[Numpy-discussion] Controlling NumPy __mul__ method or forcing it to use __rmul__ of the "other"

Stephan Hoyer shoyer at gmail.com
Mon Jun 19 12:58:32 EDT 2017


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 at 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 at 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 at 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 <ilhanpolat at 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
>>>>
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>>>>
>>>>
>>>
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