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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