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I want to slice a multidimensional numpy array (>2 dimensions) along 2 of its axes using index slicing. What are the rules for where each of its original dimensions end up?

To illustrate my problem, let me provide an example. Say we have a 4D array:

import numpy as np

a = np.arange(2*3*4*5).reshape(2,3,4,5)

I'll create a tuple of indices using numpy.where, for slicing along axes 1 and 3:

mask = np.where(np.random.rand(3,5) > 0.5)

This will pick out random slices from my array a. Let's say it returned tuples of length 7. To preserve the remaining dimensions I will use slice(None) objects:

b = a[(slice(None), mask[0], slice(None), mask[1])]

This changed the shape:

>>> a.shape
(2, 3, 4, 5)
>>> b.shape
(7, 2, 4)

The axes that were untouched (i.e. sliced using the slice(None) object) appear to have been preserved, whereas the sliced axes are destroyed and the resulting axis is moved to the front.

However, this is not always the case. When I apply a mask to axes 1 and 2:

mask2 = np.where(np.random.rand(3,4) > 0.5)
c = a[(slice(None), mask[0], mask[1], slice(None))]

I observe the following (numpy.where has returned tuples of length 7 again):

>>> c.shape
(2, 7, 5)

The axis resulting from the axes that have been destroyed by the slicing did not move to the front this time.

My guess is that it is related to whether the sliced axes are adjacent or not, but I want to know from what rules this behavior emerges.

Haminaa
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  • There's a documented complication when mixing slices (basic indexing) and masks (advanced indexing). The slice dimensions may be moved to the end – hpaulj Dec 07 '18 at 15:56
  • Afaik using a boolean mask as index is not called "slicing" but advanced indexing. Slicing refers to selecting some kind of a range of times, let's say `4:7` or `slice(4, 7)`. Slicing should preserve the dimensions of the array, while advanced indexing most probably will reduce them. – JE_Muc Dec 07 '18 at 15:58
  • You might find my answer here helpful: https://stackoverflow.com/questions/10921893/numpy-sorting-a-multidimensional-array-by-a-multidimensional-array/10922358#10922358. – user545424 Dec 07 '18 at 17:17

1 Answers1

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https://docs.scipy.org/doc/numpy-1.15.4/reference/arrays.indexing.html#combining-advanced-and-basic-indexing

Your where masks will produce a 1d (7,) shape array if applied to a 2d array, the values where the condition is true. You phrase that as 'destroying' a pair of axes.

In the second case that 7 can be placed between the 2 and 5.

But in the first it's ambiguous because of the slice in the middle (the non adjacency) - the fall back rule is to put it first, and order the slices after. In other words, instead of trying to choose between a (2,7,4) and (2,4,7) order, it chooses (7,2,4).

The ambiguity is clear in this case, and the default reasonable. It's more complicated with one or more of the dimensions is eliminated by a scalar index.

hpaulj
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