namitkewat · January 6, 2013 07:29
diff --git a/c_code.c b/c_code.c
 /* Small C file creating an array to demo C -> Python data passing 
 * 
 * Author: Gael Varoquaux
 * License: BSD
 */

 #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;
 }

diff --git a/cython_wrapper.pyx b/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

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

 # 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()


 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
    # Call the C function
    array = compute(size)

    # Create a C array to describe the shape of the ndarray
    cdef np.npy_intp shape[1]
    shape[0] = <np.npy_intp> size

    # Use the PyArray_SimpleNewFromData function from numpy to create a
    # new Python object pointing to the existing data
    ndarray = np.PyArray_SimpleNewFromData(1, shape,
                    np.NPY_INT, <void *> array)
    return ndarray
diff --git a/setup.py b/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

 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)
diff --git a/test.py b/test.py
 """ Script to smoke-test our Cython wrappers
 """
 # Author: Gael Varoquaux
 # License: BSD

 import cython_wrapper
 print cython_wrapper.py_compute(10)
	/* Small C file creating an array to demo C -> Python data passing
	*
	* Author: Gael Varoquaux
	* License: BSD
	*/

	#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;
	}
	""" 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

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

	# 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()


	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
	# Call the C function
	array = compute(size)

	# Create a C array to describe the shape of the ndarray
	cdef np.npy_intp shape[1]
	shape[0] = <np.npy_intp> size

	# Use the PyArray_SimpleNewFromData function from numpy to create a
	# new Python object pointing to the existing data
	ndarray = np.PyArray_SimpleNewFromData(1, shape,
	np.NPY_INT, <void *> array)
	return ndarray
	""" 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

	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)
	""" Script to smoke-test our Cython wrappers
	"""
	# Author: Gael Varoquaux
	# License: BSD

	import cython_wrapper
	print cython_wrapper.py_compute(10)