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3

I've found the following puzzling behavior with NumPy and a custom dtype for an ndarray:

import numpy as np

# Make a custom dtype with a single triplet of floats (my actual dtype has other
# components, but this suffices to demonstrate the problem.
dt = np.dtype([('a', np.float64, 3)])

# Make a zero array with this dtype:
points = np.zeros((4, 4), dtype=dt)

# Try to edit an entry:
points[0][0]['a'] = np.array([1, 1, 1])

print points[0][0]['a']

Now, this comes back as containing not [1. 1. 1.] as I would expect, but instead [1. 0. 0.], only performing the assignment on the first coordinate. I can get around this by performing the assignment coordinate-wise, but that seems unnecessary given that the full assignment should certainly be the default behavior in this case.

Any thoughts on what's going on here?

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2 Answers

up vote 1 down vote accepted

There are many method to assign points, if you want your method works:

points[0][0]['a'][:] = np.array([1, 1, 1])

or:

points[0,0]['a'][:] = np.array([1, 1, 1])

because points[0,0]['a'] is an array, if you want to change the content of the array, you shoud use index.

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Just what I was looking for, thank you. –  Tim Kunisky Aug 30 '11 at 14:16
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up vote 2 down vote

If you change the ordering of the indices, like this: points['a'][0][0] = np.array([1, 1, 1]), it works ok for me (python 2.6.5, numpy 1.3.0 on Ubuntu 10.04). I wish I knew why.

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Works for me too. Though what I really would have wanted to do would be something like: p = points[0][0] p['a'] = np.array([1, 1, 1]) And other manipulations on p if necessary. –  Tim Kunisky Aug 28 '11 at 13:58
    
@Tim: AFAIK, it seems natural to first specify the named column, and only then go for the numeric indices. Thus way, ability of writing either points['a'][0] or points[0]['a'] (which works for dtype-ed arrays of PODs) feels like a free lunch to me. –  Zhenya Aug 28 '11 at 17:24
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