Using Cython to wrap a c++ template to accept any numpy array

arrays, c++, cython, numpy, python

Solution

Yes, you want to use a fused type to have Cython call the sorting template for the appropriate specialization of the template. Here's a working example for all non-complex data types that does this with `std::sort`.

# cython: wraparound = False
# cython: boundscheck = False

cimport cython

cdef extern from "<algorithm>" namespace "std":
    cdef void sort[T](T first, T last) nogil

ctypedef fused real:
    cython.char
    cython.uchar
    cython.short
    cython.ushort
    cython.int
    cython.uint
    cython.long
    cython.ulong
    cython.longlong
    cython.ulonglong
    cython.float
    cython.double

cpdef void npy_sort(real[:] a) nogil:
    sort(&a[0], &a[a.shape[0]-1])

Problem

I'm trying to wrap a parallel sort written in c++ as a template, to use it with numpy arrays of any numeric type. I'm trying to use Cython to do this. My problem is that I don't know how to pass a pointer to the numpy array data (of a correct type) to a c++ template. I believe I should use fused dtypes for this, but I don't quite understand how. The code in .pyx file is below ``` # importing c++ template cdef extern from "test.cpp": void inPlaceParallelSort[T](T* arrayPointer,int arrayLength) def sortNumpyArray(np.ndarray a): # This obviously will not work, but I don't know how to make it work. inPlaceParallelSort(a.data, len(a)) ``` In the past I did similar tasks with ugly for-loops over all possible dtypes, but I believe there should be a better way to do this.

Original source