On Fri, Sep 27, 2013 at 8:27 AM, Sebastian Berg <sebastian@sipsolutions.net>wrote:
Hey,
since I am working on the indexing. I was wondering about a few smaller things:
* 0-d boolean array, `np.array(0)[True]` (will work now) would give np.array([0]) as a copy, instead of the original array. I guess I could add a FutureWarning or so, but I am not sure and overall the chance of creating bugs seems low.
(The boolean index should always add 1 dimension and here, remove 0 dimensions -> 1-d result.)
* All index operations return a view; never the object. This means that `v = arr[...]` is slightly slower. But since it does not affect `arr[...] = vals`, I think the speed implications are negligible.
* Does anyone have an idea if there is a way to change the subclass logic that view based item setting is implemented as: np.asarray(subclass[index]) = vals
I somewhat think the subclass should rather implement `__setitem__` instead of relying on numpy calling its `__getitem__`, but I don't see how it can be changed.
* Still thinking a bit about implementing a keepdims keyword or function, to handle matrix type logic mostly in the C-code.
And most importantly, is there any behaviour thing in the index machinery that is bugging you, which I may have forgotten until now?
- Sebastian
Boolean indexing could use a facelift. First, consider the following (albeit minor) annoyance:
import numpy as np a = np.arange(5) a[[True, False, True, False, True]] array([1, 0, 1, 0, 1]) b = np.array([True, False, True, False, True]) a[b] array([0, 2, 4])
Next, it would be nice if boolean indexing returned a view (wishful thinking, I know):
c = a[b] c array([0, 2, 4]) c[1] = 7 c array([0, 7, 4]) a array([0, 1, 2, 3, 4])
Cheers! Ben Root