Force NumPy ndarray to take ownership of its memory in Cython

Following this answer to “Can I force a numpy ndarray to take ownership of its memory?” I attempted to use the Python C API function PyArray_ENABLEFLAGS through Cython’s NumPy wrapper and found it is not exposed.

The following attempt to expose it manually (this is just a minimum example reproducing the failure)

from libc.stdlib cimport malloc
import numpy as np
cimport numpy as np

np.import_array()

ctypedef np.int32_t DTYPE_t

cdef extern from "numpy/ndarraytypes.h":
    void PyArray_ENABLEFLAGS(np.PyArrayObject *arr, int flags)

def test():
    cdef int N = 1000

    cdef DTYPE_t *data = <DTYPE_t *>malloc(N * sizeof(DTYPE_t))
    cdef np.ndarray[DTYPE_t, ndim=1] arr = np.PyArray_SimpleNewFromData(1, &N, np.NPY_INT32, data)
    PyArray_ENABLEFLAGS(arr, np.NPY_ARRAY_OWNDATA)

fails with a compile error:

Error compiling Cython file:
------------------------------------------------------------
...
def test():
    cdef int N = 1000

    cdef DTYPE_t *data = <DTYPE_t *>malloc(N * sizeof(DTYPE_t))
    cdef np.ndarray[DTYPE_t, ndim=1] arr = np.PyArray_SimpleNewFromData(1, &N, np.NPY_INT32, data)
    PyArray_ENABLEFLAGS(arr, np.NPY_ARRAY_OWNDATA)
                          ^
------------------------------------------------------------

/tmp/test.pyx:19:27: Cannot convert Python object to 'PyArrayObject *'

My question: Is this the right approach to take in this case? If so, what am I doing wrong? If not, how do I force NumPy to take ownership in Cython, without going down to a C extension module?

Answer

You just have some minor errors in the interface definition. The following worked for me:

from libc.stdlib cimport malloc
import numpy as np
cimport numpy as np

np.import_array()

ctypedef np.int32_t DTYPE_t

cdef extern from "numpy/arrayobject.h":
    void PyArray_ENABLEFLAGS(np.ndarray arr, int flags)

cdef data_to_numpy_array_with_spec(void * ptr, np.npy_intp N, int t):
    cdef np.ndarray[DTYPE_t, ndim=1] arr = np.PyArray_SimpleNewFromData(1, &N, t, ptr)
    PyArray_ENABLEFLAGS(arr, np.NPY_OWNDATA)
    return arr

def test():
    N = 1000

    cdef DTYPE_t *data = <DTYPE_t *>malloc(N * sizeof(DTYPE_t))
    arr = data_to_numpy_array_with_spec(data, N, np.NPY_INT32)
    return arr

This is my setup.py file:

from distutils.core import setup, Extension
from Cython.Distutils import build_ext
ext_modules = [Extension("_owndata", ["owndata.pyx"])]
setup(cmdclass={'build_ext': build_ext}, ext_modules=ext_modules)

Build with python setup.py build_ext --inplace. Then verify that the data is actually owned:

import _owndata
arr = _owndata.test()
print arr.flags

Among others, you should see OWNDATA : True.

And yes, this is definitely the right way to deal with this, since numpy.pxd does exactly the same thing to export all the other functions to Cython.