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