I don't think there's any real standard here. Just doing a github search reveals many different choices people have used:
https://github.com/search?l=Python&q=__array_priority__&type=Code&utf8=%E2%9C%93 On Mon, Jun 19, 2017 at 11:07 AM, Ilhan Polat <ilhanpo...@gmail.com> wrote: > Thank you. I didn't know that it existed. Is there any place where I can > get a feeling for a sane priority number compared to what's being done in > production? Just to make sure I'm not stepping on any toes. > > On Mon, Jun 19, 2017 at 5:36 PM, Stephan Hoyer <sho...@gmail.com> wrote: > >> I answered your question on StackOverflow: >> https://stackoverflow.com/questions/40694380/forcing-multipl >> ication-to-use-rmul-instead-of-numpy-array-mul-or-byp/44634634#44634634 >> >> In brief, you need to set __array_priority__ or __array_ufunc__ on your >> object. >> >> On Mon, Jun 19, 2017 at 5:27 AM, Ilhan Polat <ilhanpo...@gmail.com> >> wrote: >> >>> I will assume some simple linear systems knowledge but the question can >>> be generalized to any operator that implements __mul__ and __rmul__ >>> methods. >>> >>> Motivation: >>> >>> I am trying to implement a gain matrix, say 3x3 identity matrix, for >>> time being with a single input single output (SISO) system that I have >>> implemented as a class modeling a Transfer or a state space representation. >>> >>> In the typical usecase, suppose you would like to create an n-many >>> parallel connections with the same LTI system sitting at each branch. >>> MATLAB implements this as an elementwise multiplication and returning a >>> multi input multi output(MIMO) system. >>> >>> G = tf(1,[1,1]); >>> eye(3)*G >>> >>> produces (manually compactified) >>> >>> ans = >>> >>> From input 1 to output... >>> [ 1 ] >>> [ ------ , 0 , 0 ] >>> [ s + 1 ] >>> [ 1 ] >>> [ 0 , ------ , 0 ] >>> [ s + 1 ] >>> [ 1 ] >>> [ 0 , 0 , ------ ] >>> [ s + 1 ] >>> >>> Notice that the result type is of LTI system but, in our context, not a >>> NumPy array with "object" dtype. >>> >>> In order to achieve a similar behavior, I would like to let the __rmul__ >>> of G take care of the multiplication. In fact, when I do >>> G.__rmul__(np.eye(3)) I can control what the behavior should be and I >>> receive the exception/result I've put in. However the array never looks for >>> this method and carries out the default array __mul__ behavior. >>> >>> The situation is similar if we go about it as left multiplication >>> G*eye(3) has no problems since this uses directly the __mul__ of G. >>> Therefore we get a different result depending on the direction of >>> multiplication. >>> >>> Is there anything I can do about this without forcing users subclassing >>> or just letting them know about this particular quirk in the documentation? >>> >>> What I have in mind is to force the users to create static LTI objects >>> and then multiply and reject this possibility. But then I still need to >>> stop NumPy returning "object" dtyped array to be able to let the user know >>> about this. >>> >>> >>> Relevant links just in case >>> >>> the library : https://github.com/ilayn/harold/ >>> >>> the issue discussion (monologue actually) : >>> https://github.com/ilayn/harold/issues/7 >>> >>> The question I've asked on SO (but with a rather offtopic answer): >>> https://stackoverflow.com/q/40694380/4950339 >>> >>> >>> ilhan >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@python.org >>> https://mail.python.org/mailman/listinfo/numpy-discussion >>> >>> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > >
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