return numpy array in cython defined c function

Question

I am not sure if the following question makes sense, since I am newbie. I am trying to create in Cython a C function which returns a numpy array, like the following.

cdef np.ndarray[np.int32_t, ndim=1] SumPlusOne(np.ndarray[np.int32_t, ndim=1] ArgArray):
    cdef np.ndarray[int32_t, ndim=1] ReturnArray = np.zeros((len(ArgArray), dtype = np.int32)

    ReturnArray = ArgArray + 1

    return ReturnArray

However, is not letting me to compile it. But if I remove the return type of the function

cdef SumPlusOne(np.ndarray[np.int32_t, ndim=1] ArgArray):
    ...

There is no problem.

My question is, is there any way to declare numpy type for the return value? I don't really know if this is possible, since I don't know if np.ndarray needs to be converted to python type.

thank you

hpaulj · Accepted Answer · 2015-02-14 18:00:40Z

10

According to cython documentation, for a cdef function:

If no type is specified for a parameter or return value, it is assumed to be a Python object.

A numpy array is a Python object. No conversion to a Python 'type' is needed. Its elements may be Python/C types (dtype), but the array as a whole is an object.

np.zeros((len(ArgArray), dtype = np.int32)

works in Python just as well as in Cython.

In my limited testing both of your cdefs work. Maybe it's a matter of cython version?

This works both with and without the type declaration after the cpdef (using this form so I can call it from Python)

import numpy as np
cimport numpy as np
cimport cython

cpdef np.ndarray[np.int32_t, ndim=1] sum_plus_one(np.ndarray[np.int32_t, ndim=1] arg):
    cdef np.ndarray[np.int32_t, ndim=1] result = np.zeros((len(arg)), dtype = np.int32)
    result = arg + 1
    return result

edited Feb 14, 2015 at 18:00

answered Feb 14, 2015 at 17:50

hpaulj

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1 Comment

manolius Over a year ago

thank you for replying. I tried again in a different computer, and it is working both ways as you said. I don't know if it is the Cython version or that I had a typo before. Anyway, numpy arrays structure are a little confusing for me, so your explanation helped. Thank you

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