To use tensordot, compute the outer product of the g tensors:
def rotT(T, g):
gg = np.outer(g, g)
gggg = np.outer(gg, gg).reshape(4 * g.shape)
axes = ((0, 2, 4, 6), (0, 1, 2, 3))
return np.tensordot(gggg, T, axes)
On my system, this is around seven times faster than Sven’s solution. If the g tensor doesn’t change often, you can also cache the gggg tensor. If you do this and turn on some micro-optimizations (inlining the tensordot code, no checks, no generic shapes), you can still make it two times faster:
def rotT(T, gggg):
return np.dot(gggg.transpose((1, 3, 5, 7, 0, 2, 4, 6)).reshape((81, 81)),
T.reshape(81, 1)).reshape((3, 3, 3, 3))
Results of timeit on my home laptop (500 iterations):
Your original code: 19.471129179
Sven's code: 0.718412876129
My first code: 0.118047952652
My second code: 0.0690279006958
The numbers on my work machine are:
Your original code: 9.77922987938
Sven's code: 0.137110948563
My first code: 0.0569641590118
My second code: 0.0308079719543