Copy-less bindings of C-generated arrays with Cython

00README.rst

Cython example of exposing C-computed arrays in Python without data copies

The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.

The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy function in the Cython file cython_wrapper.pyx.

The purpose of the ArrayWrapper object, is to be garbage-collected by Python when the ndarray Python object disappear. The memory is then freed. Note that there is no control of when Python will deallocate the memory. If the memory is still being used by the C code, please refer to the following blog post by Travis Oliphant:

http://blog.enthought.com/python/numpy-arrays-with-pre-allocated-memory

You will need Cython, numpy, and a C compiler.

To build the C extension in-place run:

$ python setup.py build_ext -i

To test the C-Python bindings, run the test.py file.

Files
c_code.c	The C code to bind. Knows nothing about Python
cython_wrapper.c	The Cython code implementing the binding
setup.py	The configure/make/install script
test.py	Python code using the C extension

---

Author:	Gael Varoquaux
License:	BSD 3 clause

c_code.c

/* Small C file creating an array to demo C -> Python data passing 
 * 
 * Author: Gael Varoquaux
 * License: BSD 3 clause
 */

#include <stdlib.h>

float *compute(int size)
{
    int* array;
    array = malloc(sizeof(int)*size);
    int i;
    for (i=0; i<size; i++)
    {
	array[i] = i;
    }
    return array;
}

cython_wrapper.pyx

""" Small Cython file to demonstrate the use of PyArray_SimpleNewFromData
in Cython to create an array from already allocated memory.

Cython enables mixing C-level calls and Python-level calls in the same
file with a Python-like syntax and easy type cohersion. See 
http://cython.org for more information
"""

# Author: Gael Varoquaux
# License: BSD 3 clause

# Declare the prototype of the C function we are interested in calling
cdef extern from "c_code.c":
    float *compute(int size)

from libc.stdlib cimport free
from cpython cimport PyObject, Py_INCREF

# Import the Python-level symbols of numpy
import numpy as np

# Import the C-level symbols of numpy
cimport numpy as np

# Numpy must be initialized. When using numpy from C or Cython you must
# _always_ do that, or you will have segfaults
np.import_array()

# We need to build an array-wrapper class to deallocate our array when
# the Python object is deleted.

cdef class ArrayWrapper:
    cdef void* data_ptr
    cdef int size

    cdef set_data(self, int size, void* data_ptr):
        """ Set the data of the array

        This cannot be done in the constructor as it must recieve C-level
        arguments.

        Parameters:
        -----------
        size: int
            Length of the array.
        data_ptr: void*
            Pointer to the data            

        """
        self.data_ptr = data_ptr
        self.size = size

    def __array__(self):
        """ Here we use the __array__ method, that is called when numpy
            tries to get an array from the object."""
        cdef np.npy_intp shape[1]
        shape[0] = <np.npy_intp> self.size
        # Create a 1D array, of length 'size'
        ndarray = np.PyArray_SimpleNewFromData(1, shape,
                                               np.NPY_INT, self.data_ptr)
        return ndarray

    def __dealloc__(self):
        """ Frees the array. This is called by Python when all the
        references to the object are gone. """
        free(<void*>self.data_ptr)


def py_compute(int size):
    """ Python binding of the 'compute' function in 'c_code.c' that does
        not copy the data allocated in C.
    """
    cdef float *array
    cdef np.ndarray ndarray
    # Call the C function
    array = compute(size)

    array_wrapper = ArrayWrapper()
    array_wrapper.set_data(size, <void*> array) 
    ndarray = np.array(array_wrapper, copy=False)
    # Assign our object to the 'base' of the ndarray object
    ndarray.base = <PyObject*> array_wrapper
    # Increment the reference count, as the above assignement was done in
    # C, and Python does not know that there is this additional reference
    Py_INCREF(array_wrapper)


    return ndarray

setup.py

""" Example of building a module with a Cython file. See the distutils
and numpy distutils documentations for more info:
http://docs.scipy.org/doc/numpy/reference/distutils.html
"""
# Author: Gael Varoquaux
# License: BSD 3 clause

import numpy
from Cython.Distutils import build_ext

def configuration(parent_package='', top_path=None):
    """ Function used to build our configuration.
    """
    from numpy.distutils.misc_util import Configuration

    # The configuration object that hold information on all the files
    # to be built.
    config = Configuration('', parent_package, top_path)
    config.add_extension('cython_wrapper',
                         sources=['cython_wrapper.pyx'],
                         # libraries=['m'],
                         depends=['c_code.c'],
                         include_dirs=[numpy.get_include()])
    return config


if __name__ == '__main__':
    # Retrieve the parameters of our local configuration
    params = configuration(top_path='').todict()

    # Override the C-extension building so that it knows about '.pyx'
    # Cython files
    params['cmdclass'] = dict(build_ext=build_ext)

    # Call the actual building/packaging function (see distutils docs)
    from numpy.distutils.core import setup
    setup(**params)

test.py

""" Script to smoke-test our Cython wrappers
"""
# Author: Gael Varoquaux
# License: BSD 3 clause
import numpy as np

import cython_wrapper
a = cython_wrapper.py_compute(10)

print 'The array created is %s' % a
print 'It carries a reference to our deallocator: %s ' % a.base
np.testing.assert_allclose(a, np.arange(10))

The original blog post is no longer available, but I found it on the archive: http://web.archive.org/web/20160321001549/http://blog.enthought.com/python/numpy-arrays-with-pre-allocated-memory/