On Thu, Oct 4, 2012 at 12:23 PM, Jay Bourque <jay.bour...@continuum.io>wrote:
> Hey Anthony, thanks for the comments. > > On Mon, Oct 1, 2012 at 10:32 PM, Anthony Scopatz <scop...@gmail.com>wrote: > >> Hello Jay, >> >> Cool idea! I like to see work on structured arrays. Just a couple of >> questions: >> >> - Since there are already have ufuncs for primitive dtypes (int, >> float, etc), and you are just acting columnwise here, can't you write a >> single function which interprets the dtypes, gathers the approriate scalar >> ufuncs, and applies those down each field? Yes registering individual >> functions using your mechanism will be faster, but it will also be more >> work. >> >> I hadn't thought of this approach, but I suppose that would work okay too > for certain dtypes. You'd lose some flexibility with writing a ufunc for a > specific struct dtype though (for example maybe the ufunc shouldn't be > applied to all the fields in a record). Maybe this approach would be a good > default to use when a ufunc hasn't been registered for a struct dtype? > A sensible default was use case that I had in mind. I think that it is cool to be register your own ufuncs and I like what you have done there. So what I suggested is more of an addition than a strict criticism of work to date. > > >> >> - I didn't try this out myself, but will the normal numpy >> broadcasting rules apply here? >> >> I need to do more testing for this, but yes, I believe normal > broadcasting rules should work fine. > That'd be great! Be Well Anthony > > >> Be Well >> Anthony >> >> On Mon, Oct 1, 2012 at 10:36 AM, Jay Bourque <jay.bour...@continuum.io>wrote: >> >>> All, >>> >>> I've submitted the following pull request for NumPy: >>> >>> https://github.com/numpy/numpy/pull/462 >>> >>> This change allows ufuncs to be registered for structured arrays by >>> using a new API method PyUFunc_RegisterLoopForStructType. For example, a >>> ufunc could be registered to take two arrays of type 'u8,u8,u8' and return >>> an array of type 'u8,u8,u8'. I have a trivial example of this included in >>> my pull request, along with further details of my changes. I suspect there >>> might be a better way to do this, so any suggestions for improvements would >>> be welcome. >>> >>> Thanks, >>> -Jay >>> continuum.io >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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