# [Numpy-discussion] "upsample" or scale an array

Robin Kraft rkraft4 at gmail.com
Sat Dec 3 12:22:48 EST 2011

```That does repeat the elements, but doesn't get them into the desired order.

In [4]: print a
[[1 2]
[3 4]]

In [7]: np.tile(a, 4)
Out[7]:
array([[1, 2, 1, 2, 1, 2, 1, 2],
[3, 4, 3, 4, 3, 4, 3, 4]])

In [8]: np.tile(a, 4).reshape(4,4)
Out[8]:
array([[1, 2, 1, 2],
[1, 2, 1, 2],
[3, 4, 3, 4],
[3, 4, 3, 4]])

It's close, but I want to repeat the elements along the two axes, effectively stretching it by the lower right corner:

array([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 3, 4, 4],
[3, 3, 4, 4]])

It would take some more reshaping/axis rolling to get there, but it seems doable.

Anyone know what combination of manipulations would work with the result of np.tile?

-Robin

On Dec 3, 2011, at 11:05 AM, Olivier Delalleau wrote:

> You can also use numpy.tile
>
> -=- Olivier
>
> 2011/12/3 Robin Kraft

>> Thanks Warren, this is great, and even handles giant arrays just fine if you've got enough RAM.
>>
>> I also just found this StackOverflow post with another solution.
>>
>> a.repeat(2, axis=0).repeat(2, axis=1).
>> http://stackoverflow.com/questions/7525214/how-to-scale-a-numpy-array
>>
>> np.kron lets you do more, but for my simple use case the repeat() method is faster and more ram efficient with large arrays.
>>
>> In [3]: a = np.random.randint(0, 255, (2400, 2400)).astype('uint8')
>>
>> In [4]: timeit a.repeat(2, axis=0).repeat(2, axis=1)
>> 10 loops, best of 3: 182 ms per loop
>>
>> In [5]: timeit np.kron(a, np.ones((2,2), dtype='uint8'))
>> 1 loops, best of 3: 513 ms per loop
>>
>>
>> Or for a 43200x4800 array:
>>
>> In [6]: a = np.random.randint(0, 255, (2400*18, 2400*2)).astype('uint8')
>>
>> In [7]: timeit a.repeat(2, axis=0).repeat(2, axis=1)
>> 1 loops, best of 3: 6.92 s per loop
>>
>> In [8]: timeit np.kron(a, np.ones((2, 2), dtype='uint8'))
>> 1 loops, best of 3: 27.8 s per loop
>>
>> In this case repeat() peaked at about 1gb of ram usage while np.kron hit about 1.7gb.
>>
>> Thanks again Warren. I'd tried way too many variations on reshape and rollaxis, and should have come to the Numpy list a lot sooner!
>>
>> -Robin
>>
>>
>> On Dec 3, 2011, at 12:51 AM, Warren Weckesser wrote:
>>> On Sat, Dec 3, 2011 at 12:35 AM, Robin Kraft wrote:
>>>
>>> > I need to take an array - derived from raster GIS data - and upsample or
>>> > scale it. That is, I need to repeat each value in each dimension so that,
>>> > for example, a 2x2 array becomes a 4x4 array as follows:
>>> >
>>> > [[1, 2],
>>> >  [3, 4]]
>>> >
>>> > becomes
>>> >
>>> > [[1,1,2,2],
>>> >  [1,1,2,2],
>>> >  [3,3,4,4]
>>> >  [3,3,4,4]]
>>> >
>>> > It seems like some combination of np.resize or np.repeat and reshape +
>>> > rollaxis would do the trick, but I'm at a loss.
>>> >
>>> > Many thanks!
>>> >
>>> > -Robin
>>> >
>>>
>>>
>>> Just a day or so ago, Josef Perktold showed one way of accomplishing this
>>> using numpy.kron:
>>>
>>> In [14]: a = arange(12).reshape(3,4)
>>>
>>> In [15]: a
>>> Out[15]:
>>> array([[ 0,  1,  2,  3],
>>>        [ 4,  5,  6,  7],
>>>        [ 8,  9, 10, 11]])
>>>
>>> In [16]: kron(a, ones((2,2)))
>>> Out[16]:
>>> array([[  0.,   0.,   1.,   1.,   2.,   2.,   3.,   3.],
>>>        [  0.,   0.,   1.,   1.,   2.,   2.,   3.,   3.],
>>>        [  4.,   4.,   5.,   5.,   6.,   6.,   7.,   7.],
>>>        [  4.,   4.,   5.,   5.,   6.,   6.,   7.,   7.],
>>>        [  8.,   8.,   9.,   9.,  10.,  10.,  11.,  11.],
>>>        [  8.,   8.,   9.,   9.,  10.,  10.,  11.,  11.]])
>>>
>>>
>>> Warren
>>
>>
>
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