def test_badness_real():
#networks = ['data/protein_interaction.adj']
networks = ['data/food_web.adj', 'data/protein_interaction.adj', 'data/pierre_auger.adj']
for filename in networks:
G = Graph(readwrite.read_adjacency_list(filename))
G = G.largest_component()
G.defragment_indices()
G.embed_ncMCE()
badness = G.greedy_routing_badness()
max_badness = max(badness.values())
for vertex, attributes in G.vert.items():
attributes.update({'color':badness[vertex]/max_badness})
attributes.update({'size':badness[vertex]/max_badness})
G.draw(representation='hyperbolic_polar', vertex_scale=50)
G.embed_ncMCE(angular_adjustment=embedding.circular_adjustment)
badness = G.greedy_routing_badness()
max_badness = max(badness.values())
for vertex, attributes in G.vert.items():
attributes.update({'color':badness[vertex]/max_badness})
attributes.update({'size':badness[vertex]/max_badness})
G.draw(representation='hyperbolic_polar', vertex_scale=50)
return
