Major speed up to `FastMKSRegressionModel` using PyFFTW.

The `predict` method for the `FastMKSRegressionModel` is now a lot
faster using PyFFTW as well as a few more Numexpr. Using this class a
full Cahn-Hilliard simulation using the MKS is possible with a grid
size of 256x256.

 - The `FiPyCHModel` can now be passed the `a` parameter.

 - The `FiPyCHModel` does 5 sweeps to ensure that is has converged at
   a given step.

 - The `FastMKSModel` takes a `threads` argument so both the threading
   for Numexpr and PyFFTW can be set.

 - The entire `predict` method has now been overwritten by
   `FastMKSRegressionMoldel`.

Address #25
Notebook now does a complete CH simulation using the MKS.

Address #25
Notebook with parallel IPython and FastMKSRegressionModel tutorial.

The CH simulation does not work currently, something has changed since
the last commit. The notebook will include numexpr and PyFFTW
explanation.
Finish the `cahnHilliard4.ipynb` adding many changes.

 - Using a new set of parameters for the CH model.

 - Include PyFFTW and Numexpr discussion.

 - Compare the fast and slow regression models.

 - Compare MKS and FiPy directly.

 - The full simulation now works with the new parameters.

Other changes:

 - Added documentation for `FastMKS...` __init__.py

 - Added `epsilon` parameter to `MKSRegressionModel` and explicit
   instantiation of the solver.

Addresses issues #34, #32, #25, #12.
Fix typo.
Merge branch 'issue25-Nbin'

Fix #25
Fix #12
Address #32