# [Numpy-discussion] nditer: possible to manually handle dimensions with different lengths?

John Salvatier jsalvati@u.washington....
Mon Oct 3 11:03:14 CDT 2011

```Thanks mark! I think that's exactly what I'm looking for. We even had a
http://mail.scipy.org/pipermail/numpy-discussion/2011-January/054421.html).

I didn't find any documentation, I will try to add some once I understand
how it works better.

John

On Sat, Oct 1, 2011 at 2:53 PM, Mark Wiebe <mwwiebe@gmail.com> wrote:

> On Sat, Oct 1, 2011 at 1:45 PM, John Salvatier <jsalvati@u.washington.edu>wrote:
>
>> I apologize, I picked a poor example of what I want to do. Your suggestion
>> would work for the example I provided, but not for a more complex example.
>> My actual task is something like a "group by" operation along a particular
>> axis (with a known number of groups).
>>
>> Let me try again: What I would like to be able to do is to specify some of
>> the iterator dimensions to be handled manually by me. For example lets say I
>> have some kind of a 2d smoothing algorithm. If I start with an array of
>> shape [a,b,c,d] and I'd like to do the 2d smoothing over the 2nd and 3rd
>> dimensions, I'd like to be able to tell nditer to do normal broadcasting and
>> iteration over the 1st and 4th dimensions but leave iteration over the 2nd
>> and 3rd dimensions to me and my algorithm. Each iteration of nditer would
>> give me a 2d array to which I apply my algorithm. This way I could write
>> more arbitrary functions that operate on arrays and support broadcasting.
>>
>> Is clearer?
>>
>
> Maybe this will work for you:
>
> In [15]: a = np.arange(2*3*4*5).reshape(2,3,4,5)
>
> In [16]: it0, it1 = np.nested_iters(a, [[0,3], [1,2]],
> flags=['multi_index'])
>
> In [17]: for x in it0:
>    ....:     print it1.itviews[0]
>    ....:
> [[ 0  5 10 15]
>  [20 25 30 35]
>  [40 45 50 55]]
> [[ 1  6 11 16]
>  [21 26 31 36]
>  [41 46 51 56]]
> [[ 2  7 12 17]
>  [22 27 32 37]
>  [42 47 52 57]]
> [[ 3  8 13 18]
>  [23 28 33 38]
>  [43 48 53 58]]
> [[ 4  9 14 19]
>  [24 29 34 39]
>  [44 49 54 59]]
> [[ 60  65  70  75]
>  [ 80  85  90  95]
>  [100 105 110 115]]
> [[ 61  66  71  76]
>  [ 81  86  91  96]
>  [101 106 111 116]]
> [[ 62  67  72  77]
>  [ 82  87  92  97]
>  [102 107 112 117]]
> [[ 63  68  73  78]
>  [ 83  88  93  98]
>  [103 108 113 118]]
> [[ 64  69  74  79]
>  [ 84  89  94  99]
>  [104 109 114 119]]
>
> Cheers,
> Mark
>
>
>
>
>>
>>
>> On Fri, Sep 30, 2011 at 5:04 PM, Mark Wiebe <mwwiebe@gmail.com> wrote:
>>
>>> 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
>>>> )
>>>>
>>>> @cython.boundscheck(False)
>>>> 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_axes=[None, axeslist],
>>>>                 op_dtypes=['float64', 'float64'])
>>>>     it.operands[1][...] = 0
>>>>     it.reset()
>>>>     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?
>>>
>>> -Mark
>>>
>>>
>>>>
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>>>>
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>>
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