def _build_og(self, original_ppi):
""" Build the original graph, without any nodes removed. """

try:
    graph_fp = open(original_ppi, 'r')
except IOError:
    sys.exit("Could not open file: {}".format(original_ppi))

G = nx.DiGraph()
edge_list = []

# parse network input
for line in graph_fp.readlines():
    split_line = line.rstrip().split('t')
    # assume input graph is a simple edgelist with weights
    edge_list.append((split_line[0], split_line[1],  float(split_line[2])))

G.add_weighted_edges_from(edge_list)
graph_fp.close()
print edge_list

return G

1   1   0.9
1   2   0.075
1   3   0.25
2   1   0.15
2   2   0.8
2   3   0.05
3   1   0.25
3   2   0.25
3   3

def _build_matrices(self, original_ppi):
        """ Build column-normalized adjacency matrix for each graph.

        NOTE: these are column-normalized adjacency matrices (not nx
              graphs), used to compute each p-vector
        """
        original_graph = self._build_og(original_ppi)
        self.OG = original_graph
        og_not_normalized = nx.to_numpy_matrix(original_graph)
        self.og_matrix = self._normalize_cols(og_not_normalized)

def run_exp(self, source):

        CONV_THRESHOLD = 0.000001
        # set up the starting probability vector
        p_0 = self._set_up_p0(source)
        diff_norm = 1
        # this needs to be a deep copy, since we're reusing p_0 later
        p_t = np.copy(p_0)

        while (diff_norm > CONV_THRESHOLD):
            # first, calculate p^(t + 1) from p^(t)
            p_t_1 = self._calculate_next_p(p_t, p_0)

            # calculate L1 norm of difference between p^(t + 1) and p^(t),
            # for checking the convergence condition
            diff_norm = np.linalg.norm(np.subtract(p_t_1, p_t), 1)

            # then, set p^(t) = p^(t + 1), and loop again if necessary
            # no deep copy necessary here, we're just renaming p
            p_t = p_t_1

def _set_up_p0(self, source):
    """ Set up and return the 0th probability vector. """
    p_0 = [0] * self.OG.number_of_nodes()
    # convert self.OG.number_of_nodes() to list
    l =  list(self.OG.nodes())
    #nx.draw(self.OG, with_labels=True)
    #plt.show()
    for source_id in source:
        try:
            # matrix columns are in the same order as nodes in original nx
            # graph, so we can get the index of the source node from the OG
            source_index = l.index(source_id)
            p_0[source_index] = 1 / float(len(source))
        except ValueError:
            sys.exit("Source node {} is not in original graph. Source: {}. Exiting.".format(source_id, source))

    return np.array(p_0)

def _calculate_next_p(self, p_t, p_0):
        """ Calculate the next probability vector. """
        print 'p_0t{}'.format(p_0)
        print 'p_tt{}'.format(p_t)
        epsilon = np.squeeze(np.asarray(np.dot(self.og_matrix, p_t)))
        print 'epsilont{}'.format(epsilon)
        print 10*"*"
        return np.array(epsilon)

p_0     [0. 1. 0.]
p_t     [0. 1. 0.]
epsilon [0.3125 0.625  0.0625]
**********
p_0     [0. 1. 0.]
p_t     [0.3125 0.625  0.0625]
epsilon [0.41582532 0.46461004 0.11956464]
**********
p_0     [0. 1. 0.]
p_t     [0.41582532 0.46461004 0.11956464]
epsilon [0.44104068 0.3969176  0.16204171]
**********
p_0     [0. 1. 0.]
p_t     [0.44104068 0.3969176  0.16204171]
epsilon [0.44017539 0.36889829 0.19092632]
**********
p_0     [0. 1. 0.]
p_t     [0.44017539 0.36889829 0.19092632]
epsilon [0.43274594 0.35763862 0.20961544]
**********
p_0     [0. 1. 0.]
p_t     [0.43274594 0.35763862 0.20961544]
epsilon [0.42532978 0.35332572 0.22134451]
**********
p_0     [0. 1. 0.]
p_t     [0.42532978 0.35332572 0.22134451]
epsilon [0.41962966 0.35181043 0.22855991]
**********
p_0     [0. 1. 0.]
p_t     [0.41962966 0.35181043 0.22855991]
epsilon [0.41569093 0.35137062 0.23293845]
**********
p_0     [0. 1. 0.]
p_t     [0.41569093 0.35137062 0.23293845]
epsilon [0.41311858 0.3513113  0.23557012]
**********
p_0     [0. 1. 0.]
p_t     [0.41311858 0.3513113  0.23557012]
epsilon [0.41149463 0.35136436 0.23714102]
**********
p_0     [0. 1. 0.]
p_t     [0.41149463 0.35136436 0.23714102]
epsilon [0.41049166 0.35143431 0.23807403]
**********
p_0     [0. 1. 0.]
p_t     [0.41049166 0.35143431 0.23807403]
epsilon [0.40988136 0.35149249 0.23862616]
**********
p_0     [0. 1. 0.]
p_t     [0.40988136 0.35149249 0.23862616]
epsilon [0.40951383 0.35153418 0.23895199]
**********
p_0     [0. 1. 0.]
p_t     [0.40951383 0.35153418 0.23895199]
epsilon [0.40929414 0.35156196 0.2391439 ]
**********
p_0     [0. 1. 0.]
p_t     [0.40929414 0.35156196 0.2391439 ]
epsilon [0.40916352 0.35157973 0.23925675]
**********
p_0     [0. 1. 0.]
p_t     [0.40916352 0.35157973 0.23925675]
epsilon [0.40908617 0.35159079 0.23932304]
**********
p_0     [0. 1. 0.]
p_t     [0.40908617 0.35159079 0.23932304]
epsilon [0.40904049 0.35159756 0.23936195]
**********
p_0     [0. 1. 0.]
p_t     [0.40904049 0.35159756 0.23936195]
epsilon [0.40901358 0.35160165 0.23938477]
**********
p_0     [0. 1. 0.]
p_t     [0.40901358 0.35160165 0.23938477]
epsilon [0.40899775 0.35160411 0.23939815]
**********
p_0     [0. 1. 0.]
p_t     [0.40899775 0.35160411 0.23939815]
epsilon [0.40898845 0.35160557 0.23940599]
**********
p_0     [0. 1. 0.]
p_t     [0.40898845 0.35160557 0.23940599]
epsilon [0.40898299 0.35160643 0.23941058]
**********
p_0     [0. 1. 0.]
p_t     [0.40898299 0.35160643 0.23941058]
epsilon [0.40897978 0.35160695 0.23941327]
**********
p_0     [0. 1. 0.]
p_t     [0.40897978 0.35160695 0.23941327]
epsilon [0.40897791 0.35160725 0.23941485]
**********
p_0     [0. 1. 0.]
p_t     [0.40897791 0.35160725 0.23941485]
epsilon [0.4089768  0.35160743 0.23941577]
**********
p_0     [0. 1. 0.]
p_t     [0.4089768  0.35160743 0.23941577]
epsilon [0.40897616 0.35160753 0.23941631]
**********
p_0     [0. 1. 0.]
p_t     [0.40897616 0.35160753 0.23941631]
epsilon [0.40897578 0.35160759 0.23941662]
**********
1       0.41
3       0.35
2       0.24