Python networkx.eccentricity函数代码示例

 def test_eccentricity(self):
     assert_equal(networkx.eccentricity(self.G, 1), 6)
     e = networkx.eccentricity(self.G)
     assert_equal(e[1], 6)
     sp = dict(networkx.shortest_path_length(self.G))
     e = networkx.eccentricity(self.G, sp=sp)
     assert_equal(e[1], 6)
     e = networkx.eccentricity(self.G, v=1)
     assert_equal(e, 6)
     # This behavior changed in version 1.8 (ticket #739)
     e = networkx.eccentricity(self.G, v=[1, 1])
     assert_equal(e[1], 6)
     e = networkx.eccentricity(self.G, v=[1, 2])
     assert_equal(e[1], 6)
     # test against graph with one node
     G = networkx.path_graph(1)
     e = networkx.eccentricity(G)
     assert_equal(e[0], 0)
     e = networkx.eccentricity(G, v=0)
     assert_equal(e, 0)
     assert_raises(networkx.NetworkXError, networkx.eccentricity, G, 1)
     # test against empty graph
     G = networkx.empty_graph()
     e = networkx.eccentricity(G)
     assert_equal(e, {})

 def test_eccentricity(self):
     assert_equal(networkx.eccentricity(self.G,1),6)
     e=networkx.eccentricity(self.G)
     assert_equal(e[1],6) 
     sp=networkx.shortest_path_length(self.G)
     e=networkx.eccentricity(self.G,sp=sp)
     assert_equal(e[1],6) 
     e=networkx.eccentricity(self.G,v=1)
     assert_equal(e,6) 
     e=networkx.eccentricity(self.G,v=[1,1])
     assert_equal(e,6) 

def search20q(G,ranking):
    seen = []
    seen.append("Emotion")
    Q = deque()
    s = bfs_successors(G,"Emotion")
    #aggregateRanking[q
    #questions = sorted(s["Emotion"], key=ranking.get)
    #questions = sorted(s["Emotion"], key=G.out_degree)
    questions = sorted(s["Emotion"], key=lambda x: nx.eccentricity(U,x))
    print questions
    Q.extend(questions)
    seen.extend(questions)
    history = []
    count = 0
    while(Q):
        count = count+1
        print count
        #Q = sorted(Q, key=ranking.get)
        #Q = deque(Q)
        print Q
        t = Q.pop()
        if isinstance(t,tuple): qgloss = t[0]
        else: qgloss = t
        print "history"
        print history
        if history: 
            tmp1 = zip(*history)
            if qgloss in tmp1[0]: continue
        ans = ask(qgloss)
        history.append((qgloss,ans))
        if not isinstance(t,tuple): # we are dealing w/ guess as opposed to
                                    # question
            if ans == "yes": #found it
                print "awesome!"
                return t
            else:
                pass # no inference/action for wrong guess
        elif isinstance(t,tuple):  # we were asking a question, as opp to guess
            try:
                successors = bfs_successors(G,(qgloss,ans))
                #questions = sorted(s[t], key=ranking.get)
                questions = sorted(s[t], key=lambda x: nx.eccentricity(U,x))
                for q in questions:
                    if not q in seen:
                        seen.append(q)
                        #if(ans == "yes"):
                        Q.append(q)
                        #else:
                        #    Q.appendleft(q)
            except KeyError:
                successors = []

def calculate_max_ecc(graph, nodes):
    max_ecc = 0
    for node in nodes:
        ecc = nx.eccentricity(graph, node)
        if ecc > max_ecc:
            max_ecc = ecc
    return max_ecc

    def nodes_by_eccentricity(graph):
        if len(graph) == 1:
            return graph.nodes()
# need to crop the global shortest paths otherwise get 
#NetworkXError: Graph not connected: infinite path length
        eccentricities = nx.eccentricity(graph)
        return sorted(eccentricities.keys(), key = lambda n: eccentricities[n])

def whole_graph_metrics(graph, weighted=False):
    graph_metrics = {}

    # Shortest average path length
    graph_metrics['avg_shortest_path'] = \
        nx.average_shortest_path_length(graph, weight=weighted)

