Python networkx.write_pajek函数代码示例

def getGraph(fileRef):
    data = getFiles(fileName)
    nodes = getNodes(data)
    edges = getEdges(data)
    graph = createNetwork(edges, nodes)

    gml_output_path = os.path.join('output', 'network',
                                   fileRef.
                                   split('.')[0].
                                   split('/')[1] + '.gml')

    print "Writing GML file to %s" % gml_output_path
    nx.write_gml(graph, gml_output_path)

    net_output_path = os.path.join('output', 'network',
                                   fileRef.
                                   split('.')[0].
                                   split('/')[1] + '.net')

    print "Writing net file to %s" % net_output_path
    nx.write_pajek(graph, net_output_path)

    params = (graph.number_of_nodes(), graph.number_of_edges())
    print "Graph has %s nodes, %s edges" % params
    print

def lei_vs_lei(nedges=None):
    """
    Grafo de todas com todas (leis)
    """
    # Verão original Flávio comentada
    # curgrafo.execute('select lei_id_1,esfera_1,lei_1,lei_id_2,esfera_2, lei_2, peso from vw_gr_lei_lei where  peso >300 and lei_id_2>2')
    # curgrafo.execute('select lei_id_1,lei_tipo_1,lei_nome_1,lei_id_2,lei_tipo_2, lei_nome_2, peso from vw_gr_lei_lei where lei_count <= 20 and lei_id_1 = 1 and lei_id_2 <= 20 limit 0,1000')
    # curgrafo.execute('select lei_id_1,lei_tipo_1,lei_nome_1,lei_id_2,lei_tipo_2, lei_nome_2, peso from vw_gr_lei_lei where lei_count <= 8 and lei_id_1 <= 20 and lei_id_2 <= 20 limit 0,1000')
    curgrafo.execute('select lei_id_1,esfera_1,lei_1,lei_id_2,esfera_2, lei_2, peso from vw_gr_lei_lei where lei_count <= 10 and lei_id_1 <= 50 and lei_id_2 <= 200 limit 0,10000')
    if not nedges:
        res = curgrafo.fetchall()
        nedges = len(res)
    else:
        res = curgrafo.fetchmany(nedges)
    eds = [(i[0],i[3],i[6]) for i in res]
    G = nx.Graph()
    #eds = [i[:3] for i in res]
    G.add_weighted_edges_from(eds)
    print "== Grafo Lei_Lei =="
    print "==> Order: ",G.order()
    print "==> # Edges: ",len(G.edges())
    # Adding attributes to nodes
    for i in res:
        G.node[i[0]]['esfera'] = i[1]
        G.node[i[0]]['lei'] = i[2]
        G.node[i[3]]['esfera'] = i[4]
        G.node[i[3]]['lei'] = i[5]
    nx.write_graphml(G,'lei_lei.graphml')
    nx.write_gml(G,'lei_lei.gml')
    nx.write_pajek(G,'lei_lei.pajek')
    nx.write_dot(G,'lei_lei.dot')
    return G,res

def generate_graphs_from_baited_loci():
    
    # Adjacent duplets
    print "Loading adjacent duplets"
    graph, duplets = load_adjacent_duplets_graph()
    linked_list_file = os.path.join(work_dir, "baited_adj_duplets.txt")
    with open(linked_list_file, "w") as outf:
        outf.write("#From\tTo\tWeight\n")
        for edge in graph.edges_iter(data=True):
            _from = edge[0]
            _to = edge[1]
            _weight = edge[2]["weight"]
            outf.write("%s\t%s\t%s\n" % (_from, _to, _weight))
    print "Writing adjacent duplets"
    pajek_file = os.path.join(work_dir, "baited_adj_duplets.net")
    nx.write_pajek(graph, pajek_file)
    
