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

Ilhan Polat ilhanpolat at gmail.com
Mon Jun 19 12:07:46 EDT 2017


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-
> multiplication-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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