将 Cython 中的 numpy 数组传递给需要动态分配数组的 C 函数

Question

I have some C code that has the following declaration:

int myfunc(int m, int n, const double **a, double **b, double *c);

So a is a constant 2D array, b is a 2D array, and c is a 1D array, all dynamically allocated. b and c do not need to be anything specifically before they are passed to myfunc , and should be understood as output information. For the purposes of this question, I'm not allowed to change the declaration of myfunc .

Question 1: How do I convert a given numpy array a_np into an array a with the format required by this C function, so that I can call this C function in Cython with a ?

Question 2: Are the declarations for b and c below correct, or do they need to be in some other format for the C function to understand them as a 2D and 1D array (respectively)?

My attempt:

myfile.pxd

cdef extern from "myfile.h":
    int myfunc(int p, int q, const double **a, double **b, double *c)

mytest.pyx

cimport cython
cimport myfile
import numpy as np
cimport numpy as np

p = 3
q = 4
cdef:
    double** a = np.random.random([p,q])
    double** b
    double* c

myfile.myfunc(p, q, a, b, c)

Then in iPython I run

import pyximport; pyximport.install()
import mytest

The line with the definition of a gives me the error message Cannot convert Python object to 'double **' . I don't get any error messages regarding b or c , but since I'm unable to run the C function at this time, I'm not sure the declarations of b and c are written correctly (that is, in a way that will enable the C function to output a 2D and a 1D array, respectively).

Other attempts: I've also tried following the solution here , but this doesn't work with the double-asterisk type of arrays I have in the myfunc declaration. The solution here does not apply to my task because I can't change the declaration of myfunc .

Answer 1

Create a helper array in cython

To get a double** from a numpy array, you can create a helper-array of pointers in your *.pyx file. Further more, you have to make sure that the numpy array has the correct memory layout. (It might involve creating a copy)

Fortran order

If your C-function expects fortran order (all x-coordinates in one list, all y coordinates in another list, all z-coordinates in a third list, if your array a corresponds to a list of points in 3D space)

N,M = a.shape
# Make sure the array a has the correct memory layout (here F-order)
cdef np.ndarray[double, ndim=2, mode="fortran"] a_cython =
                         np.asarray(a, dtype = float, order="F")
#Create our helper array
cdef double** point_to_a = <double **>malloc(M * sizeof(double*))
if not point_to_a: raise MemoryError
try:
    #Fillup the array with pointers
    for i in range(M): 
        point_to_a[i] = &a_cython[0, i]
    # Call the C function that expects a double**
    myfunc(... &point_to_a[0], ...)
finally:
    free(point_to_a)

C-order

If your C-function expects C-order ([x1,y1,z1] is the first list, [x2,y2,z2] the second list for a list of 3D points):

N,M = a.shape
# Make sure the array a has the correct memory layout (here C-order)
cdef np.ndarray[double, ndim=2, mode="c"] a_cython =
                         np.asarray(a, dtype = float, order="C")
#Create our helper array
cdef double** point_to_a = <double **>malloc(N * sizeof(double*))
if not point_to_a: raise MemoryError
try:
    for i in range(N): 
        point_to_a[i] = &a_cython[i, 0]
    # Call the C function that expects a double**
    myfunc(... &point_to_a[0], ...)
finally:
    free(point_to_a)

Answer 2

Reply 1: You can pass NumPy array via Cython to C using the location of the start of the array (see code below).

Reply 2: Your declarations seem correct but I don't use this approach of explicit memory management. You can use NumPy to declare cdef -ed arrays.

Use

cdef double[:,::1] a = np.random.random([p, q])
cdef double[:,::1] b = np.empty([p, q])
cdef double[::1] b = np.empty(q)

Then pass &a[0] , the location of the start of the array, to your C function. The ::1 is to ensure contiguousness.

A good reference for this is Jake Vanderplas' blog: https://jakevdp.github.io/blog/2012/08/08/memoryview-benchmarks/

Finally, typically one creates functions in Cython and calls them in Python, so your Python code would be:

import pyximport; pyximport.install()
import mytest
mytest.mywrappedfunc()

where mywrappedfunc is a Python ( def and not cdef ) function defined in the module that can do the array declaration show above.