Does Cython offer any reasonably easy and efficient way to iterate Numpy arrays as if they were flat?

Question

Let\'s say I want to implement Numpy\'s

x[:] += 1

in Cython. I could write

@cython.boundscheck(False)
@cython.wraparoundcheck(F         


        

Answer 1

With numpy.ravel

numpy.ravel(a, order='C')

Return a contiguous flattened array.

A 1-D array, containing the elements of the input, is returned. A copy is made only if needed.

@cython.boundscheck(False)
@cython.wraparound(False)
def add1_ravel(np.ndarray xs):
    cdef unsigned long i
    cdef double[::1] aview

    narray = xs.ravel()
    aview = narray

    for i in range(aview.shape[0]):
        aview[i] += 1

    # return xs if the memory is shared
    if not narray.flags['OWNDATA'] or np.may_share_memory(xs, narray):
        return xs

    # otherwise new array reshaped
    shape = tuple(xs.shape[i] for i in range(xs.ndim))
    return narray.reshape(shape)