    # duplets
    print "Loading duplets"
    graph, duplets = load_duplets_graph()
    print "Writing duplets"
    linked_list_file = os.path.join(work_dir, "baited_duplets.txt")
    with open(linked_list_file, "w") as outf:
        outf.write("#From\tTo\tWeight\n")
        for edge in graph.edges_iter(data=True):
            _from = edge[0]
            _to = edge[1]
            _weight = edge[2]["weight"]
            outf.write("%s\t%s\t%s\n" % (_from, _to, _weight))
    pajek_file = os.path.join(work_dir, "baited_duplets.net")
    nx.write_pajek(graph, pajek_file)

    def saveGraphPajek(self, filename=None):
        if filename is not None:
            self.filename = filename

        nx.write_pajek(self.G, self.filename)

        logger.info(u"Saved - %s" % self.filename)

def make_network(raw_file_list):

# handles the individual nodes
    collect_nodes_by_source = []
    list_of_source_names = []
    node_list = []

    GB = nx.Graph()

# for all the nodes...
    for i in range(len(raw_file_list)):    
        # check whether they are name, source or else (not returned). "i" works as an index to identify the respective node when it comes back
        checker, a = my_containsAny(raw_file_list[i], i)

        # raw data starts with a source, all following non-source lines refer to names or tags. So all returned nodes should be linked to each other
        
        if checker == "source":
            GB.add_node(raw_file_list[a], bipartite = 0)
            source = raw_file_list[a]

        while source == raw_file_list[a]:
            if checker == "node":
                GB.add_node(raw_file_list[a], bipartite = 1)  
                GB.add_edge(raw_file_list[a], raw_file_list[a+1])


    G = bnx.weighted_projected_graph(GB, GB.nodes(["bipartite"]==1))

    #nx.write_graphml(GB, "abolitionists_bipartite.graphml")
    nx.write_pajek(G, "abolitionists.net")

    print "done!"

def ministro_ministro(G):
    """
    Cria um grafo de ministros conectados de acordo com a sobreposição de seu uso da legislação
    Construido a partir to grafo ministro_lei
    """
    GM = nx.Graph()
    for m in G:
        try:
            int(m)
        except ValueError:# Add only if node is a minister
            if m != "None":
                GM.add_node(m.decode('utf-8'))
#    Add edges
    for n in GM:
        for m in GM:
            if n == m: continue
            if GM.has_edge(n,m) or GM.has_edge(m,n): continue
            # Edge weight is the cardinality of the intersection each node neighbor set.
            w = len(set(nx.neighbors(G,n.encode('utf-8'))) & set(nx.neighbors(G,m.encode('utf-8')))) #encode again to allow for matches
            if w > 5:
                GM.add_edge(n,m,{'weight':w})
    # abreviate node names
    GMA = nx.Graph()
    GMA.add_weighted_edges_from([(o.replace('MIN.','').strip(),d.replace('MIN.','').strip(),di['weight']) for o,d,di in GM.edges_iter(data=True)])
    P.figure()
    nx.draw_spectral(GMA)
    nx.write_graphml(GMA,'ministro_ministro.graphml')
    nx.write_gml(GMA,'ministro_ministro.gml')
    nx.write_pajek(GMA,'ministro_ministro.pajek')
    nx.write_dot(GMA,'ministro_ministro.dot')
    return GMA

def addTracks(artist, tracks, artistGraph):
	for track in tracks:
	# get list of users who have favorited this user's track
		favoriters = client.get('/tracks/' + str(track) + '/favoriters')
		print "Add Favoriters"
		for user in favoriters:
			addWeight(user.id, artist, artistGraph, 'fav_weight')
		try:
			print "Writing out new artists..."
			nx.write_pajek(artistGraph, 'artistGraph.net')
			print "New artists written successfully!"
			print "The artist graph currently contains " + str(len(artistGraph)) + " artists."
			print "The artist graph currently contains " + str(nx.number_strongly_connected_components(artistGraph)) + " strongly connected components."
		except IOError:
			print "New artists could not be written..."
	
