To me, this sounds like a common use-case, but I couldn't find the proper function/thread for it, yet.
I have two numpy arrays, one is a sequence of triplets and the other the associated sequence of indices. I want to create a 1-dim array of equal sequence length, composed of the mapping items according to their index.
Example:
mapping = np.array(((25, 120, 240), (18, 177, 240), (0, 0, 0), (10, 120, 285)))
indices = np.array((0, 1, 0, 0))
print "mapping:", mapping
print "indices:", indices
print "mapped:", mapping[indices]
Which produces the following output:
mapping: [[ 25 120 240]
[ 18 177 240]
[ 0 0 0]
[ 10 120 285]]
indices: [0 1 0 0]
mapped: [[ 25 120 240]
[ 18 177 240]
[ 25 120 240]
[ 25 120 240]]
Of course, this approach takes the whole mapping array as one mapping, not as a list of mappings, returning only the 1st or 2nd inner mapping, according to the indices array. But what I was looking for is this:
mapped: [25 177 0 10]
... which is made from the 1st item of the 1st mapping, the 2nd of the 2nd mapping and the first of the 3rd and 4th mapping.
Is there a lean way to do it with numpy functionality alone, without external looping and without excess of memory usage for temporary arrays?
I think you are looking for this part of numpy's documentation on indexing.
In [17]: mapping[(np.arange(indices.shape[-1]),indices)]
Out[17]: array([ 25, 177, 0, 10])
This create a temporary array (np.arange) but it is 1-dimensional and I couldn't think of anything better.