[Numpy-discussion] Interleaved Arrays and

Anne Archibald peridot.faceted@gmail....
Tue Jun 16 21:43:53 CDT 2009


I'm not sure it's worth having a function to replace a one-liner
(column_stack followed by reshape). But if you're going to implement
this with slice assignment, you should take advantage of the
flexibility this method allows and offer the possibility of
interleaving "raggedly", that is, where the size of the arrays drops
at some point, so that you could interleave arrays of size 4, 4, and 3
to get one array of size 11. This allows split and join operations,
for example for multiprocessing. On the other hand you should also
include a documentation warning that this can be slow when
interleaving large numbers of small arrays.

Anne

2009/6/16 Neil Martinsen-Burrell <nmb@wartburg.edu>:
> On 2009-06-16 16:05 , Robert wrote:
>> Neil Martinsen-Burrell wrote:
>>> On 06/16/2009 02:18 PM, Robert wrote:
>>>>    >>>   n = 10
>>>>    >>>   xx = np.ones(n)
>>>>    >>>   yy = np.arange(n)
>>>>    >>>   aa = np.column_stack((xx,yy))
>>>>    >>>   bb = np.column_stack((xx+1,yy))
>>>>    >>>   aa
>>>> array([[ 1.,  0.],
>>>>           [ 1.,  1.],
>>>>           [ 1.,  2.],
>>>>           [ 1.,  3.],
>>>>           [ 1.,  4.],
>>>>           [ 1.,  5.],
>>>>           [ 1.,  6.],
>>>>           [ 1.,  7.],
>>>>           [ 1.,  8.],
>>>>           [ 1.,  9.]])
>>>>    >>>   bb
>>>> array([[ 2.,  0.],
>>>>           [ 2.,  1.],
>>>>           [ 2.,  2.],
>>>>           [ 2.,  3.],
>>>>           [ 2.,  4.],
>>>>           [ 2.,  5.],
>>>>           [ 2.,  6.],
>>>>           [ 2.,  7.],
>>>>           [ 2.,  8.],
>>>>           [ 2.,  9.]])
>>>>    >>>   np.column_stack((aa,bb))
>>>> array([[ 1.,  0.,  2.,  0.],
>>>>           [ 1.,  1.,  2.,  1.],
>>>>           [ 1.,  2.,  2.,  2.],
>>>>           [ 1.,  3.,  2.,  3.],
>>>>           [ 1.,  4.,  2.,  4.],
>>>>           [ 1.,  5.,  2.,  5.],
>>>>           [ 1.,  6.,  2.,  6.],
>>>>           [ 1.,  7.,  2.,  7.],
>>>>           [ 1.,  8.,  2.,  8.],
>>>>           [ 1.,  9.,  2.,  9.]])
>>>>    >>>   cc = _
>>>>    >>>   cc.reshape((n*2,2))
>>>> array([[ 1.,  0.],
>>>>           [ 2.,  0.],
>>>>           [ 1.,  1.],
>>>>           [ 2.,  1.],
>>>>           [ 1.,  2.],
>>>>           [ 2.,  2.],
>>>>           [ 1.,  3.],
>>>>           [ 2.,  3.],
>>>>           [ 1.,  4.],
>>>>           [ 2.,  4.],
>>>>           [ 1.,  5.],
>>>>           [ 2.,  5.],
>>>>           [ 1.,  6.],
>>>>           [ 2.,  6.],
>>>>           [ 1.,  7.],
>>>>           [ 2.,  7.],
>>>>           [ 1.,  8.],
>>>>           [ 2.,  8.],
>>>>           [ 1.,  9.],
>>>>           [ 2.,  9.]])
>>>>    >>>
>>>>
>>>>
>>>> However I feel too, there is a intuitive abbrev function like
>>>> 'interleave' or so missing in numpy shape_base or so.
>>>
>>> Using fancy indexing, you can set strided portions of an array equal to
>>> another array.  So::
>>>
>>> In [2]: aa = np.empty((10,2))
>>>
>>> In [3]: aa[:, 0] = 1
>>>
>>> In [4]: aa[:,1] = np.arange(10)
>>>
>>> In [5]: bb = np.empty((10,2))
>>>
>>> In [6]: bb[:,0] = 2
>>>
>>> In [7]: bb[:,1] = aa[:,1] # this works
>>>
>>> In [8]: cc = np.empty((20,2))
>>>
>>> In [9]: cc[::2,:] = aa
>>>
>>> In [10]: cc[1::2,:] = bb
>>>
>>> In [11]: cc
>>> Out[11]:
>>> array([[ 1.,  0.],
>>>          [ 2.,  0.],
>>>          [ 1.,  1.],
>>>          [ 2.,  1.],
>>>          [ 1.,  2.],
>>>          [ 2.,  2.],
>>>          [ 1.,  3.],
>>>          [ 2.,  3.],
>>>          [ 1.,  4.],
>>>          [ 2.,  4.],
>>>          [ 1.,  5.],
>>>          [ 2.,  5.],
>>>          [ 1.,  6.],
>>>          [ 2.,  6.],
>>>          [ 1.,  7.],
>>>          [ 2.,  7.],
>>>          [ 1.,  8.],
>>>          [ 2.,  8.],
>>>          [ 1.,  9.],
>>>          [ 2.,  9.]])
>>>
>>> Using this syntax, interleave could be a one-liner.
>>>
>>> -Neil
>>
>> that method of 'filling an empty with a pattern' was mentioned in
>> the other (general) interleaving question. It requires however a
>> lot of particular numbers and :'s in the code, and requires even
>> more statements which can hardly be written in functional style -
>> in one line?. The other approach is more jount, free of fancy
>> indexing assignments.
>
> jount?  I think that assigning to a strided index is very clear, but
> that is a difference of opinion.  All of the calls to np.empty are the
> equivalent of the column_stack's in your example.  I think that
> operations on segments of arrays are fundamental to an array-processing
> language such as NumPy.  Using ";" you can put as many of those
> statements as you would like one line. :)
>
>> The general interleaving should work efficiently in one like this:
>>
>> np.column_stack/concatenate((r,g,b,....), axis=...).reshape(..)
>>
>> But as all this is not intuitive, something like this should be in
>> numpy perhaps? :
>>
>> def interleave( tup_arrays, axis = None )
>
> Here is a minimally tested implementation.  If anyone really wants this
> for numpy, I'll gladly add comments and tests.  I couldn't figure out
> how to automatically find the greatest dtype, so I added an argument to
> specify, otherwise it uses the type of the first array.
>
> def interleave(arrays, axis=0, dtype=None):
>     assert len(arrays) > 0
>     first = arrays[0]
>     assert all([arr.shape == first.shape for arr in arrays])
>     new_shape = list(first.shape)
>     new_shape[axis] *= len(arrays)
>     if dtype is None:
>         new_dtype = first.dtype
>     else:
>         new_dtype = dtype
>     interleaved = np.empty(new_shape, new_dtype)
>     axis_slice = [slice(None, None, None)]*axis + \
>                  [slice(0,None,len(arrays))] + [Ellipsis]
>     for i, arr in enumerate(arrays):
>         axis_slice[axis] = slice(i, None, len(arrays))
>         interleaved[tuple(axis_slice)] = arr
>     return interleaved
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