Hi Charles, Thank you for your response!
I do think np.einsum() is really great. I am not clear about how that ties to my question though, because I was thinking more in lines of wrapping and reshaping the output (#1) and improve the documentation (#2), where the proper outcome was already calculated. -Shawn On Tue, Nov 25, 2014 at 11:12 PM, Charles R Harris <charlesr.har...@gmail.com> wrote: > Take a look as einsum, it is quite good for such things. > > Chuck > > On Tue, Nov 25, 2014 at 9:06 PM, Yuxiang Wang <yw...@virginia.edu> wrote: >> >> Dear all, >> >> I have been doing tensor algebra recently (for continuum mechanics) >> and was looking into two common operations: tensor product & tensor >> contraction. >> >> 1. Tensor product >> >> One common usage is: >> a[i1, i2, i3, ..., iN, j1, j2, j3, ..., jM] = b[i1, i2, i3, ..., iN] * >> c[j1, j2, j3, ..., jM] >> >> I looked into the current np.outer(), and the only difference is that >> it always flattens the input array. So actually, the function for >> tensor product is simply >> >> np.outer(a, b, out=out).reshape(a.shape+b.shape) <-- I think I got >> this right but please do correct me if I am wrong >> >> Would anyone think it helpful or harmful to add such a function, >> np.tensorprod()? it will simply be like >> def tensorprod(a, b, out=None): >> return outer(a, b, out=out).reshape(a.shape+b.shape) >> >> >> 2. Tensor contraction >> >> It is currently the np.tensordot(a, b) and it will do np.tensordot(a, >> b, axes=2) by default. I think this is all great, but it would be even >> better if we specify in the doc, that: >> i) say explicitly that by default it will be the double-dot or >> contraction operator, and >> ii) explain that in cases where axes is an integer-like scalar, >> which axes were selected from the two array and in what order. Like: >> if axes is an integer-like scalar, it is the number axes to sum over, >> equivalent to axes=(list(range(-axes, 0)), list(range(0, axes))) (or >> something like this) >> >> >> It'd be great to hear what you would think about it, >> >> Shawn >> >> >> -- >> Yuxiang "Shawn" Wang >> Gerling Research Lab >> University of Virginia >> yw...@virginia.edu >> +1 (434) 284-0836 >> https://sites.google.com/a/virginia.edu/yw5aj/ >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > -- Yuxiang "Shawn" Wang Gerling Research Lab University of Virginia yw...@virginia.edu +1 (434) 284-0836 https://sites.google.com/a/virginia.edu/yw5aj/ _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
