How to filter numpy array by list of indices?

Question

I have a numpy array, filtered__rows, comprised of LAS data [x, y, z, intensity, classification]. I have created a cKDTree of points

Answer 1

Using Docs: https://docs.scipy.org/doc/numpy-1.13.0/user/basics.indexing.html The following implementation should work for arbitrary number of dimensions/shapes for some numpy ndarray.

First we need a multi-dimensional set of indexes and some example data:

import numpy as np
y = np.arange(35).reshape(5,7)
print(y) 
indexlist = [[0,1], [0,2], [3,3]]
print ('indexlist:', indexlist)

To actually extract the intuitive result the trick is to use a Transpose:

indexlisttranspose = np.array(indexlist).T.tolist()
print ('indexlist.T:', indexlisttranspose)
print ('y[indexlist.T]:', y[ tuple(indexlisttranspose) ])

Makes the following terminal output:

y: [[ 0  1  2  3  4  5  6]
 [ 7  8  9 10 11 12 13]
 [14 15 16 17 18 19 20]
 [21 22 23 24 25 26 27]
 [28 29 30 31 32 33 34]]
indexlist: [[0, 1], [0, 2], [3, 3]]
indexlist.T: [[0, 0, 3], [1, 2, 3]]
y[indexlist.T]: [ 1  2 24]

The tuple... fixes a future warning which we can cause like so:

print ('y[indexlist.T]:', y[ indexlisttranspose ])

FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`.
In the future this will be interpreted as an array index,
`arr[np.array(seq)]`, which will result either in an error or a
different result.
    print ('y[indexlist.T]:', y[ indexlisttranspose ])
y[indexlist.T]: [ 1  2 24]