    # Average eccentricity
    ecc_dict = nx.eccentricity(graph)
    graph_metrics['avg_eccentricity'] = np.mean(np.array(ecc_dict.values()))

    # Average clustering coefficient
    # NOTE: Option to include or exclude zeros
    graph_metrics['avg_ccoeff'] = \
        nx.average_clustering(graph, weight=weighted, count_zeros=True)

    # Average node betweeness
    avg_node_btwn_dict = nx.betweenness_centrality(graph, normalized=True)
    graph_metrics['avg_node_btwn'] = \
        np.mean(np.array(avg_node_btwn_dict.values()))

    # Average edge betweeness
    avg_edge_btwn_dict = nx.edge_betweenness_centrality(graph, normalized=True)
    graph_metrics['avg_edge_btwn'] = \
        np.mean(np.array(avg_edge_btwn_dict.values()))

    # Number of isolates
    graph_metrics['isolates'] = len(nx.isolates(graph))

    return graph_metrics

    def basic_stats(self):
        #not decided on what level to deal with this yet:
        #either return error un not dealing with unconnected files,
        #or making it deal with unconnected files: the latter.
        #How about with dealing with each independently.
        #    if not nx.is_connected(g):
        #        conl= nx.connected_components(g)
        #        for n in conl:
        #            turn n into graph if it isnt
        #            calculate ec, per, cnt
        #            how and when to visualise the subgraphs?
        #            iterate to next n

        if nx.is_connected(self.nx_graph):
            ec = nx.eccentricity(self.nx_graph) 
        else:
            ec = 'NA - graph is not connected'

        per = nx.periphery(self.nx_graph)
        cnt = nx.center(self.nx_graph)
        result = { #"""fast betweenness algorithm"""  
            'bbc': nx.brandes_betweenness_centrality(self.nx_graph),
            'tn': nx.triangles(self.nx_graph), # number of triangles
            'ec': ec,
            'per': per,
            'cnt': cnt,
            'Per': self.nx_graph.subgraph(per),
            'Cnt': self.nx_graph.subgraph(cnt)
            }
        return result

def eccentricityAttributes(graph):
	return_values = []
	#Average effective eccentricity
	eccVals = []
	e = 0
	for n in graph.nodes():
		try: 
			eccVals.append(nx.eccentricity(graph, v=n))	
		except nx.NetworkXError:
			eccVals.append(0)
	eccSum = 0
	center_nodes = 0
	phobic = 0
	diameter = max(eccVals)
	radius = min(eccVals)
	for i in range(len(eccVals)):
		if eccVals[i] ==  radius:
			center_nodes += 1
			if graph.node[i]['hydro'] == 'phobic':
				phobic += 1
		eccSum += eccVals[i]
	return_values.append(eccSum / float(nx.number_of_nodes(graph)))	
	#Effective diameter
	return_values.append(diameter)
	#Effective radius
	return_values.append(radius)
	#Percentage central nodes
	return_values.append(center_nodes / float(nx.number_of_nodes(graph)))
	#Percentage central nodes that are hydrophobic
	return_values.append(phobic / float(center_nodes))
	return return_values

    def connected_components(self):
        """
        Returns basic statistics about the connected components of the
        graph. This includes their number, order, size, diameter, radius,
        average clusttering coefficient, transitivity, in addition to basic
        info about the largest and smallest connected components.
        """
        cc_stats = {}
        cc = nx.connected_components(self.graph.structure)

        for index, component in enumerate(cc):
            cc_stats[index] = {}
            this_cc = cc_stats[index]

            this_cc["order"] = len(component)
            this_cc["size"] = len(self.graph.structure.edges(component))

            subgraph = self.graph.structure.subgraph(component)
            this_cc["avg_cluster"] = nx.average_clustering(subgraph)
            this_cc["transitivity"] = nx.transitivity(subgraph)

