = dimensions[1];
npy_intp dp = dimensions[2]; npy_intp dp = dimensions[2];
/* striding over core dimensions */ /* striding over core dimensions */
npy_intp is1_m = steps[0]; npy_intp is1_m = steps[0];
npy_intp is1_n = steps[1]; npy_intp is1_n = steps[1];
npy_intp is2_n = steps[2]; npy_intp is2_n = steps[2];
npy_intp is2_p = steps[3]; npy_intp is2_p = steps[3];
npy_intp os_m = steps[4]; npy_intp os_m = steps[4];
npy_intp os_p = steps[5]; npy_intp os_p = steps[5];
/* core dimensions counters */ /* core dimensions counters */
npy_intp m, p; npy_intp m, p;
/* calculate dot product for each row/column vector pair */ /* calculate dot product for each row/column vector pair */
for (m = 0; m < dm; m++) for (m = 0; m < dm; m++) {
{ for (p = 0; p < dp; p++) {
for (p = 0; p < dp; p++) SpecialMethods<T>::dotfunc(ip1, is1_n, ip2, is2_n, op, dn, NULL);
{
SpecialMethods<T>::dotfunc(ip1, is1_n, ip2, is2_n, op, dn, NULL); /* advance to next column of 2nd input array and output array */
ip2 += is2_p;
/* advance to next column of 2nd input array and output array */ op += os_p;
ip2 += is2_p;
op += os_p;
}
/* reset to first column of 2nd input array and output array */
ip2 -= is2_p * p;
op -= os_p * p;
/* advance to next row of 1st input array and output array */
ip1 += is1_m;
op += os_m;
}
}
template<typename T>
void gufunc_matrix_multiply(char **args, npy_intp EIGENPY_NPY_CONST_UFUNC_ARG *dimensions,
npy_intp EIGENPY_NPY_CONST_UFUNC_ARG *steps, void *NPY_UNUSED(func))
{
/* outer dimensions counter */
npy_intp N_;
/* length of flattened outer dimensions */
npy_intp dN = dimensions[0];
/* striding over flattened outer dimensions for input and output arrays */
npy_intp s0 = steps[0];
npy_intp s1 = steps[1];
npy_intp s2 = steps[2];
/*
* loop through outer dimensions, performing matrix multiply on
* core dimensions for each loop
*/
for (N_ = 0; N_ < dN; N_++, args[0] += s0, args[1] += s1, args[2] += s2)
{
matrix_multiply<T>(args, dimensions+1, steps+3);
} }
/* reset to first column of 2nd input array and output array */
ip2 -= is2_p * p;
op -= os_p * p;
/* advance to next row of 1st input array and output array */
ip1 += is1_m;
op += os_m;
} }
#define EIGENPY_REGISTER_BINARY_OPERATOR(name,op) \
template<typename T1, typename T2, typename R> \
void binary_op_##name(char** args, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp * dimensions, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp * steps, void * /*data*/) \
{ \
npy_intp is0 = steps[0], is1 = steps[1], \
os = steps[2], n = *dimensions; \
char * i0 = args[0], *i1 = args[1], *o = args[2]; \
int k; \
for (k = 0; k < n; k++) \
{ \
T1 & x = *static_cast<T1*>(static_cast<void*>(i0)); \
T2 & y = *static_cast<T2*>(static_cast<void*>(i1)); \
R & res = *static_cast<R*>(static_cast<void*>(o)); \
res = x op y; \
i0 += is0; i1 += is1; o += os; \
} \
} \
\
template<typename T> \
void binary_op_##name(char** args, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp * dimensions, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp * steps, void * data) \
{ \
binary_op_##name<T,T,T>(args,dimensions,steps,data); \
}
EIGENPY_REGISTER_BINARY_OPERATOR(add,+)
EIGENPY_REGISTER_BINARY_OPERATOR(subtract,-)
EIGENPY_REGISTER_BINARY_OPERATOR(multiply,*)
EIGENPY_REGISTER_BINARY_OPERATOR(divide,/)
EIGENPY_REGISTER_BINARY_OPERATOR(equal,==)
EIGENPY_REGISTER_BINARY_OPERATOR(not_equal,!=)
EIGENPY_REGISTER_BINARY_OPERATOR(less,<)
EIGENPY_REGISTER_BINARY_OPERATOR(greater,>)
EIGENPY_REGISTER_BINARY_OPERATOR(less_equal,<=)
EIGENPY_REGISTER_BINARY_OPERATOR(greater_equal,>=)
