mirror of
https://github.com/triqs/dft_tools
synced 2024-12-25 22:03:43 +01:00
fc2a620eae
- improve the mem_block and shared_block. - the reference counting is now done in the mem_block and shared_block, removing the need of shared_ptr. - speed tests shows that shared_ptr is very slow (due to thread safety?) the new version is much better, though not perfect. - Hence introducing weak views. - also : -- clean the guard mechanism for python (to allow returning from python without any python ref left). -- clean code, add documentation for mem_block -- remove nan init, which was not working, and corresponding test -- serialisation of view still unchanged (need to forbid serialization of view ??). - tests ok, incl. valgrind tests.
77 lines
3.1 KiB
C++
77 lines
3.1 KiB
C++
/*******************************************************************************
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*
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* TRIQS: a Toolbox for Research in Interacting Quantum Systems
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*
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* Copyright (C) 2011 by O. Parcollet
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*
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* TRIQS is free software: you can redistribute it and/or modify it under the
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* terms of the GNU General Public License as published by the Free Software
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* Foundation, either version 3 of the License, or (at your option) any later
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* version.
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*
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* TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
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* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
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* details.
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*
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* You should have received a copy of the GNU General Public License along with
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* TRIQS. If not, see <http://www.gnu.org/licenses/>.
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*
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******************************************************************************/
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#ifndef TRIQS_ARRAYS_TO_PYTHON_H
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#define TRIQS_ARRAYS_TO_PYTHON_H
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#ifndef TRIQS_WITH_PYTHON_SUPPORT
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#error "You must define the macro TRIQS_WITH_PYTHON_SUPPORT to use Python interface"
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#endif
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#include <complex>
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#include "../impl/indexmap_storage_pair.hpp"
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//#include "../array.hpp"
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namespace triqs { namespace arrays { namespace numpy_interface {
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template<typename ArrayViewType >
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PyObject * array_view_to_python ( ArrayViewType const & A, bool copy=false) {
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_import_array();
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typedef typename ArrayViewType::value_type value_type;
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static const int rank = ArrayViewType::rank;
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const int elementsType (numpy_to_C_type<typename boost::remove_const<value_type>::type>::arraytype);
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npy_intp dims[rank], strides[rank];
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for(size_t i =0; i<rank; ++i) { dims[i] = A.indexmap().lengths()[i]; strides[i] = A.indexmap().strides()[i]*sizeof(value_type); }
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const value_type * data = A.data_start();
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//int flags = NPY_ARRAY_BEHAVED & ~NPY_ARRAY_OWNDATA;;// for numpy2
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#ifdef TRIQS_NUMPY_VERSION_LT_17
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int flags = NPY_BEHAVED & ~NPY_OWNDATA;
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#else
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int flags = NPY_ARRAY_BEHAVED & ~NPY_ARRAY_OWNDATA;
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#endif
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PyObject* res = PyArray_NewFromDescr(&PyArray_Type, PyArray_DescrFromType(elementsType), (int) rank, dims, strides, (void*) data, flags, NULL);
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if (!res) {
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if (PyErr_Occurred()) {PyErr_Print();PyErr_Clear();}
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TRIQS_RUNTIME_ERROR<<" array_view_from_numpy : the python numpy object could not be build";
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}
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if (!PyArray_Check(res)) TRIQS_RUNTIME_ERROR<<" array_view_from_numpy : internal error : the python object is not a numpy";
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PyArrayObject * arr = (PyArrayObject *)(res);
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//PyArray_SetBaseObject(arr, A.storage().new_python_ref());
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#ifdef TRIQS_NUMPY_VERSION_LT_17
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arr->base = A.storage().new_python_ref();
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assert( arr->flags == (arr->flags & ~NPY_OWNDATA));
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#else
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int r = PyArray_SetBaseObject(arr,A.storage().new_python_ref());
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if (r!=0) TRIQS_RUNTIME_ERROR << "Internal Error setting the guard in numpy !!!!";
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assert( PyArray_FLAGS(arr) == (PyArray_FLAGS(arr) & ~NPY_ARRAY_OWNDATA));
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#endif
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if (copy) {
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PyObject * na = PyObject_CallMethod(res,(char*)"copy",NULL);
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Py_DECREF(res);
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// POrt this for 1.7
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//assert(((PyArrayObject *)na)->base ==NULL);
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res = na;
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}
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return res;
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}
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}}}
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#endif
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