Re: [Numpy-discussion] Indexing arrays with arrays

[Bill B. <wb...@gm...>]

I read the chapter in your book and sort of vaguely understand what it does
now.

Take an array A and index array ind:
>>> A
array([[ 0,  5, 10, 15],
       [ 1,  6, 11, 16],
       [ 2,  7, 12, 17],
       [ 3,  8, 13, 18],
       [ 4,  9, 14, 19]])
>>> ind
array([[1, 3, 4],
       [0, 2, 1]])

And you get
>>> A[ind]
array([[[ 1,  6, 11, 16],
        [ 3,  8, 13, 18],
        [ 4,  9, 14, 19]],

       [[ 0,  5, 10, 15],
        [ 2,  7, 12, 17],
        [ 1,  6, 11, 16]]])

In this case it's roughly equivalent to [ A[row] for row in ind ].

>>> num.asarray( [ A[r] for r in ind ] )
array([[[ 1,  6, 11, 16],
        [ 3,  8, 13, 18],
        [ 4,  9, 14, 19]],

       [[ 0,  5, 10, 15],
        [ 2,  7, 12, 17],
        [ 1,  6, 11, 16]]])
>>>

So I guess it could be useful if you want to take a bunch of different
random samples of your data and stack them all up.  E.g. you have a
(1000,50) shaped grid of data, and you want to take N random samplings, eac=
h
consisting of 100 rows from the original grid, and then put them all
together into an (N,100,50) array.   Or say you want to make a stack of
sliding windows on the data like rows 0-5, then rows 1-6, then rows 2-7, et=
c
to make a big (1000-5,5,50) array. Might be useful for that kind of thing.

But thinking about applying an index obj of shape (2,3,4) to a
(10,20,30,40,50) shaped array just makes my head hurt. :-)

Does anyone actually use it, though?  I also found it unexpected that A[
(ind[0], ind[1] ) ]  doesn't do the same thing as A[ind]  when ind.shape=3D=
(
A.ndim, N).
List of array --  as in A[ [ind[0], ind[1]] ] -- seems to act just like
tuple of array also.

--bill