#define EIGENPY_REGISTER_UNARY_OPERATOR(name,op) \
template<typename T, typename R> \
void unary_op_##name(char** args, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp * dimensions, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp * steps, void * /*data*/) \
{ \
npy_intp is = steps[0], \
os = steps[1], n = *dimensions; \
char * i = args[0], *o = args[1]; \
int k; \
for (k = 0; k < n; k++) \
{ \
T & x = *static_cast<T*>(static_cast<void*>(i)); \
R & res = *static_cast<R*>(static_cast<void*>(o)); \
res = op x; \
i += is; o += os; \
} \
} \
\
template<typename T> \
void unary_op_##name(char** args, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp * dimensions, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp * steps, void * data) \
{ \
unary_op_##name<T,T>(args,dimensions,steps,data); \
}
EIGENPY_REGISTER_UNARY_OPERATOR(negative,-)
} // namespace internal
#define EIGENPY_REGISTER_BINARY_UFUNC(name,code,T1,T2,R) { \
PyUFuncObject* ufunc = \
(PyUFuncObject*)PyObject_GetAttrString(numpy, #name); \
int _types[3] = { Register::getTypeCode<T1>(), Register::getTypeCode<T2>(), Register::getTypeCode<R>()}; \
if (!ufunc) { \
/*goto fail; \*/ \
} \
if (sizeof(_types)/sizeof(int)!=ufunc->nargs) { \
PyErr_Format(PyExc_AssertionError, \
"ufunc %s takes %d arguments, our loop takes %lu", \
#name, ufunc->nargs, (unsigned long) \
(sizeof(_types)/sizeof(int))); \
Py_DECREF(ufunc); \
} \
if (PyUFunc_RegisterLoopForType((PyUFuncObject*)ufunc, code, \
internal::binary_op_##name<T1,T2,R>, _types, 0) < 0) { \
/*Py_DECREF(ufunc);*/ \
/*goto fail; \*/ \
} \
Py_DECREF(ufunc); \
} }
#define EIGENPY_REGISTER_UNARY_UFUNC(name,code,T,R) { \ template <typename T>
PyUFuncObject* ufunc = \ void gufunc_matrix_multiply(char **args,
(PyUFuncObject*)PyObject_GetAttrString(numpy, #name); \ npy_intp EIGENPY_NPY_CONST_UFUNC_ARG *dimensions,
int _types[2] = { Register::getTypeCode<T>(), Register::getTypeCode<R>()}; \ npy_intp EIGENPY_NPY_CONST_UFUNC_ARG *steps,
if (!ufunc) { \ void *NPY_UNUSED(func)) {
/*goto fail; \*/ \ /* outer dimensions counter */
} \ npy_intp N_;
if (sizeof(_types)/sizeof(int)!=ufunc->nargs) { \
PyErr_Format(PyExc_AssertionError, \ /* length of flattened outer dimensions */
"ufunc %s takes %d arguments, our loop takes %lu", \ npy_intp dN = dimensions[0];
#name, ufunc->nargs, (unsigned long) \
(sizeof(_types)/sizeof(int))); \ /* striding over flattened outer dimensions for input and output arrays */
Py_DECREF(ufunc); \ npy_intp s0 = steps[0];
} \ npy_intp s1 = steps[1];
if (PyUFunc_RegisterLoopForType((PyUFuncObject*)ufunc, code, \ npy_intp s2 = steps[2];
internal::unary_op_##name<T,R>, _types, 0) < 0) { \
/*Py_DECREF(ufunc);*/ \ /*
/*goto fail; \*/ \ * loop through outer dimensions, performing matrix multiply on
} \ * core dimensions for each loop
Py_DECREF(ufunc); \ */
for (N_ = 0; N_ < dN; N_++, args[0] += s0, args[1] += s1, args[2] += s2) {
matrix_multiply<T>(args, dimensions + 1, steps + 3);
}
} }
template<typename Scalar> #define EIGENPY_REGISTER_BINARY_OPERATOR(name, op) \
void registerCommonUfunc() template <typename T1, typename T2, typename R> \
void binary_op_##name( \
char **args, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp *dimensions, \
EIGENPY_NPY_CONST_UFUNC_ARG npy_intp *steps, void * /*data*/) { \
npy_intp is0 = steps[0], is1 = steps[1], os = steps[2], n = *dimensions; \
char *i0 = args[0], *i1 = args[1], *o = args[2]; \
int k; \
for (k = 0; k < n; k++) { \
T1 &x = *static_cast<T1 *>(static_cast<void *>(i0)); \
T2 &y = *static_cast<T2 *>(static_cast<void *>(i1)); \
