Not that I’m against different “levels” of ndarray granularity, but I just don’t want it to introduce complexity for the end-user. For example, it would be unreasonable to expect the end-user to check for all parts of the interface that they need support for separately.
Keeping this in view; different levels only make sense if and only if they are strict sub/supersets of each other, so the user can just check for the highest level of compatibility they require, but even then they would need to learn about the different “levels". PS, thanks for putting this together! I was thinking of doing it this weekend but you beat me to it and covered aspects I wouldn’t have thought of. The name “asarraylike” appeals to me, as does a “custom=“ kwarg for asanyarray. Sent from Astro <https://www.helloastro.com> for Mac On Mar 9, 2018 at 02:51, Juan Nunez-Iglesias <jni.s...@gmail.com> wrote: On Fri, Mar 9, 2018, at 5:56 AM, Stephan Hoyer wrote: Marten's case 1: works exactly like ndarray, but stores data differently: parallel arrays (e.g., dask.array), sparse arrays (e.g., https://github.com/pydata/sparse), hypothetical non-strided arrays (e.g., always C ordered). Two other "hypotheticals" that would fit nicely in this space: - the Open Connectome folks (https://neurodata.io) proposed linearising indices using space-filling curves, which minimizes cache misses (or IO reads) for giant volumes. I believe they implemented this but can't find it currently. - the N5 format for chunked arrays on disk: https://github.com/saalfeldlab/n5 Finally for the name, what about `asduckarray`? Thought perhaps that could be a source of confusion, and given the gradation of duck array like types. I suggest that the name should *not* use programmer lingo, so neither "abstract" nor "duck" should be in there. My humble proposal is "arraylike". (I know that this term has included things like "list-of-list" before but only in text, not code, as far as I know.) _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
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