Friedrich Romstedt wrote:
> 2010/6/12 Alan Bromborsky <[email protected]>:
>
>> If I have a single numpy array, for example with 3 indices T_{ijk} and I
>> want to sum over two them in the sense of tensor contraction -
>>
>> T_{k} = \sum_{i=0}^{n-1} T_{iik}. Is there an easy way to do this with
>> numpy?
>>
>
> Also you can give:
>
> T[I, I, :].sum(axis=0)
>
> a try with:
>
> I = numpy.arange(0, n) .
>
> This has the benefit to not be limited to two-dimensional diagonals,
> like .diagonal() is.
>
>
> But note that for:
> T[:, I, I]
> the shape is reversed with respect to that of:
> T[I, :, I] and T[I, I, :] .
>
> I think it should be written in the docs how the shape is derived.
>
> hth,
> Friedrich
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>
Your method works, but I should have posed the general case. You have
an array T_{i_{1}...i_{r}} and you wish to calculate
T_{i_{1}...i_{r-2}} =
\sum_{i_{j}=i_{k}=0}^{n-1}T_{i_{1}...i_{j}...i_{k}...i_{r}}
I don't have a clue as to how to generalize your method.
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