I think I've answered my own question - I remembered tensordot, and the
following seems to work:
def transform(tx_matrix, psi1, psi2):
psi = np.tensordot(tx_matrix,
np.concatenate((psi1[newaxis],psi2[newaxis])),axes=1))
return psi[0], psi[1]
sorry for the noise,
Gary
Gary Ruben wrote:
> I'm trying to work out how to apply a 2x2 transform matrix to a spinor, e.g.
>
> [psi1'] [a b][psi1]
> [ ] = [ ][ ]
> [psi2'] [c d][psi2]
>
> where [[a,b],[c,d]] is a transform matrix and psi1 and psi2 are
> i x j x k complex arrays representing complex scalar field data. I
> worked that one way to do it with 2D fields (psi1 and psi2 being 2D,
> i x j arrays) is
>
> def transform(tx_matrix, psi1, psi2):
> psi = np.dot(tx_matrix, np.rollaxis(np.dstack((psi1,psi2)),2,1))
> return psi[0], psi[1]
>
> or, equivalently
>
> def transform(tx_matrix, psi1, psi2):
> psi = np.dot(tx_matrix, np.rollaxis(
> np.concatenate((psi1[newaxis],psi2[newaxis])),1))
> return psi[0], psi[1]
>
> but, as seems usual for me, I'm confused with trying to extend this to
> the next higher dimension; 3D in this case. It seems to me that there
> might be a neater way to do this, i.e. some built-in feature of an
> existing numpy function. How do I extend this for 3D psi1 and psi2
> arrays? Are there any general tips or guides for helping to minimise
> confusion when it comes to manipulating axes like this, such as standard
> ways to extend 2D recipes to 3D?
>
> Gary R.
> _______________________________________________
> Numpy-discussion mailing list
> Numpy-discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
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