Brain.set_locs()

We could add a new function: `Brain.set_locs()`.
- Syntax: `bo.set_locs(new_locs, nearest_neighbors=False, include_original_locations=False, rbf_width=20, match_threshold='auto')`.
- If `nearest_neighbors=False` (default), blur out the current set of locations to the new locations. Otherwise round all current locations to their nearest equivalent, within the parameters of match_threshold (behave like the current `nearest_neighbors` options in `Model` objects). Also remove `nearest_neighbors` stuff from `Model` objects. `If nearest_neighbor=False`, ignore the `match_threshold` argument.
- If `include_original_locations=True`, also include the original data (just add to it). Idea: this might be a good (alternative) null model– e.g. if you just use spatial blur on the raw data, how well can you fill in missing data. It'll be similar to using just the subject's data to compute the correlation matrix, but it won't factor in long-distance correlations.
- Use the `rbf_width` parameter (default: 20) to do the blurring
- This should update `self.locs` to reflect the new locations
- All "blurring" should be done via `self.get_locs()` and `self.get_data()` (i.e. filtering out "bad" electrodes).
- Kurtosis values still need to be re-computed for all new data
- If no blurring is needed (e.g. if `new_locs` is a subset of `self.get_locs`, or if `nearest_neighbors=True`), just re-use the existing kurtosis values