def get_degree_of_separation_visualisation(self, author1, author2):
if author1 == '' or author2 == '':
return Graph()
if author1 == author2:
return Graph()
# Compute all the shortest paths from author1 to author2
try:
list_of_paths = all_shortest_paths(self.authors_graph, self.author_idx[author1], self.author_idx[author2])
except NetworkXError as e:
return "Not found"
g = Graph()
# Add the shortest paths to the graph
try:
for path in list_of_paths:
g.add_path(path)
except NetworkXNoPath as e:
return Graph()
# Add attributes to nodes
for i in g.nodes():
g.node[i]['name']=self.authors[i].name
print g.nodes(data=True)
return g
def _sequence_graph(parameterValues):
'''Convert a list of values to a graph that connects adgacent values'''
graph = Graph()
graph.add_nodes_from(parameterValues)
graph.add_path(parameterValues)
assert len(graph.nodes())>0, "Generated an empty graph. Is parameterValues empty?"
return graph
def get_control_flow(self):
"""Create a control flow graph from saved nodes.
"""
from networkx import Graph
graph = Graph()
for offset, node in self.nodes.iteritems():
for following in node["successors"]:
graph.add_path([offset, following])
return graph