R &res = *static_cast<R *>(static_cast<void *>(o)); \
res = x op y; \
i0 += is0; \
i1 += is1; \
o += os; \
} \
} \
\
template <typename T> \
void binary_op_##name( \
char **args, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp *dimensions, \
EIGENPY_NPY_CONST_UFUNC_ARG npy_intp *steps, void *data) { \
binary_op_##name<T, T, T>(args, dimensions, steps, data); \
}
EIGENPY_REGISTER_BINARY_OPERATOR(add, +)
EIGENPY_REGISTER_BINARY_OPERATOR(subtract, -)
EIGENPY_REGISTER_BINARY_OPERATOR(multiply, *)
EIGENPY_REGISTER_BINARY_OPERATOR(divide, /)
EIGENPY_REGISTER_BINARY_OPERATOR(equal, ==)
EIGENPY_REGISTER_BINARY_OPERATOR(not_equal, !=)
EIGENPY_REGISTER_BINARY_OPERATOR(less, <)
EIGENPY_REGISTER_BINARY_OPERATOR(greater, >)
EIGENPY_REGISTER_BINARY_OPERATOR(less_equal, <=)
EIGENPY_REGISTER_BINARY_OPERATOR(greater_equal, >=)
#define EIGENPY_REGISTER_UNARY_OPERATOR(name, op) \
template <typename T, typename R> \
void unary_op_##name( \
char **args, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp *dimensions, \
EIGENPY_NPY_CONST_UFUNC_ARG npy_intp *steps, void * /*data*/) { \
npy_intp is = steps[0], os = steps[1], n = *dimensions; \
char *i = args[0], *o = args[1]; \
int k; \
for (k = 0; k < n; k++) { \
T &x = *static_cast<T *>(static_cast<void *>(i)); \
R &res = *static_cast<R *>(static_cast<void *>(o)); \
res = op x; \
i += is; \
o += os; \
} \
} \
\
template <typename T> \
void unary_op_##name( \
char **args, EIGENPY_NPY_CONST_UFUNC_ARG npy_intp *dimensions, \
EIGENPY_NPY_CONST_UFUNC_ARG npy_intp *steps, void *data) { \
unary_op_##name<T, T>(args, dimensions, steps, data); \
}
EIGENPY_REGISTER_UNARY_OPERATOR(negative, -)
EIGENPY_REGISTER_UNARY_OPERATOR(square, x *)
} // namespace internal
#define EIGENPY_REGISTER_BINARY_UFUNC(name, code, T1, T2, R) \
{ \
PyUFuncObject *ufunc = \
(PyUFuncObject *)PyObject_GetAttrString(numpy, #name); \
int _types[3] = {Register::getTypeCode<T1>(), Register::getTypeCode<T2>(), \
Register::getTypeCode<R>()}; \
if (!ufunc) { \
/*goto fail; \*/ \
} \
if (sizeof(_types) / sizeof(int) != ufunc->nargs) { \
PyErr_Format(PyExc_AssertionError, \
"ufunc %s takes %d arguments, our loop takes %lu", #name, \
ufunc->nargs, \
(unsigned long)(sizeof(_types) / sizeof(int))); \
Py_DECREF(ufunc); \
} \
if (PyUFunc_RegisterLoopForType((PyUFuncObject *)ufunc, code, \
internal::binary_op_##name<T1, T2, R>, \
_types, 0) < 0) { \
/*Py_DECREF(ufunc);*/ \
/*goto fail; \*/ \
} \
Py_DECREF(ufunc); \
}
#define EIGENPY_REGISTER_UNARY_UFUNC(name, code, T, R) \
{ \
PyUFuncObject *ufunc = \
(PyUFuncObject *)PyObject_GetAttrString(numpy, #name); \
int _types[2] = {Register::getTypeCode<T>(), Register::getTypeCode<R>()}; \
if (!ufunc) { \
/*goto fail; \*/ \
} \
if (sizeof(_types) / sizeof(int) != ufunc->nargs) { \
PyErr_Format(PyExc_AssertionError, \
"ufunc %s takes %d arguments, our loop takes %lu", #name, \
ufunc->nargs, \
(unsigned long)(sizeof(_types) / sizeof(int))); \
Py_DECREF(ufunc); \
} \
if (PyUFunc_RegisterLoopForType((PyUFuncObject *)ufunc, code, \
internal::unary_op_##name<T, R>, _types, \
0) < 0) { \
/*Py_DECREF(ufunc);*/ \
/*goto fail; \*/ \
} \
Py_DECREF(ufunc); \
}
template <typename Scalar>
void registerCommonUfunc() {
const int type_code = Register::getTypeCode<Scalar>();
PyObject *numpy_str;
numpy_str = PyStr_FromString("numpy");
PyObject *numpy;
numpy = PyImport_Import(numpy_str);
Py_DECREF(numpy_str);
import_ufunc();
// Matrix multiply
{ {
const int type_code = Register::getTypeCode<Scalar>(); int types[3] = {type_code, type_code, type_code};
PyObject* numpy_str;
#if PY_MAJOR_VERSION >= 3