	# get list of users who have commented on this user's track			
		commenters = client.get('/tracks/' + str(track) + '/comments')
		print "Add Commenters"
		for user in commenters:
			addWeight(user.user_id, artist, artistGraph, 'com_weight')
		try:
			print "Writing out new artists..."
			nx.write_pajek(artistGraph, 'artistGraph.net')
			print "New artists written successfully!"
			print "The artist graph currently contains " + str(len(artistGraph)) + " artists."
			print "The artist graph currently contains " + str(nx.number_strongly_connected_components(artistGraph)) + " strongly connected components."
		except IOError:
			print "New artists could not be written..."			

def main(max_depth = 2, graphfile='music_network.net', id_mapfile='artist_id_map.p'):
    g = net.Graph()
    id_map = []
    for artist_id, artist_name in get_playlist_artists('science'):
        id_map.append({'id': artist_id, 'name':artist_name})
        snowball_sampling(g, artist_search, artist_id, id_map=id_map, max_depth=max_depth)
    net.write_pajek(g, graphfile)
    pickle.dump(id_map, open(id_mapfile,'wb')) 

def write_graph(G, gfile):
    try:
        print "Writing out new artists..."
        nx.write_pajek(G, gfile)
        print "New artists written successfully!"
        print "The artist graph currently contains " + str(len(G)) + " artists."
        print "The artist graph currently contains " + str(nx.number_strongly_connected_components(G)) + " strongly connected components."
    except IOError:
        print "New artists could not be written..."

def processDirectory(dir_path, top_number, lemma_flag):
    """
        Process the directory the user inputs
    """
    if (not lemma_flag):
        print 'NO LEMMATIZING'
    
    cross_graph = nx.MultiDiGraph()

    for file_name in os.listdir(dir_path):
        file_path = os.path.join(dir_path, file_name)
        if os.path.isfile(file_path) and file_name.endswith('.txt'):
            print 'analyzing ' + file_name, '...'
            try:
                file_open = io.open(file_path, 'r')
            except IOError:
                print 'OPEN FILE ' + file_path + ' ERROR'
                sys.exit(1)
            sent_seg = preprocessSent(file_open.read())
            sent_depend = produceDepend(sent_seg, lemma_flag)
            # print sent_seg
            # print sent_depend
            file_open.close()
            
            single_graph = drawDependGraph(sent_depend, sent_seg, dir_path, file_name, lemma_flag)
            
            # Doing the combination
            cross_graph.add_nodes_from([v for v, d in  single_graph.nodes(data = True) if v not in cross_graph.nodes()], freq = 0)
            
            for u, v, d in single_graph.edges(data = True):
                if (u, v) not in cross_graph.edges():
                    cross_graph.add_edge(u, v, dependency = d['dependency'], label = d['label'], freq = 0)
                else:
                    list_dif_depend = [cross_graph[u][v][i]['dependency'] for i in range(len(cross_graph[u][v].items()))]
                    if d['dependency'] not in list_dif_depend:
                        cross_graph.add_edge(u, v, dependency = d['dependency'], label = d['label'], freq = 0)
                    

            for v, d in cross_graph.nodes(data = True):
                if v in single_graph.nodes():
                    d['freq'] += single_graph.node[v]['freq']
            
            for u, v, d in cross_graph.edges(data = True):
                if (u, v) in single_graph.edges():
                    list_dif_depend = [single_graph[u][v][i]['dependency'] for i in range(len(single_graph[u][v].items()))]
                    dict_dif_depend = dict(zip(list_dif_depend, range(len(single_graph[u][v].items()))))
                    if d['dependency'] in list_dif_depend:
                        depend_index = dict_dif_depend[d['dependency']]
                        d['freq'] += single_graph[u][v][depend_index]['freq']

