On Fri, Apr 20, 2012 at 2:15 PM, Andre Martel <soucoupevola...@yahoo.com>wrote:
> What would be the best way to remove the maximum from a cube and > "collapse" the remaining elements along the z-axis ? > For example, I want to reduce Cube to NewCube: > > >>> Cube > array([[[ 13, 2, 3, 42], > [ 5, 100, 7, 8], > [ 9, 1, 11, 12]], > > [[ 25, 4, 15, 1], > [ 17, 30, 9, 20], > [ 21, 2, 23, 24]], > > [[ 1, 2, 27, 28], > [ 29, 18, 31, 32], > [ -1, 3, 35, 4]]]) > > NewCube > > array([[[ 13, 2, 3, 1], > [ 5, 30, 7, 8], > [ 9, 1, 11, 12]], > > [[ 1, 2, 15, 28], > [ 17, 18, 9, 20], > [ -1, 2, 23, 4]]]) > > I tried with argmax() and then roll() and delete() but these > all work on 1-D arrays only. Thanks. > > Actually, those commands do work with n-dimensional arrays, but you'll have to specify the axis (the default for all these functions is `axis=None` which tell the function to operate on flattened the arrays). If you don't care about the order of the "collapse", you can just do a simple sort (and drop the last---i.e. max---sub-array): >>> np.sort(cube, axis=0)[:2] If you need to keep the order, you can probably use some combination of `np.argsort` and `np.choose`. Cheers, -Tony
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