Hi all,
So, in advanced indexing, numpy decides where to put new axes based on
whether the "advanced indices" are all next to each other.
>>> np.random.random((3,4,5,6,7,8))[:, [[0,0],[0,0]], 1, :].shape
(3, 2, 2, 6, 7, 8)
>>> np.random.random((3,4,5,6,7,8))[:, [[0,0],[0,0]], :, 1].shape
(2, 2, 3, 5, 7, 8)
In creating a wrapper type around arrays, I'm finding myself needing to
suppress this behavior, so that the new axes consistently appear in the
front. I thought of a dumb hat trick:
def index(x, indices):
return x[(True, None) + indices]
Which certainly gets the new dimensions where I want them, but it
introduces a ghost dimension of 1 (and sometimes two such dimensions!) in a
place where I'm not sure I can easily find it.
>>> np.random.random((3,4,5,6,7,8))[True, None, 1].shape
(1, 1, 4, 5, 6, 7, 8)
>>> np.random.random((3,4,5,6,7,8))[True, None, :, [[0,0],[0,0]], 1,
:].shape
(2, 2, 1, 3, 6, 7, 8)
>>> np.random.random((3,4,5,6,7,8))[True, None, :, [[0,0],[0,0]], :,
1].shape
(2, 2, 1, 3, 5, 7, 8)
any better ideas?
---
Michael
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