Hi again. Thanks, everyone, for your quick replies. Travis Oliphant wrote:
amcmorl wrote:
Hi all,
I'm having a bit of trouble getting my head around numpy's indexing capabilities. A quick summary of the problem is that I want to lookup/index in nD from a second array of rank n+1, such that the last (or first, I guess) dimension contains the lookup co-ordinates for the value to extract from the first array. Here's a 2D (3,3) example:
In [12]:print ar [[ 0.15 0.75 0.2 ] [ 0.82 0.5 0.77] [ 0.21 0.91 0.59]]
In [24]:print inds [[[1 1] [1 1] [2 1]]
[[2 2] [0 0] [1 0]]
[[1 1] [0 0] [2 1]]]
then somehow return the array (barring me making any row/column errors): In [26]: c = ar.somefancyindexingroutinehere(inds)
You can do this with "fancy-indexing". Obviously it is going to take some time for people to get used to this idea as none of the responses yet suggest it. But the following works. c = ar[inds[...,0],inds[...,1]]
gives the desired effect.
Thus, your simple description c[x,y] = ar[inds[x,y,0],inds[x,y,1]] is a text-book description of what fancy-indexing does.
Great. Turns out I wasn't too far off then. I've written a quick function of my own that extends the fancy indexing to nD: def fancy_index_nd(ar, ind): evList = ['ar['] for i in range(len(ar.shape)): evList = evList + [' ind[...,%d]' % i] if i < len(ar.shape) - 1: evList = evList + [","] evList = evList + [' ]'] return eval(''.join(evList)) 1) Am I missing a simpler way to extend the fancy-indexing to n-dimensions? If not... 2) this seems (conceptually) that it might be a little faster than the routines that have to calculate a flat index. Hopefully it could be of use to people. Any thoughts? Cheers, Angus -- Angus McMorland email a.mcmorland@auckland.ac.nz mobile +64-21-155-4906 PhD Student, Neurophysiology / Multiphoton & Confocal Imaging Physiology, University of Auckland phone +64-9-3737-599 x89707 Armourer, Auckland University Fencing Secretary, Fencing North Inc.