> 31 дек. 2016 г., в 2:09, Nicolas P. Rougier <nicolas.roug...@inria.fr> > написал(а): > >> >> On 30 Dec 2016, at 20:36, Alex Rogozhnikov <alex.rogozhni...@yandex.ru> >> wrote: >> >> Hi Nicolas, >> that's a very nice work! >> >>> Comments/questions/fixes/ideas are of course welcome. >> >> Boids example brought my attention too, some comments on it: >> - I find using complex numbers here very natural, this should speed up >> things and also shorten the code (rotating without einsum, etc.) >> - you probably can speed up things with going to sparse arrays >> - and you can go to really large numbers of 'birds' if you combine it with >> preliminary splitting of space into squares, thus analyze only birds from >> close squares >> >> Also I think worth adding some operations with HSV / HSL color spaces as >> those can be visualized easily e.g. on some photo. >> >> Thanks, >> Alex. > > > Thanks. > > I'm not sure to know how to use complex with this example. Could you > elaborate ?
Position and velocity are encoded by complex numbers. Rotation is multiplication by exp(i \phi), translating is adding a complex number. Distance = abs(x - y). I think, that's all operations you need, but maybe I miss something. > > For the preliminary splitting, a quadtree (scipy KDTree) could also help a > lot but I wanted to stick to numpy only. > A simpler square splitting as you suggest could make thing faster but require > some work. I'm not sure yet I see how to restrict analysis to close squares. > > Nicolas > > >> >> >> >>> 23 дек. 2016 г., в 12:14, Kiko <kikocorre...@gmail.com> написал(а): >>> >>> >>> >>> 2016-12-22 17:44 GMT+01:00 Nicolas P. Rougier <nicolas.roug...@inria.fr>: >>> >>> Dear all, >>> >>> I've just put online a (kind of) book on Numpy and more specifically about >>> vectorization methods. It's not yet finished, has not been reviewed and >>> it's a bit rough around the edges. But I think there are some material that >>> can be interesting. I'm specifically happy with the boids example that show >>> a nice combination of numpy and matplotlib strengths. >>> >>> Book is online at: http://www.labri.fr/perso/nrougier/from-python-to-numpy/ >>> Sources are available at: https://github.com/rougier/from-python-to-numpy >>> >>> >>> Comments/questions/fixes/ideas are of course welcome. >>> >>> Wow!!! Beautiful. >>> >>> Thanks for sharing. >>> >>> >>> >>> Nicolas >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> https://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org <mailto:NumPy-Discussion@scipy.org> > https://mail.scipy.org/mailman/listinfo/numpy-discussion > <https://mail.scipy.org/mailman/listinfo/numpy-discussion>
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