pybind11和numpy进行交互的方法

struct buffer_info {
  void *ptr;
  ssize_t itemsize;
  std::string format;
  ssize_t ndim;
  std::vector shape;
  std::vector strides;
};

#include 
 #include 
 namespace py = pybind11;
 py::array_t add_arrays(py::array_t input1, py::array_t input2) {
   py::buffer_info buf1 = input1.request(), buf2 = input2.request();
   if (buf1.ndim != 1 || buf2.ndim != 1)
     throw std::runtime_error("Number of dimensions must be one");
   if (buf1.size != buf2.size)
     throw std::runtime_error("Input shapes must match");
   /* No pointer is passed, so NumPy will allocate the buffer */
   auto result = py::array_t(buf1.size);
   py::buffer_info buf3 = result.request();
   double *ptr1 = (double *) buf1.ptr,
      *ptr2 = (double *) buf2.ptr,
      *ptr3 = (double *) buf3.ptr;
   for (size_t idx = 0; idx 

#include 
 #include  
 #include  
 // N.B. this would equally work with Eigen-types that are not predefined. For example replacing
 // all occurrences of "Eigen::MatrixXd" with "MatD", with the following definition:
 //
 // typedef Eigen::Matrix MatD;
 
 Eigen::MatrixXd inv(const Eigen::MatrixXd &xs)
 {
  return xs.inverse();
 }
 
 double det(const Eigen::MatrixXd &xs)
 {
  return xs.determinant();
 }
 
 namespace py = pybind11;
 
 PYBIND11_MODULE(example,m)
 {
  m.doc() = "pybind11 example plugin";
 
  m.def("inv", &inv);
 
  m.def("det", &det);
 }