Standardize IO

[ebridge2]

Shared io functions in utils, so there is less inconsistency and chances for breakage. Ie, this breaks id imagine https://github.com/neurodata/pygraphstats/blob/master/graphstats/ase/ase.py with networkx.

Todo:

• profie networkx to numpy matrix function (sparse as function of n, dense as function of n from n=10 to 10k on log scale; sparse = O(n) edges, dense = O(n^2) edges)
• embed base class
• IO functions
• IO tests
• travis integration

DoD:

• figures showing profiling results from above
• IO functions
• consistent base class for "embed" methods
• tests for both of the above, demonstrating that they work
• Description of tests:
  • test1: show that input accepts both networkx and numpy objects, and correctly returns the same object type for both
• start travis