/*******************************************************************************
 *
 * TRIQS: a Toolbox for Research in Interacting Quantum Systems
 *
 * Copyright (C) 2011 by O. Parcollet
 *
 * TRIQS is free software: you can redistribute it and/or modify it under the
 * terms of the GNU General Public License as published by the Free Software
 * Foundation, either version 3 of the License, or (at your option) any later
 * version.
 *
 * TRIQS 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 General Public License for more
 * details.
 *
 * You should have received a copy of the GNU General Public License along with
 * TRIQS. If not, see <http://www.gnu.org/licenses/>.
 *
 ******************************************************************************/
#include "../impl/common.hpp"
#include "../impl/traits.hpp"
#include "../impl/flags.hpp"
#include "../../utility/mini_vector.hpp"
#include "./numpy_extractor.hpp"
#ifdef TRIQS_WITH_PYTHON_SUPPORT

namespace triqs { namespace arrays { namespace numpy_interface  {


 PyObject * numpy_extractor_impl ( PyObject * X, bool allow_copy, std::string type_name, 
   int elementsType, int rank, size_t * lengths, std::ptrdiff_t * strides, size_t size_of_ValueType) {

  PyObject * numpy_obj;

  if (X==NULL) TRIQS_RUNTIME_ERROR<<"numpy interface : the python object is NULL !";
  if (_import_array()!=0) TRIQS_RUNTIME_ERROR <<"Internal Error in importing numpy";

  static const char * error_msg = "   A deep copy of the object would be necessary while views are supposed to guarantee to present a *view* of the python data.\n";

  if (!allow_copy) {
   if (!PyArray_Check(X)) throw copy_exception () << error_msg<<"   Indeed the object was not even an array !\n";
   if ( elementsType != PyArray_TYPE((PyArrayObject*)X))
    throw copy_exception () << error_msg<<"   The deep copy is caused by a type mismatch of the elements. Expected "<< type_name<< " and found XXX \n";
   PyArrayObject *arr = (PyArrayObject *)X;
#ifdef TRIQS_NUMPY_VERSION_LT_17
   if ( arr->nd != rank) throw copy_exception () << error_msg<<"   Rank mismatch . numpy array is of rank "<< arr->nd << "while you ask for rank "<< rank<<". \n";
#else
   if ( PyArray_NDIM(arr) != rank) throw copy_exception () << error_msg<<"   Rank mismatch . numpy array is of rank "<<  PyArray_NDIM(arr) << "while you ask for rank "<< rank<<". \n";
#endif
   numpy_obj = X; Py_INCREF(X);
  }
  else {
   // From X, we ask the numpy library to make a numpy, and of the correct type.
   // This handles automatically the cases where :
   //   - we have list, or list of list/tuple
   //   - the numpy type is not the one we want.
   //   - adjust the dimension if needed
   // If X is an array :
   //   - if Order is same, don't change it
   //   - else impose it (may provoque a copy).
   // if X is not array :
   //   - Order = FortranOrder or SameOrder - > Fortran order otherwise C

   //bool ForceCast = false;// Unless FORCECAST is present in flags, this call will generate an error if the data type cannot be safely obtained from the object.
   int flags = 0; //(ForceCast ? NPY_FORCECAST : 0) ;// do NOT force a copy | (make_copy ?  NPY_ENSURECOPY : 0);
   if (!(PyArray_Check(X) ))
    //flags |= ( IndexMapType::traversal_order == indexmaps::mem_layout::c_order(rank) ? NPY_C_CONTIGUOUS : NPY_F_CONTIGUOUS); //impose mem order
#ifdef TRIQS_NUMPY_VERSION_LT_17
    flags |= (NPY_C_CONTIGUOUS); //impose mem order
#else
    flags |= (NPY_ARRAY_C_CONTIGUOUS); //impose mem order
#endif
   numpy_obj= PyArray_FromAny(X,PyArray_DescrFromType(elementsType), rank,rank, flags , NULL );

   // do several checks
   if (!numpy_obj) {// The convertion of X to a numpy has failed !
    if (PyErr_Occurred()) {
     //PyErr_Print();
     PyErr_Clear();
    }
    TRIQS_RUNTIME_ERROR<<"numpy interface : the python object  is not convertible to a numpy. ";
   }
   assert (PyArray_Check(numpy_obj)); assert((numpy_obj->ob_refcnt==1) || ((numpy_obj ==X)));

   PyArrayObject *arr_obj;
   arr_obj = (PyArrayObject *)numpy_obj;
   try {
#ifdef TRIQS_NUMPY_VERSION_LT_17
    if (arr_obj->nd!=rank)  TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : dimensions do not match";
    if (arr_obj->descr->type_num != elementsType)
     TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : incorrect type of element :" <<arr_obj->descr->type_num <<" vs "<<elementsType;
#else
    if ( PyArray_NDIM(arr_obj) !=rank)  TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : dimensions do not match";
    if ( PyArray_DESCR(arr_obj)->type_num != elementsType)
     TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : incorrect type of element :" <<PyArray_DESCR(arr_obj)->type_num <<" vs "<<elementsType;
#endif
   }
   catch(...) { Py_DECREF(numpy_obj); throw;} // make sure that in case of problem, the reference counting of python is still ok...
  }

  // extract strides and lengths
  PyArrayObject *arr_obj;
  arr_obj = (PyArrayObject *)numpy_obj;
#ifdef TRIQS_NUMPY_VERSION_LT_17
  const size_t dim =arr_obj->nd; // we know that dim == rank
  for (size_t i=0; i< dim ; ++i) {
   lengths[i] = size_t(arr_obj-> dimensions[i]);
   strides[i] = std::ptrdiff_t(arr_obj-> strides[i])/ size_of_ValueType;
  }
#else
  const size_t dim = PyArray_NDIM(arr_obj); // we know that dim == rank
  for (size_t i=0; i< dim ; ++i) {
   lengths[i] = size_t( PyArray_DIMS(arr_obj)[i]);
   strides[i] = std::ptrdiff_t( PyArray_STRIDES(arr_obj)[i])/ size_of_ValueType;
  }
#endif

  return numpy_obj;
 }
}}}
#endif