Controlling NumPy __mul__ method or forcing it to use __rmul__ of the "other"
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
I answered your question on StackOverflow: https://stackoverflow.com/questions/40694380/forcing-multiplication-to-use-r... 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 <ilhanpolat@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 <shoyer@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 <ilhanpolat@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 <nathan12343@gmail.com> wrote:
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 <shoyer@gmail.com> wrote:
I answered your question on StackOverflow: https://stackoverflow.com/questions/40694380/forcing-multiplication-to-use-r... 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 <ilhanpolat@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 <shoyer@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 <ilhanpolat@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 <nathan12343@gmail.com> wrote:
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 <shoyer@gmail.com> wrote:
participants (3)
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Ilhan Polat -
Nathan Goldbaum -
Stephan Hoyer