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

[Nathan Goldbaum]

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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>>> NumPy-Discussion at python.org
>>> https://mail.python.org/mailman/listinfo/numpy-discussion
>>>
>>>
>>
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