On Fri, Sep 30, 2011 at 8:03 AM, John Salvatier <jsalvati@u.washington.edu> wrote:
Using nditer, is it possible to manually handle dimensions  with different lengths? 

For example, lets say I had an array A[5, 100] and I wanted to sample every 10 along the second axis so I would end up with an array B[5,10]. Is it possible to do this with nditer, handling the iteration over the second axis manually of course (probably in cython)?

I want something like this (modified from http://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#putting-the-inner-loop-in-cython)

def sum_squares_cy(arr):
    cdef np.ndarray[double] x
    cdef np.ndarray[double] y
    cdef int size
    cdef double value
    cdef int j

    axeslist = list(arr.shape)
    axeslist[1] = -1

    out = zeros((arr.shape[0], 10))
    it = np.nditer([arr, out], flags=['reduce_ok', 'external_loop',
                                      'buffered', 'delay_bufalloc'],
                op_flags=[['readonly'], ['readwrite', 'no_broadcast']],
                op_axes=[None, axeslist],
                op_dtypes=['float64', 'float64'])
    it.operands[1][...] = 0
    for xarr, yarr in it:
        x = xarr
        y = yarr
        size = x.shape[0]
        j = 0
        for i in range(size):
           #some magic here involving indexing into x[i] and y[j]
    return it.operands[1]

Does this make sense? Is it possible to do?

I'm not sure I understand precisely what you're asking. Maybe you could reshape A to have shape [5, 10, 10], so that one of those 10's can match up with the 10 in B, perhaps with the op_axes?


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