def generate_rip_graph(draw_flag, size):
"""
:param draw_flag: boolean indicating whether the graph should be drawn or not
:param size: Integer indicating the size of the graph to be generated
:return n_graph: the generated graph
"""
n_graph = graph_gen.generate_graph(size)
# Add a distance matrix, a best weights vector and a queue to all the nodes
n = n_graph.number_of_nodes()
for i in range(0, n):
new_distance_matrix = np.empty((n, n))
new_distance_matrix.fill(sys.maxint)
new_distance_matrix[i].fill(-1) # Fill with -1 me as destination
new_distance_matrix[:, i] = -1 # Fill with -1 me as path
new_best_weights_vector = np.empty(n)
new_best_weights_vector.fill(sys.maxint)
new_default_next_hop_vector = np.empty(n)
new_default_next_hop_vector.fill(None)
n_graph.node[i]['distance_matrix'] = new_distance_matrix
n_graph.node[i]['default_next_hop'] = new_default_next_hop_vector
# Control messages content
n_graph.node[i]['best_weights_vector'] = new_best_weights_vector
n_graph.node[i]['buffer_queue'] = Queue.Queue(maxsize=n)
n_graph.nodes(data=True)
if draw_flag:
graph_gen.draw_graph(n_graph)
return n_graph
rip_broadcast(net_graph)
rip_update_distance_matrix(net_graph)
return net_graph
if __name__ == '__main__':
# network_graph = generate_rip_graph(False, 4)
#
# rip_first_iteration(network_graph)
#
# while not convergence:
# rip_broadcast(network_graph)
# rip_update_distance_matrix(network_graph)
network_graph = rip_simple(100)
# Comparing with bellman-ford for testing
for n, nattr in network_graph.nodes(data=True): # For each node n and attribute nattr
print "Our RIP:"
print "(%d,%s)" % (n, nattr['best_weights_vector'])
print "(%d,%s)" % (n, nattr['default_next_hop'])
pred, dist = nx.bellman_ford(network_graph, n)
print "Bellman_ford:"
print sorted(dist.items())
print sorted(pred.items())
break
graph_gen.draw_graph(network_graph)
