This PR changes the behaviour of masked record arrays. The rationale makes
sense to me, but I don't actually use this functionality. Any masked array
users want to chime in?
-n
---------- Forwarded message ----------
From: "Thomas Robitaille" <notificati...@github.com>
Date: 11 Oct 2012 19:35
Subject: [numpy] When accessing MaskedArray rows, always return an mvoid
object (#483)
To: "numpy/numpy" <nu...@noreply.github.com>
The current behavior of MaskedArray when accessing a row object is that if
all masks are False, the row is an np.void object, and if any are set to
True, it is a np.ma.core.mvoid object. The issue with this is that users
can access/modify masks for rows that already have at least one mask set to
True, but not for rows that have no masks set. For example:
In [1]: a = ma.array(zip([1,2,3]), mask=[0,1,0], dtype=[('x', int)])
In [2]: a[0].mask
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-2221dd3fce8c> in <module>()
----> 1 a[0].mask
AttributeError: 'numpy.void' object has no attribute 'mask'
In [3]: a[1].mask
Out[3]: (True,)
There is no reason why a row should behave differently whether any values
are masked or not, so this PR defaults to always returning an mvoid object,
otherwise mask values cannot be set for rows that start off with no mask
values set.
(Of course, the present implementation in Numpy also has a performance
impact - for arrays with many fields, each call to a row has to flatten the
mask of the whole record just to check what type to return, which is
inefficient. With this PR, row access should be faster for masked arrays.
But this is secondary.)
------------------------------
You can merge this Pull Request by running:
git pull https://github.com/astrofrog/numpy fix-masked-getitem
Or view, comment on, or merge it at:
https://github.com/numpy/numpy/pull/483
Commit Summary
- When accessing MaskedArray rows, always return an mvoid object
File Changes
- *M* numpy/ma/core.py (10)
Patch Links
- https://github.com/numpy/numpy/pull/483.patch
- https://github.com/numpy/numpy/pull/483.diff
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