Python networkx.write_gexf方法代碼示例

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def write_graph(self, G=None, subgraph_file=None):
        if G is None:
            G = self.context_graph
        if subgraph_file is None:
            subgraph_file = self.context_graph_file
        logging.info("Writing graph.")
        # write the graph out
        file_format = subgraph_file.split(".")[-1]
        if file_format == "graphml":
            nx.write_graphml(G, subgraph_file)
        elif file_format == "gml":
            nx.write_gml(G, subgraph_file)
        elif file_format == "gexf":
            nx.write_gexf(G, subgraph_file)
        elif file_format == "net":
            nx.write_pajek(G, subgraph_file)
        elif file_format == "yaml":
            nx.write_yaml(G, subgraph_file)
        elif file_format == "gpickle":
            nx.write_gpickle(G, subgraph_file)
        else:
            print "File format not found, writing graphml."
            nx.write_graphml(G, subgraph_file) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def save(self, name, resolution, gain, equalize=True, cluster='agglomerative', statistics='db', max_K=5):
        """
        Generates a topological representation using the Mapper algorithm with resolution and gain specified by the
        parameters 'resolution' and 'gain'. When equalize is set to True, patches are chosen such that they
        contain the same number of points. The parameter 'cluster' specifies the clustering method ('agglomerative' or
        'kmeans'). The parameter 'statistics' specifies the criterion for choosing the optimal number of clusters
        ('db' for Davies-Bouildin index, or 'gap' for the gap statistic). The parameter 'max_K' specifies the maximum
        number of clusters to be considered within each patch. The topological representation is stored in the files
        'name.gexf' and 'name.json'. It returns a dictionary with the patches.
        """
        G, all_clusters, patches = sakmapper.mapper_graph(self.df, lens_data=self.lens_data_mds,
                                                          resolution=resolution,
                                                          gain=gain, equalize=equalize, clust=cluster,
                                                          stat=statistics, max_K=max_K)
        dic = {}
        for n, rs in enumerate(all_clusters):
            dic[str(n)] = map(lambda x: int(x), rs)
        with open(name + '.json', 'wb') as handle3:
            json.dump(dic, handle3)
        networkx.write_gexf(G, name + '.gexf')
        return patches 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def WriteAdjList(adjList, graphName, colorLabels=None):
    g = nx.Graph()

    for i in range(0,len(adjList)):
        if (colorLabels is not None):
            g.add_node(i, color=colorLabels[i], id=str(i))
        else:
            g.add_node(i)

    idx = 0
    for i in range(0,len(adjList)):
        for j in range(0,len(adjList[i])):
            g.add_edge(i, adjList[i][j][0], capacity=adjList[i][j][1])

    if (graphName.find("gml") >= 0):
        nx.write_gml(g, graphName)
    elif (graphName.find("gexf") >= 0):
        nx.write_gexf(g, graphName)
    elif (graphName.find("graphml") >= 0):
        nx.write_graphml(g, graphName) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def test_default_attribute(self):
        G=nx.Graph()
        G.add_node(1,label='1',color='green')
        G.add_path([0,1,2,3])
        G.add_edge(1,2,foo=3)
        G.graph['node_default']={'color':'yellow'}
        G.graph['edge_default']={'foo':7}
        fh = io.BytesIO()
        nx.write_gexf(G,fh)
        fh.seek(0)
        H=nx.read_gexf(fh,node_type=int)
        assert_equal(sorted(G.nodes()),sorted(H.nodes()))
        assert_equal(
            sorted(sorted(e) for e in G.edges()),
            sorted(sorted(e) for e in H.edges()))
        # Reading a gexf graph always sets mode attribute to either
        # 'static' or 'dynamic'. Remove the mode attribute from the
        # read graph for the sake of comparing remaining attributes.
        del H.graph['mode']
        assert_equal(G.graph,H.graph) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def test_default_attribute(self):
        G = nx.Graph()
        G.add_node(1, label='1', color='green')
        nx.add_path(G, [0, 1, 2, 3])
        G.add_edge(1, 2, foo=3)
        G.graph['node_default'] = {'color': 'yellow'}
        G.graph['edge_default'] = {'foo': 7}
        fh = io.BytesIO()
        nx.write_gexf(G, fh)
        fh.seek(0)
        H = nx.read_gexf(fh, node_type=int)
        assert_equal(sorted(G.nodes()), sorted(H.nodes()))
        assert_equal(
            sorted(sorted(e) for e in G.edges()),
            sorted(sorted(e) for e in H.edges()))
        # Reading a gexf graph always sets mode attribute to either
        # 'static' or 'dynamic'. Remove the mode attribute from the
        # read graph for the sake of comparing remaining attributes.
        del H.graph['mode']
        assert_equal(G.graph, H.graph) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def gera_gexf_G(self, G, path):
        # Antes de gerar esse formato, necessario adaptar alguns atributos do grafo
        G_adapt = G.copy()

        pos = nx.spring_layout(G_adapt, dim=4, scale=1000)
        
        for node in G_adapt.nodes:
            tipo_pessoa = G_adapt.nodes[node]['tipo_pessoa']

            G_adapt.nodes[node]['label'] = G_adapt.nodes[node]['nome']

