exception_ptr make_exception_ptr(T v)
{
return copy_exception(wrap<T> (v));
}
exception_ptr make_exception_ptr(T v)
{
return copy_exception(v);
}
numpy_extractor ( PyObject * X, bool allow_copy) {
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";
// make sure IndexMap is cuboid ?s
static const char Order = IndexMapType::index_order_type::C_or_F;
static_assert( ((Order=='F')||(Order=='C')), "Ordering must be C or Fortran");
if ((!PyArray_Check(X)) && (Order=='D'))
TRIQS_RUNTIME_ERROR<<"numpy interface : the python object is not a numpy and you ask me to deduce the ordering in memory !";
const int elementsType (numpy_to_C_type<typename boost::remove_const<ValueType>::type>::arraytype);
int rank = IndexMapType::rank;
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) {
// in case of a view, we decide ourselves if we can do it...
// a previous uses PyArray_FromAny, but behaviour changes between different version of numpy, so it is not portable...
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. \n";
PyArrayObject *arr = (PyArrayObject *)X;
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";
if ((Order == 'C') && (PyArray_ISFORTRAN(arr)))
throw copy_exception () << error_msg<<" The numpy is in Fortran order while it is expected in C order. \n";
if ((Order == 'F') && (!PyArray_ISFORTRAN(arr)))
throw copy_exception () << error_msg<<" The numpy is not in Fortran order as it is expected. \n";
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 = (ForceCast ? NPY_FORCECAST : 0) ;// do NOT force a copy | (make_copy ? NPY_ENSURECOPY : 0);
if (!(PyArray_Check(X) && (Order=='D'))) flags |= (Order =='F' ? NPY_F_CONTIGUOUS : NPY_C_CONTIGUOUS); //impose mem order
//if (!(PyArray_Check(X) && (Order=='D'))) flags |= (Order =='F' ? NPY_FARRAY : NPY_CARRAY); //impose mem order
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)));
arr_obj = (PyArrayObject *)numpy_obj;
try {
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;
if (Order == 'F') { if (!PyArray_ISFORTRAN(numpy_obj)) TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : should be Fortran array";}
else {if (!PyArray_ISCONTIGUOUS(numpy_obj)) TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : should be contiguous";}
}
catch(...) { Py_DECREF(numpy_obj); throw;} // make sure that in case of problem, the reference counting of python is still ok...
}
arr_obj = (PyArrayObject *)numpy_obj;
}
// return a NEW (owned) reference
//
inline 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;
}
