This PR is still in progress, but I wanted to upload my initial classes to support this. The problem is broken down into two cases:

1. Non-Polar spacegroups: easier case because they have a finite number of cases to evaluate
2. Polar spacegroups: more challenging because of the continuous alternative origins.

These classes use the sum of the squared phase residual between two sets of phases as a loss function to minimize, which has been pretty effective in my hands.

The NonPolarTranslator does a brute-force search of the 64 possible cases to evaluate the optimal translation vector. The PolarTranslator does a global optimization to determine the optimal translation vector. This is a bit non-trivial -- its a bumpy loss landscape -- but I've found the scipy dual_annealing optimizer to work very well. Another trick has been to start at low-resolution and gradually increase to full resolution. After discussion with @kmdalton, we can also use a FFT-based method to come up with a sensible initial value for optimization.

My todo items:

• Add user-facing method that takes two datasets, and uses the proper class based on the spacegroup to determine the translation vector that gives a common phase origin.
• Add a suite of tests for common spacegroups that cover the polar/non-polar cases
• (optional improvement) Add initialization code based on FFT-method to speed things up