> On 5 Jul 2017, at 19:05, Stephan Hoyer <sho...@gmail.com> wrote:
>
> On Wed, Jul 5, 2017 at 10:40 AM, Chris Barker <chris.bar...@noaa.gov
> <mailto:chris.bar...@noaa.gov>> wrote:
> Along those lines, there was some discussion of having a set of utilities (or
> maybe eve3n an ABC?) that would make it easier to create a ndarray-like
> object.
>
> That is, the boilerplate needed for multi-dimensional indexing and slicing,
> etc...
>
> That could be a nice little sprint-able project.
>
> Indeed. Let me highlight a few mixins
> <https://github.com/pydata/xarray/blob/6a20f917041abf53bcb35e210d59f5b331211012/xarray/core/utils.py#L381-L425>
> that I wrote for xarray that might be more broadly useful. The challenge
> here is that there are quite a few different meanings to "ndarray-like", so
> mixins really need to be mix-and-match-able. But at least defining a base
> list of methods to implement/override would be useful.
>
> In NumPy, this could go along with NDArrayOperatorsMixins in
> numpy/lib/mixins.py
> <https://github.com/numpy/numpy/blob/14cd918c651d72f4c2a8681093e114f01d5bdc36/numpy/lib/mixins.py>
Slightly off topic, but as someone who has just spent a fair amount of time
implementing various
subclasses of nd-array, I am interested (and a little concerned), that the
consensus is not to use
them. Is there anything available which explains why this is the case and what
the alternatives
are?
Ben
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