            eccentricity = nx.eccentricity(subgraph)
            ecc_values = eccentricity.values()
            this_cc["diameter"] = max(ecc_values)
            this_cc["radius"] = min(ecc_values)

        return cc_stats

def print_graph_info(graph):
  e = nx.eccentricity(graph)
  print 'graph with %u nodes, %u edges' % (len(graph.nodes()), len(graph.edges()))
  print 'radius: %s' %  nx.radius(graph, e) # min e
  print 'diameter: %s' % nx.diameter(graph, e) # max e
  print 'len(center): %s' % len(nx.center(graph, e)) # e == radius
  print 'len(periphery): %s' % len(nx.periphery(graph, e)) # e == diameter

def get_nations_network_by_year(year):
  cursor = get_db().cursor()
  cursor.execute("""SELECT reporting, reporting_slug, partner, partner_slug, Flow, expimp,
                    reporting_continent, partner_continent,reporting_type,partner_type
                    FROM flow_joined
                    WHERE reporting NOT LIKE "Worl%%"
                    AND partner NOT LIKE "Worl%%"
                    AND Flow != "null"
                    AND year = %s
                    """%(year)
              )

  table = [list(r) for r in cursor]

  json_sql_response=[]

  for row in table:
    json_sql_response.append({
      "reporting": row[0],
      "reporting_id": row[1],
      "partner": row[2],
      "partner_id": row[3],
      "flow": row[4],
      "expimp": row[5],
      "reporting_continent": row[6],
      "partner_continent": row[7],
      "reporting_type": row[8],
      "partner_type": row[9]
      })


  # Create a graph instance
  G=nx.Graph()

  nodes = []
  for row in table:
    nodes.append(row[1])
    nodes.append(row[3])
    # add edge to the graph
    G.add_edge(row[1], row[3])

  nodes = set(nodes)

  # add nodes to graph
  G.add_nodes_from(nodes)
  if len(G.nodes())>0:
    stats = {
      "average_clustering": nx.average_clustering(G),
      "center": nx.center(G),
      "diameter": nx.diameter(G),
      "eccentricity": nx.eccentricity(G)
    }
  else:
    stats=[]
  json_response = {}
  json_response["stats"] = stats
  json_response["network"] = json_sql_response

  return json.dumps(json_response,encoding="UTF8")

def graph_diameter(graph):
    sp = nx.shortest_path_length(graph,weight='weight')
    ecc = nx.eccentricity(graph,sp=sp)
    if ecc:
        dia = nx.diameter(graph,e=ecc)
    else:
        dia = 0
    return dia

def graph_radius(graph):
    sp = nx.shortest_path_length(graph,weight='weight')
    ecc = nx.eccentricity(graph,sp=sp)
    if ecc:
        rad = nx.radius(graph,e=ecc)
    else:
        rad = 0
    return rad

 def OrigEccentricity(self):
     ''' returns a 2d array containing the eccentricity of the origin node for all edges
     ''' 
     sp = self.get_shortest_path_dict()
     probas = np.dot( 
                   np.array(nx.eccentricity(self, sp = sp).values(),dtype=float).reshape(-1,1),
                   np.ones((1,self.number_of_nodes())))
     return probas

    def nodes_by_eccentricity(graph):
        if len(graph) == 1:
            return graph.nodes()
# need to crop the global shortest paths otherwise get 
#NetworkXError: Graph not connected: infinite path length
        eccentricities = {}
        try:
            eccentricities = nx.eccentricity(graph)
        except nx.exception.NetworkXError:
# If not strongly connected, perform eccentricities per connected component
            if not nx.is_strongly_connected(graph):
                #TODO: provide this function inside ANK, add memoization for intensive operation
                for component_nodes in nx.strongly_connected_components(graph):
                    eccentricities.update(nx.eccentricity(graph.subgraph(component_nodes)))

# sort nodes by name, stability sort ensures that lexical order is used as tie-breaker for equal eccen.
        nodes_sorted = sorted(graph.nodes(), key = lambda x: x.fqdn)
        return sorted(nodes_sorted, key = lambda n: eccentricities[n])