Support for new sampling methods and new model formats in dingo

Package dingo is a python package that analyzes metabolic networks. It relies on high dimensional sampling with Markov Chain Monte Carlo (MCMC) methods and fast optimization methods to analyze the possible states of a metabolic network. It represents a metabolic network with a convex polytope, while the points in the interior of the polytope correspond to steady states of the network. By sampling, dingo, explores and statistically studies the flux space of the network. To perform MCMC sampling, dingo relies on the C++ library volesti. Currently, it provides MMCS algorithm which is based on Billiard Walk and on a multiphase rounding scheme. This project will provide dingo additional options for uniform sampling by exposing into it several C++ implementations of random walks that are included in volesti. Moreover, it will provide sampling from the exponential distribution using the C++ implementation of Hamiltonian Monte Carlo in volesti.