#ifndef NDARRAY_EIGEN_BP_Array_h_INCLUDED

#define NDARRAY_EIGEN_BP_Array_h_INCLUDED

#include "ndarray/eigen/bp/EigenView.h"

namespace ndarray {

template <typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>

class ToBoostPython< Eigen::Array<Scalar,Rows,Cols,Options,MaxRows,MaxCols> > {

typedef Eigen::Array<Scalar,Rows,Cols,Options,MaxRows,MaxCols> Input;

typedef boost::numpy::ndarray result_type;

typedef typename SelectEigenView<Input>::Type View;

static boost::numpy::ndarray apply(Input const & input) {

return ToBoostPython< View >::apply(ndarray::copy(input));

}

};

template <typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>

class FromBoostPython< Eigen::Array<Scalar,Rows,Cols,Options,MaxRows,MaxCols> > {

typedef Eigen::Array<Scalar,Rows,Cols,Options,MaxRows,MaxCols> Output;

// Use noncontiguous EigenView ('false' below) because we don't care about the input strides.

typedef typename SelectEigenView<Output,false>::Type View;

explicit FromBoostPython(boost::python::object const & input) : _impl(input) {}

bool convertible() { return _impl.convertible(); }

Output operator()() { return Output(_impl()); }

FromBoostPython< View > _impl;

};

} // namespace ndarray

#endif // !NDARRAY_EIGEN_BP_Matrix_h_INCLUDED