I'd like to convert some ctypes code to use cython instead, but I'm struggling. Essentially, the ctypes code:

• copies the contents (floats) of two lists into C-compatible structs
• sends the structs to my binary via FFI
• receives the structs back (the length is never modified)
• copies the contents to two new lists
• sends the structs back across the FFI boundary so their memory can be freed

My ctypes code looks like this so far:

rlib.h

#ifndef RLIB_H
#define RLIB_Htypedef struct _FFIArray {void* data;size_t len;
} _FFIArray;typedef struct _Result_Tuple {_FFIArray e;_FFIArray n;
} _Result_Tuple;_Result_Tuple convert_to_bng_threaded(_FFIArray x, _FFIArray y);
void drop_float_array(_FFIArray x, _FFIArray y)#endif

mylib.pxd

cdef extern from "rlib.h":struct _FFIArray:void* datasize_t lenstruct _Result_Tuple:_FFIArray e_FFIArray ncdef _Result_Tuple convert_to_bng_threaded(_FFIArray x, _FFIArray y)cdef void drop_float_array(_FFIArray x, _FFIArray y)

util_cython.pyx

import cython
from mylib cimport _FFIArray, convert_to_bng_threaded, drop_float_arraydef call_convert_bng(_FFIArray x, _FFIArray y):return convert_to_bng_threaded(x, y)def call_drop_float_array(_FFIArray x, _FFIArray y):return drop_float_array(x, y)

setup.py

from setuptools import setup, Extension, find_packagesfrom Cython.Build import cythonize
from Cython.Distutils import build_extext = Extension('util_cython', sources=['util_cython.pyx'],libraries=['latlon_bng',],library_dirs=['.',],include_dirs=['.']
)extensions = [ext,]setup(name = "util_cython",ext_modules = cythonize(extensions),cmdclass={'build_ext': build_ext},
)

I have a few questions about how to proceed:

Firstly, the compilation step is currently failing:

python setup.py build_ext --inplace
Compiling util_cython.pyx because it changed.
[1/1] Cythonizing util_cython.pyxError compiling Cython file:
------------------------------------------------------------
...
import cython
from mylib cimport _FFIArray, convert_to_bng_threaded, drop_float_arraydef call_convert_bng(_FFIArray x, _FFIArray y):return convert_to_bng_threaded(x, y)^
------------------------------------------------------------util_cython.pyx:5:34: Cannot convert '_Result_Tuple' to Python object
Traceback (most recent call last):File "setup.py", line 17, in <module>ext_modules = cythonize(extensions),File "/Users/sth/dev/cythonize_test/venv/lib/python2.7/site-packages/Cython/Build/Dependencies.py", line 912, in cythonizecythonize_one(*args)File "/Users/sth/dev/cythonize_test/venv/lib/python2.7/site-packages/Cython/Build/Dependencies.py", line 1034, in cythonize_oneraise CompileError(None, pyx_file)
Cython.Compiler.Errors.CompileError: util_cython.pyx

Why is Cython failing to convert _Result_tuple?

Secondly, how do I define a cython function to accept lists (or arrays; anything that supports __iter__), and copy their contents into _FFIArray structs, so I can call call_convert_bng?

The following is untested, because I don't have the library code and have had to take some guesses as to how it works. But it should give you an idea as to how you go about it.

I'd start by using a Python array type to store your input/output. Either the standard library array type, or numpy arrays. These store the arrays continuously in memory (like C would) and so provided the _FFIArray data attribute can just be pointed at this memory for input.

def convert_bng(double[::1] x, double[::1] y):# I'm assuming that the data is double, not float, but it's easy changed# here the [::1] promises it's continuous in memorycdef _FFIArray x_ffi, y_ffi# get a pointer to the data, and cast it to void*x_ffi.data = <void*>&x[0]x_ffi.len = x.shape[0] # possibly *sizeof(double) - depends on the C api# repeat for yy_ffi.data = <void*>&y[0]y_ffi.len = y.shape[0]cdef _Result_Tuple result = convert_to_bng_threaded(x_ffi, y_ffi)# get data pointers for the two result arrayscdef double* e_ptr = <double*>(result.e.data)cdef double* n_ptr = <double*>(result.n.data)# now view your output arrays using memoryviews# you need to tell it the length (this is how many doubles the contain)cdef double[::1] e = <double[:result.e.len:1]>e_ptrcdef double[::1] n = <double[:result.n.len:1]>n_ptr# create a numpy copy of the two arraysimport numpy as npe_numpy = np.copy(e)n_numpy = np.copy(n)# you can now free your two returned arrays# I assume this is done with drop_float_arraydrop_float_array(result.e,result.n)# return as tuple containing two arrays to pythonreturn e_numpy, n_numpy