On 01/22/2018 10:53 AM, josef.p...@gmail.com wrote:
On Sun, Jan 21, 2018 at 9:48 PM, Allan Haldane <allanhald...@gmail.com
In many examples where I used structured dtypes a long time ago,
switched between consistent views as either a standard array of subsets
or as .structured dtypes.
For this usecase it wouldn't matter whether a[['a', 'c']] returns a view
or copy, as long as we can get the second view that is consistent with
the selected part of the memory. This would also be independent of
whether numpy pads internally and adjusts the strides if possible or not.
np.__version__
'1.11.2'
a = np.ones(5, dtype=[('a', 'i8'), ('b', 'f8'), ('c', 'f8')])
a
array([(1, 1.0, 1.0), (1, 1.0, 1.0), (1, 1.0, 1.0), (1, 1.0, 1.0),
(1, 1.0, 1.0)],
dtype=[('a', '<i8'), ('b', '<f8'), ('c', '<f8')])
a[['b', 'c']].view(('f8', 2)).dtype
dtype('float64')
Thanks for a real example to think about. I just want to note that I
thought of another way to "fix" this for 1.15 which does not involve
"pack_fields", which is
a[['b', 'c']].astype('f8,f8').view(('f8', 2))
Which is back-compatible will numpy back to 1.7, I think.
So that's another option to ease the transition.
Allan
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