Ah OK. I was just wondering if there are recommended values to start with in case below some values are reserved for NumPy/SciPy internals. I'll just go with the ufunc path just in case.
This really looks like TeX overful/underful badness value adjustment. As long as the journal accepts don't mention it. :) On Mon, Jun 19, 2017 at 6:58 PM, Stephan Hoyer <sho...@gmail.com> wrote: > Coming up with a single number for a sane "array priority" is basically an > impossible task :). If you only need compatibility with the latest version > of NumPy, this is one good reason to set __array_ufunc__ instead, even if > only to write __array_ufunc__ = None. > > On Mon, Jun 19, 2017 at 9:14 AM, Nathan Goldbaum <nathan12...@gmail.com> > wrote: > >> 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 >>> >>> >> >> _______________________________________________ >> 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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