We avoid the bug by not applying the transformation and casting the
result of jacobianInverseTransposed to a FieldMatrix. This way we do
not exploit the diagonal structure of the Transformation. Should get
fixed in the near future.

In the end I had to combine the last two versions. For jacobians:
- We want to split according to the FunctionView (e.g. Stokes).
- We don't want to split according to gradients (e.g. sumfactorization).

Example: Before this commit the expression

\sum_i a_i (\nabla v)_i + \sum_j b_j (\nabla v)_j

was split into two accumulation terms:

1) a_k with corresponding test function (\nabla v)_k
2) b_l with corresponding test function (\nabla v)_l

Now we split into:

a_k + b_k with corresponding test function (\nabla v)_k

This is possible since we have linearity in the test function.

TODO: Jacobians are not yet working

... by listing all packages manually in setup.py

But only when they are actually used!

It should be possible to have multiple of these with different increment