Ah sorry, I hadn't read carefully enough what you were trying to achieve. I think the double repeat solution looks like your best option then.
-=- Olivier 2011/12/3 Robin Kraft <rkra...@gmail.com> > 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 > > > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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