I think I understand the intended structure of the tensor.tensor module a bit more now. A lot of the things I have fooled around with are quick hacks that don't really fit the use of each class (i.e., just returning a TensMul from a covariant derivative function). A quick question - why is it true that TensAdd objects are not supposed to support index order? Is this a function of the other algorithms like canonicalization that I'm not as interested in?
I'd like to ask more generally what use a tensor is if the indices are unordered, but I suppose that just reveals my ignorance as a novice physicist - I think of Tensors in a pretty concrete matrix-based way, and when I'm not thinking of them as matrices, I'm thinking of them as functions, which requires an argument order. I will take your advice and look more at sympy.diffgeom for the time being - do you know if it's possible to implement mixed tensors there? Perhaps it would be easier to build a TensorArray class that provides an interface more like sympy.tensor.tensor while using the implementation of sympy.diffgeom. Thank you for taking the time to explain everything about the structure of the module. -- 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. To view this discussion on the web visit https://groups.google.com/d/msgid/sympy/0a52a40a-f5cc-4dac-98b2-83d334b0b446%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
