[Numpy-discussion] Logical indexing and higher-dimensional arrays.

Benjamin Root ben.root@ou....
Tue Feb 7 12:17:02 CST 2012


On Tue, Feb 7, 2012 at 11:11 AM, Jordi Gutiérrez Hermoso <jordigh@octave.org
> wrote:

> Consider the following. Is this a bug?
>
> Thanks,
> - Jordi G. H.
>
> -----------------------------------------------
> #!/usr/bin/python
>
> import numpy as np
>
> x = np.reshape(np.random.uniform(size=2*3*4), [2,3,4])
>
> idx = np.array([False, True, False, True])
> y = x[0,:,:];
>
> ## Why is this transposed?
> print x[0, :, idx].T == y[:, idx]
>
> ## This doesn't happen with non-boolean indexing
> print x[0, :, 1:3] == y[:, 1:3]


Funny things do happen when you mix boolean indexing with slicing/fancy
indexing.  This does seem strange to me:

>>> print x.shape
(2, 3, 4)
>>> print x[0, :, :].shape
(3, 4)
>>> print x[0, :, idx].shape
(2, 3)

# 1-D slices using regular indexing and slicing
>>> print x[0, :, 1]
[ 0.06500275  0.46899149  0.50125757]
>>> print x[0, :, 3]
[ 0.06500275  0.46899149  0.50125757]

# 2-D view using regular indexing, slicing and boolean indexing
>>> print x[0, :, idx]
[[ 0.06500275  0.46899149  0.50125757]
 [ 0.68811907  0.94795054  0.86839934]]

# 2-D view using indexing and slicing
>>> print x[0, :, 1:4]
[[ 0.06500275  0.95042819  0.68811907]
 [ 0.46899149  0.49388795  0.94795054]
 [ 0.50125757  0.04363919  0.86839934]]

The 1-D views makes sense for them to be of shape (3,), but the 2-D view is
inconsistent with the last result.  Could this be a bug related to how
boolean indexing tends to flatten the results?  Stacking flattened arrays
would yield the second result.

Ben Root
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