[SciPy-dev] Suppressing of numpy __mul__, __div__ etc

Sebastian Walter sebastian.walter@gmail....
Wed Dec 16 16:53:16 CST 2009


I have also implemented/wrapped various automatic differentiation
tools in python
(http://github.com/b45ch1/algopy , http://github.com/b45ch1/pyadolc,
http://github.com/b45ch1/pycppad if someone is interested)
and I have also come across some numpy glitches and inconsistencies.

However, this particular problem I have not encountered. I'm not sure
I understand the rationale why an expression like numpy.array([1,2] *
oofun(1) should  call the oofun.__rmul__ operator.
Hence, I'm a little sceptical about this enhancement.

What is wrong with the following implementation? It works perfectly fine...

--------------- start code snippet -----------------

class oofun:
    def __init__(self,x):
        self.x = x

    def __mul__(self, rhs):
        print 'called __mul__'
        if isinstance(rhs, oofun):
            return oofun(self.x * rhs.x)
        else:
            return rhs * self

    def __rmul__(self, lhs):
        print 'called __rmul__'
        return oofun(self.x * lhs)

    def __str__(self):
        return str(self.x)+'a'

    def __repr__(self):
        return str(self)

------------- end code snippet ----------------

--------- output ----------
basti@shlp:~/Desktop$ python live_demo.py
called __mul__
called __rmul__
called __rmul__
called __rmul__
[2.0a 2.0a 2.0a]
called __rmul__
called __rmul__
called __rmul__
[2.0a 2.0a 2.0a]
------------- end output --------------


regards,
Sebastian




2009/12/16 Dag Sverre Seljebotn <dagss@student.matnat.uio.no>:
> Charles R Harris wrote:
>>
>>
>> 2009/12/14 Dmitrey <tmp50@ukr.net <mailto:tmp50@ukr.net>>
>>
>>
>>
>>     --- Исходное сообщение ---
>>     От кого: Charles R Harris <charlesr.harris@gmail.com
>>     <mailto:charlesr.harris@gmail.com>>
>>     Кому: SciPy Developers List <scipy-dev@scipy.org
>>     <mailto:scipy-dev@scipy.org>>
>>     Дата: 14 декабря, 20:26:12
>>     Тема: Re: [SciPy-dev] Suppressing of numpy __mul__, __div__ etc
>>
>>         > --- Исходное сообщение ---
>>         > От кого: Charles R Harris <charlesr.harris@gmail.com
>>         <mailto:charlesr.harris@gmail.com>>
>>         > Кому: SciPy Developers List <scipy-dev@scipy.org
>>         <mailto:scipy-dev@scipy.org>>
>>         > Дата: 14 декабря, 20:06:46
>>         > Тема: Re: [SciPy-dev] Suppressing of numpy __mul__, __div__ etc
>>
>>         > I think it should have numpy or ndarray in the name
>>         somewhere to indicate
>>         > that it is numpy specific. Hmm <looks in thesaurus>, maybe,
>>         >
>>         > __supercede_ndarray__,
>>         > __disallow_ndarray__,
>>         > __deny_ndarray__,
>>         > __reject_ndarray__,
>>         > __refuse_ndarray__,
>>         > __exclude_ndarray__,
>>         > __reject_ndarray__, ...
>>         >
>>         > My preference among those would be __deny_ndarray__.
>>         >
>>         >
>>         > But isn't the issue present with numpy matrices or
>>         scipy.sparse matrices as
>>         > well?
>>         > So I guess instead of ndarray another word should be used.
>>         >
>>
>>         __has_precedence__
>>         __is_prior__
>>
>>         Chuck
>>
>>     N
>>     I guess some numpy developers should choose the final name in
>>     numpy IRC channel and inform the list (ASAP) about their
>>     collective (and hence final) decision. I'm not skilled in English
>>     quite enough, but so short names seems too uninformative to me,
>>     they will be not used too often so I guess more informative should
>>     be preferred.
>>
>>
>> I don't think we should rush this. I'm waiting for more people to
>> weigh in or offer different solutions.
>
> Well, a different solution would be to have a standard with "operand
> precedence", i.e. an integer which could be compared between objects,
> and the highest one wins and gets to decide how the arithmetic operation
> is carried out.
>
> This could be used to simplify NumPy itself. I.e. assign e.g.
> __operand_precedence__ = 0 for ndarray, 100 for matrix, and use it
> internally in NumPy to decide who carries out the operation.
>
> Between other libraries it gets messy to try to figure out what range of
> values to use though. One would really want to be able to make
> statements about a partially ordered set ("I take priority over ndarray;
> a Frobnicator takes priority over me..."), but it's probably way too
> time consuming and complicated.
>
> I like the "precedence" word though. So my suggestions are
>  __operand_precedence__ # boolean or integer?
>  __numpy_operand_precedence__ # boolean or integer?
>  __precedence_over_ndarray__ # bool
>
>
> Dag Sverre
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>


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