    if lemma_flag:
        nx.write_pajek(cross_graph, dir_path + 'syntactic_lemma/' + 'syntactic_graph_cross_txt_lemma.net')
        graphAnalysis(cross_graph, top_number, dir_path + 'syntactic_lemma/' + 'syntactic_graph_lemma_analysis.csv')
    else:
        nx.write_pajek(cross_graph, dir_path + 'syntactic_no_lemma/' + 'syntactic_graph_cross_txt_no_lemma.net')
        graphAnalysis(cross_graph, top_number, dir_path + 'syntactic_no_lemma/' + 'syntactic_graph_analysis_no_lemma.csv')

def save_friend_list(friend_list,path='/home/pj/Python/social_analysis/',filename='friend.net'):
    fullpath=path+filename
    print '开始保存社交关系'
    g=networkx.Graph()
    for name in friend_list.keys():
        for node in friend_list[name]:
            g.add_edge(name,node[1])
    networkx.write_pajek(g,fullpath)
    print '好友列表存储在%s'%(fullpath)
    return 1

 def write_graph_in_format(self, filerootname, fileformat='gexf'):
     fileformat = fileformat.lower()
     filename = "%s.%s" % (filerootname.replace(" ", "_"), fileformat)
     if fileformat == 'json':
         with open(filename, 'w') as f:
             json.dump(json_graph.node_link_data(self), f)
     elif fileformat == 'net':
         nx.write_pajek(self, filename)
     elif fileformat == 'gexf':
         nx.write_gexf(self, filename)

def writepajek(A,filename):
    SP = np_to_scipy_matrix(A)
    edges = []
    for i in range(0,A.shape[0]):
        for j in range(0,A.shape[1]):
            if A[i,j]!=0:
                edges.append(['A'+str(i),'B'+str(j)])

    G = nx.bipartite.from_biadjacency_matrix(SP,create_using=None)
    nx.write_pajek(G, filename + '.net')

  def run(self):
    # Run simulation for several type of networks, in this case Erdos-Renyi and Random Network
    self.networks = [
      {
        'network': nx.scale_free_graph(n = self.nodes),
        'name': 'ScaleFree'
      },
      {
        'network': nx.erdos_renyi_graph(n = self.nodes, p = 0.1), # 0.2, 0.5
        'name': 'ErdosRenyi'
      },
      {
        'network': nx.random_regular_graph(n = self.nodes, d = 10),
        'name': 'RandomNetwork'
      }
    ]

    for network in self.networks:
      nxgraph = network['network']
      graph = helper.convert_nxgraph_to_dict(nxgraph)

      # write network in pajek
      filename = 'pajek/'+ network['name'] + '_' + str(self.nodes) + '.net'
      nx.write_pajek(nxgraph, filename)
      
      for mu in self.mu_values:
        print 'Starting...'
        start_time = time.time()

        # B beta (at least 51 values, B=0.02)
        beta = 0.0
        betas = []
        averages = []
        for i in range(0, 51):
          start_time_beta = time.time()
          sis_initial_model = sis.SIS(graph, mu, beta, self.p0)
          simulation = mc.MonteCarlo(sis_initial_model, self.rep, self.t_max, self.t_trans)
          total_average = simulation.run()

          total_time_beta = time.time() - start_time_beta
          betas.append(beta)
          averages.append(total_average)

          print 'B: {}, average: {}, time: {}s'.format(beta, total_average, total_time_beta)
          beta += 0.02

        total_time = time.time() - start_time
        print 'total time: {}'.format(total_time)

        # plot results and save in file
        helper.plot_results(network['name'], self.nodes, mu, betas, averages)
        helper.write_results(network['name'], self.nodes, mu, betas, averages)

      break 

def main():
    badges = user_badge_extract('Badges.xml')
    G = create_graph(badges)
    
#    draw_graph(G)
    
    # Saving the graph in Pajek format
    nx.write_pajek(G, "graph.net")
    
    # Saving the graph as AdjList
    nx.write_adjlist(G, "graph.adjlist")