力numpy的ndarray采取它在用Cython内存的所有权 [英] Force NumPy ndarray to take ownership of its memory in Cython

问题描述

回答我可以强制numpy的ndarray采取其内存的所有权？我试图使用Python的C API函数 PyArray_ENABLEFLAGS 通过用Cython的numpy的包装，发现它不外露。

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.

以下尝试手动将其暴露（这只是一个例子最小再现故障）

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)

失败，编译错误：

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 *'

我的问题：这是拿在这种情况下，正确的做法？如果是这样，我究竟做错了什么？如果不是，我怎么逼numpy的采取用Cython所有权，没有去到一个C扩展模块？

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?

推荐答案

您只需在接口定义了一些小错误。下面为我​​工作：

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

这是我的 setup.py 文件：

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)

与蟒蛇setup.py build_ext --inplace 生成。然后验证数据实际上拥有：

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

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

其中，你应该看到 OWNDATA：真

和是的，这绝对是处理这个正确的方式，因为 numpy.pxd 不完全是一回事，导出所有其他功能用Cython。

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.

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