Return variable length array in Numpy C-extension?

Question

I have made some Numpy C-extensions before with great help from this site, but as far as I can see the returned parameters are all fixed length.

Is there any way to have a Numpy C-extension return a variable length numpy array instead?

Answer 1

You may find it easier to make numpy extensions in Cython using the Numpy C-API which simplifies the process as it allows you to mix python and c objects. In that case there is little difficult about making a variable length array, you can simply specify an array with an arbitrary shape.

The Cython numpy tutorial is probably the best source on this topic.

For example, here is a function I recently wrote:

import numpy as np
cimport numpy as np
cimport cython

dtype = np.double
ctypedef double dtype_t

np.import_ufunc()
np.import_array()

def ewma(a, d, axis):
    #Calculates the exponentially weighted moving average of array a along axis using the parameter d.
    cdef void *args[1]

    cdef double weight[1]
    weight[0] = <double>np.exp(-d)


    args[0] = &weight[0]

    return apply_along_axis(&ewma_func, np.array(a, dtype = float), np.double, np.double, False, &(args[0]), <int>axis)

cdef void ewma_func(int n, void* aData,int astride, void* oData, int ostride, void** args):
    #Exponentially weighted moving average calculation function 

    cdef double avg = 0.0
    cdef double weight = (<double*>(args[0]))[0]
    cdef int i = 0

    for i in range(n): 

        avg = (<double*>((<char*>aData) + i * astride))[0]*weight + avg * (1.0 - weight) 


        (<double*>((<char*>oData) + i * ostride))[0] = avg  

ctypedef void (*func_1d)(int, void*, int, void*, int, void **)

cdef apply_along_axis(func_1d function, a, adtype, odtype, reduce,  void** args, int axis):
    #generic function for applying a cython function along a particular dimension

    oshape = list(a.shape)

    if reduce :
        oshape[axis] = 1

    out = np.empty(oshape, odtype)

    cdef np.flatiter ita, ito

    ita = np.PyArray_IterAllButAxis(a,   &axis)
    ito = np.PyArray_IterAllButAxis(out, &axis)

    cdef int axis_length = a.shape[axis]
    cdef int a_axis_stride = a.strides[axis]
    cdef int o_axis_stride = out.strides[axis]

    if reduce: 
        o_axis_stride = 0

    while np.PyArray_ITER_NOTDONE(ita):

        function(axis_length, np.PyArray_ITER_DATA (ita), a_axis_stride, np.PyArray_ITER_DATA (ito), o_axis_stride, args)

        np.PyArray_ITER_NEXT(ita)
        np.PyArray_ITER_NEXT(ito)

    if reduce:  
        oshape.pop(axis)
        out.shape = oshape

    return out  

If this doesn't suit you, there is a function for making a new empty array with arbitrary shape (link).

Answer 2

I am interpreting your question to mean "I have a function that takes a NumPy array of length n, but it will return another array of length m different from n." If that is the case, you will need to malloc a new C array in the extension, e.g.

new_array = malloc(m * sizeof(int64)); // or whatever your data type is

then create a new NumPy array with that. This example assumes a 1D array:

int npy_intp dims[1];
dims[0] = m;
PyArrayObject *out = (PyArrayObject *)PyArray_SimpleNewFromData(1,          // 1D array
                                                                dims,       // dimensions
                                                                NPY_INT64,  // type
                                                                new_array);
PyArray_ENABLEFLAGS(out, NPY_ARRAY_OWNDATA);

Then return the new array. The important part here is to set the NPY_ARRAY_OWNDATA flag so that the memory you allocated is freed when the Python object is garbage collected.