I was just considering, isn't it easier to store abstract and numeric indices separately?
I mean, the idea of changing all canonization algorithm is long and will need a longer phase of testing to check all possible cases. Besides, I have already prepared some code to add a numpy ndarray to the tensor objects in order to perform numerical calculations on them. The idea behind that is just to use both abstract indices and integer positions, i.e. A(m0, m1, m2)[2,0, 1] Here [2, 0, 1] are the values to access positions identified by abstract indices (m0, m1, m2). The problem here is that this A(m0, m1, m2)[2,0, 1] notation clashes with the A(F(2), F(0), F(1)) notation. An easier to implement idea would be to create hidden *TensorIndex* objects, and put the numerical value inside of them. Hidden indices will be of a particular type which does not perform contraction, but otherwise behaves like a free index. In any case, it is better to take some days to think about it before starting to write any code. Do you believe that my alternative could be a good one? -- You received this message because you are subscribed to the Google Groups "sympy" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at http://groups.google.com/group/sympy. For more options, visit https://groups.google.com/groups/opt_out.
