mirror of
https://github.com/triqs/dft_tools
synced 2024-12-27 06:43:40 +01:00
79 lines
2.5 KiB
ReStructuredText
79 lines
2.5 KiB
ReStructuredText
.. highlight:: c
|
|
|
|
Interface with Python numpy arrays
|
|
===================================================================
|
|
|
|
The array, matrix, vector and their views are fully interoperable with the numpy array objects in python.
|
|
|
|
From Python to C++
|
|
--------------------------
|
|
|
|
Value and view classes can be constructed from a PyObject * (the opaque type of python object).
|
|
They follow their respective semantic :
|
|
|
|
* `value classes` (array, matrix, vector) **always** make copies.
|
|
|
|
Hence they can be constructed from a python object X which is not an array, but
|
|
out of which numpy can make an array of the correct type.
|
|
|
|
* `view classes` **never** make copies, they present views of the numpy array.
|
|
|
|
If this is not possible (e.g. the python object is not a numpy, but a list, the type are not exactly the same)
|
|
they throw an exception (`triqs::runtime_error`), with an explanation of the problem.
|
|
|
|
|
|
From C++ to Python
|
|
----------------------
|
|
|
|
Value and view classes have a to_python method with the following synopsis ::
|
|
|
|
PyObject * to_python() const
|
|
|
|
which return a **new** reference to the numpy array.
|
|
|
|
To be more precise, two cases must be distinguished.
|
|
|
|
* array_view constructed from a PyObject * .
|
|
|
|
In this case, the array_view's storage is the numpy array, and it keeps a
|
|
(owned) reference to the python array all along its existence.
|
|
This means that Python can not destroy the array as long as the view exists.
|
|
|
|
The to_python method simply returns a new reference to this numpy array.
|
|
|
|
* array or an array_view which was *not* constructed from a PyObject* .
|
|
|
|
In this case, the storage has been allocated by C++, for example because the array
|
|
was created in a C++ routine. There is no natural numpy array to return.
|
|
The library returns a new numpy array which *owns* the C++ data,
|
|
so the usage of the class is completely transparent.
|
|
Python will *automatically* release the memory allocated by the C++ routine
|
|
when the array in no longer needed.
|
|
|
|
Cython
|
|
------------
|
|
|
|
TRIQS main tool for interacing python/C++ code is Cython.
|
|
We provide in pytriqs/pxd the cython interface arrays.pxd for the array classes.
|
|
|
|
Examples
|
|
-----------------
|
|
|
|
Split in several files. --> also the theory above.
|
|
|
|
Put here the array_cython example
|
|
|
|
- a routine that take a view
|
|
- a routine that take an array (beware to the copy).
|
|
- a wrapped class.
|
|
- a function that returns a new array from C++. Check references....
|
|
|
|
|
|
.. code-block:: python
|
|
|
|
import numpy,_testarray
|
|
a=numpy.array([[1.0,2],[3,4]])
|
|
_testarray.f(a)
|
|
|
|
|