Supercellular sampler

[Lioscro]

SupercellularSampler implementation, along with several utility functions in CassiopeiaTree that are useful in dealing with ambiguous states and branch distances.

Some important changes are the following.

• Ambiguous characters are denoted as a tuple in the character matrix and/or the network. Updates to some functions and addition of new functions in CassiopeaTree to support ambiguous characters.
• When computing dissimilarities with CassiopeiaTree.compute_dissimilarity_map, a new cluster_dissimilarity function should be used when there are ambiguous characters. Here is an example snippet

from functools import partial
dissimilarity_function = partial(cluster_dissimilarity, weighted_hamming_distance)
tree.compute_dissimilarity_map(dissimilarity_function)

I had considered whether this should be done automatically in compute_dissimilarity_map, but felt that the arguments should always be explicit for the user.

• Updated n_cell, n_character and populate_tree in CassiopeiaTree to use the current character matrix instead of the original one because these properties/function should always return/operate on the current object state, instead of a possibly-outdated state (i.e. the original character matrix). Previously, the original (old) state was being used and this was causing problems for GreedySolver when trying to call collapse_mutationless_edges.

This PR is big enough for reviews from both @mattjones315 and @richardyz98.

[Lioscro]

Thanks for the helpful reviews @mattjones315! Almost ready for merge. :)

[Lioscro]

@mattjones315 and I went through some extensive reviews, but could you also take a look @richardyz98? Would appreciate any feedback!