numpy_str = PyUnicode_FromString("numpy");
#else
numpy_str = PyString_FromString("numpy");
#endif
PyObject* numpy;
numpy = PyImport_Import(numpy_str);
Py_DECREF(numpy_str);
import_ufunc();
// Matrix multiply
{
int types[3] = {type_code,type_code,type_code};
std::stringstream ss;
ss << "return result of multiplying two matrices of ";
ss << bp::type_info(typeid(Scalar)).name();
PyUFuncObject *ufunc =
(PyUFuncObject *)PyObject_GetAttrString(numpy, "matmul");
if (!ufunc) {
std::stringstream ss; std::stringstream ss;
ss << "return result of multiplying two matrices of "; ss << "Impossible to define matrix_multiply for given type "
ss << bp::type_info(typeid(Scalar)).name(); << bp::type_info(typeid(Scalar)).name() << std::endl;
PyUFuncObject* ufunc = (PyUFuncObject*)PyObject_GetAttrString(numpy, "matmul"); eigenpy::Exception(ss.str());
if(!ufunc) }
{ if (PyUFunc_RegisterLoopForType((PyUFuncObject *)ufunc, type_code,
std::stringstream ss; &internal::gufunc_matrix_multiply<Scalar>,
ss << "Impossible to define matrix_multiply for given type " << bp::type_info(typeid(Scalar)).name() << std::endl; types, 0) < 0) {
eigenpy::Exception(ss.str()); std::stringstream ss;
} ss << "Impossible to register matrix_multiply for given type "
if(PyUFunc_RegisterLoopForType((PyUFuncObject*)ufunc, type_code, << bp::type_info(typeid(Scalar)).name() << std::endl;
&internal::gufunc_matrix_multiply<Scalar>, types, 0) < 0) eigenpy::Exception(ss.str());
{
std::stringstream ss;
ss << "Impossible to register matrix_multiply for given type " << bp::type_info(typeid(Scalar)).name() << std::endl;
eigenpy::Exception(ss.str());
}
Py_DECREF(ufunc);
} }
// Binary operators Py_DECREF(ufunc);
EIGENPY_REGISTER_BINARY_UFUNC(add,type_code,Scalar,Scalar,Scalar);
EIGENPY_REGISTER_BINARY_UFUNC(subtract,type_code,Scalar,Scalar,Scalar);
EIGENPY_REGISTER_BINARY_UFUNC(multiply,type_code,Scalar,Scalar,Scalar);
EIGENPY_REGISTER_BINARY_UFUNC(divide,type_code,Scalar,Scalar,Scalar);
// Comparison operators
EIGENPY_REGISTER_BINARY_UFUNC(equal,type_code,Scalar,Scalar,bool);
EIGENPY_REGISTER_BINARY_UFUNC(not_equal,type_code,Scalar,Scalar,bool);
EIGENPY_REGISTER_BINARY_UFUNC(greater,type_code,Scalar,Scalar,bool);
EIGENPY_REGISTER_BINARY_UFUNC(less,type_code,Scalar,Scalar,bool);
EIGENPY_REGISTER_BINARY_UFUNC(greater_equal,type_code,Scalar,Scalar,bool);
EIGENPY_REGISTER_BINARY_UFUNC(less_equal,type_code,Scalar,Scalar,bool);
// Unary operators
EIGENPY_REGISTER_UNARY_UFUNC(negative,type_code,Scalar,Scalar);
Py_DECREF(numpy);
} }
} // namespace eigenpy
#endif // __eigenpy_ufunc_hpp__ // Binary operators
EIGENPY_REGISTER_BINARY_UFUNC(add, type_code, Scalar, Scalar, Scalar);
EIGENPY_REGISTER_BINARY_UFUNC(subtract, type_code, Scalar, Scalar, Scalar);
EIGENPY_REGISTER_BINARY_UFUNC(multiply, type_code, Scalar, Scalar, Scalar);
EIGENPY_REGISTER_BINARY_UFUNC(divide, type_code, Scalar, Scalar, Scalar);
// Comparison operators
EIGENPY_REGISTER_BINARY_UFUNC(equal, type_code, Scalar, Scalar, bool);
EIGENPY_REGISTER_BINARY_UFUNC(not_equal, type_code, Scalar, Scalar, bool);
EIGENPY_REGISTER_BINARY_UFUNC(greater, type_code, Scalar, Scalar, bool);
EIGENPY_REGISTER_BINARY_UFUNC(less, type_code, Scalar, Scalar, bool);
EIGENPY_REGISTER_BINARY_UFUNC(greater_equal, type_code, Scalar, Scalar, bool);
EIGENPY_REGISTER_BINARY_UFUNC(less_equal, type_code, Scalar, Scalar, bool);
// Unary operators
EIGENPY_REGISTER_UNARY_UFUNC(negative, type_code, Scalar, Scalar);
EIGENPY_REGISTER_UNARY_UFUNC(square, type_code, Scalar, Scalar);
Py_DECREF(numpy);
}
} // namespace eigenpy
#endif // __eigenpy_ufunc_hpp__