On Nov 13, 2010, at 4:23 AM, Øyvind Jensen wrote:
> fr., 12.11.2010 kl. 11.19 -0700, skrev Aaron Meurer:
>> If I understand you correctly, you will implicitly sum over Symbol
>> indices and loop over Idx indices, right? If so, that sounds like a
>> good idea, except why do you want to use Symbol instead of creating a
>> new class?
>
> Well, maybe a new class is better, I'm not sure. The idea was to allow
> implicit summation with repeated indices of class Idx as it is done
> today, and use something else for indices that are not subject to
> implicit summation.
>
>>
>> I'm also confused about creating one class vs. another. What would
>> happen in your model if you mix both, like
>>
>>>>> i = Idx('i')
>>>>> j = Symbol('j', dimension=N, integer=True) # Or whatever you end up doing
>>>>> A[i, j]
>> ???
>
> That's a good question. Maybe there should only be one Indexed class
> that can handle indices of various types. It may be the cleanest
> approach. If I recall correctly this is the way covariant and
> contravariant indices are implemented in GiNaC: In addition to an Idx
> class, GiNaC has a VarIdx class for indices with given variance. We
> should also be able to follow this approach for variance, and it may be
> a good approach for elemental indices as well.
I agree. Just put the type as a keyword argument to Idx, and store it as a
property of the element (unless the two different things really are different
enough that they deserve to be different classes).
Aaron Meurer
>
> To follow that approach, I think we are going to need
>
> 1) some representation of elemental indices (Symbol? ElemIdx?
> FixedIdx? ...)
> 2) improvements to sympy.tensor.indexed.Indexed
> 3) some changes in sympy.tensor.index_methods
> 4) printing of Sum in the code printers
>
> Øyvind
>
>
>>
>> Aaron Meurer
>
>
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