            # Configura atributos de visualizacao necessarios para alguns leitores
            G_adapt.nodes[node]['viz'] = {'size':10}

            if tipo_pessoa == 1:
                if G_adapt.nodes[node]['situacao'] == '02':
                    G_adapt.nodes[node]['viz']['color'] = {'a':1,'r':1,'g':57,'b':155}
                else:
                    G_adapt.nodes[node]['viz']['color'] = {'a':1,'r':255,'g':0,'b':0}
            else:
                G_adapt.nodes[node]['viz']['color'] = {'a':1,'r':46,'g':125,'b':32}

            G_adapt.nodes[node]['viz']['position'] = {'x':pos[node][0],
                                                      'y':pos[node][1],
                                                      'z':5}

            # Converte cols para float, por incompatibilidade do nx com o numpy.float64
            for coluna in COL_FLOAT64:
                if coluna in G_adapt.nodes[node]:
                    G_adapt.nodes[node][coluna] = float(G_adapt.nodes[node][coluna])

        nx.write_gexf(G_adapt, path) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def write(self,path):
        nx.write_gexf(self.G, path) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def get_weights(self):
        G = self.G
        pr = self.pr
        max_pagerank = max(pr.itervalues())
        # get the largest count to scale weights between 0 and 1.

        t = datetime.datetime.now()
        ts = int(time.mktime(t.timetuple()))
        temp = tempfile.mktemp(prefix=str(ts), suffix=".gexf")

        nx.write_gexf(G, temp)

        for (k, v) in pr.iteritems():
            yield (k, float(v / max_pagerank)) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def WriteGraph(g, graphName, colorLabels=None):

    if (graphName.find("gml") >= 0):
        nx.write_gml(g, graphName)
    elif (graphName.find("gexf") >= 0):
        nx.write_gexf(g, graphName)
    elif (graphName.find("graphml") >= 0):
        nx.write_graphml(g, graphName) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def test_write_read_simple_directed_graphml(self):
        G=self.simple_directed_graph
        fh=io.BytesIO()
        nx.write_gexf(G,fh)
        fh.seek(0)
        H=nx.read_gexf(fh)
        assert_equal(sorted(G.nodes()),sorted(H.nodes()))
        assert_equal(sorted(G.edges()),sorted(H.edges()))
        assert_equal(sorted(G.edges(data=True)),
                     sorted(H.edges(data=True)))
        self.simple_directed_fh.seek(0) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def test_serialize_ints_to_strings(self):
        G=nx.Graph()
        G.add_node(1,id=7,label=77)
        fh = io.BytesIO()
        nx.write_gexf(G,fh)
        fh.seek(0)
        H=nx.read_gexf(fh,node_type=int)
        assert_equal(H.nodes(),[7])
        assert_equal(H.node[7]['label'],'77') 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def test_bool(self):
        G=nx.Graph()
        G.add_node(1, testattr=True)
        fh = io.BytesIO()
        nx.write_gexf(G,fh)
        fh.seek(0)
        H=nx.read_gexf(fh,node_type=int)
        assert_equal(H.node[1]['testattr'], True) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def write_gexf(G, path, encoding='utf-8',prettyprint=True,version='1.1draft'):
    """Write G in GEXF format to path.

    "GEXF (Graph Exchange XML Format) is a language for describing
    complex networks structures, their associated data and dynamics" [1]_.

    Parameters
    ----------
    G : graph
       A NetworkX graph
    path : file or string
       File or file name to write.
       File names ending in .gz or .bz2 will be compressed.
    encoding : string (optional)
       Encoding for text data.
    prettyprint : bool (optional)
       If True use line breaks and indenting in output XML.

    Examples
    --------
    >>> G=nx.path_graph(4)
    >>> nx.write_gexf(G, "test.gexf")

    Notes
    -----
    This implementation does not support mixed graphs (directed and undirected
    edges together).

    The node id attribute is set to be the string of the node label.
    If you want to specify an id use set it as node data, e.g.
    node['a']['id']=1 to set the id of node 'a' to 1.

    References
    ----------
    .. [1] GEXF graph format, http://gexf.net/format/
    """
    writer = GEXFWriter(encoding=encoding,prettyprint=prettyprint,
                        version=version)
    writer.add_graph(G)
    writer.write(path) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def test_write_read_simple_directed_graphml(self):
        G = self.simple_directed_graph
        fh = io.BytesIO()
        nx.write_gexf(G, fh)
        fh.seek(0)
        H = nx.read_gexf(fh)
        assert_equal(sorted(G.nodes()), sorted(H.nodes()))
        assert_equal(sorted(G.edges()), sorted(H.edges()))
        assert_equal(sorted(G.edges(data=True)),
                     sorted(H.edges(data=True)))
        self.simple_directed_fh.seek(0) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import write_gexf [as 別名]
def test_serialize_ints_to_strings(self):
        G = nx.Graph()
        G.add_node(1, id=7, label=77)
        fh = io.BytesIO()
        nx.write_gexf(G, fh)
        fh.seek(0)
        H = nx.read_gexf(fh, node_type=int)
        assert_equal(list(H), [7])
        assert_equal(H.nodes[7]['label'], '77')