Python networkx.write_graphml方法代码示例

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def to_graphml(graph: BELGraph, path: Union[str, BinaryIO], schema: Optional[str] = None) -> None:
    """Write a graph to a GraphML XML file using :func:`networkx.write_graphml`.

    :param graph: BEL Graph
    :param path: Path to the new exported file
    :param schema: Type of export. Currently supported: "simple" and "umbrella".

    The .graphml file extension is suggested so Cytoscape can recognize it.
    By default, this function exports using the PyBEL schema of including modifier information into the edges.
    As an alternative, this function can also distinguish between
    """
    if schema is None or schema == 'simple':
        rv = _to_graphml_simple(graph)
    elif schema == 'umbrella':
        rv = _to_graphml_umbrella(graph)
    else:
        raise ValueError('Unhandled schema: {}'.format(schema))

    nx.write_graphml(rv, path) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
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) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def main():
	# Step 1: Build up a graph 
	'''
	G = buildupgraph.build_graph_wikipedia_pagerank_example()
	out_file = 'wikipedia_pagerank_example.graphml'
	nx.write_graphml(G, out_file)
	'''

	in_file = 'wikipedia_pagerank_example_layout.graphml'	# Visualize the graph with the help of Graphviz
	G = buildupgraph.read_graphml_with_position(in_file)

	# Step 2: PageRank calculation
	node_and_pr = pagerank_iterative(G)

	# Normalized PageRank
	total_pr = sum(node_and_pr.values())		# 0.843339703286
	node_and_pr = {node : pr/total_pr for node, pr in node_and_pr.items()}

	# Plot the graph
	node_size = [pr*30000 for node, pr in node_and_pr.items()]
	node_and_labels = {node : node+'\n'+str(round(pr, 3))
							for node, pr in node_and_pr.items()}

	plotnxgraph.plot_graph(G, node_size=node_size, node_and_labels=node_and_labels) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def realign_all_nodes(inGraph, input_dict):
	logging.info('Running realign_all_nodes')

	realign_node_list = []

	iso_list = inGraph.graph['isolates'].split(',')

	# Load genomes into memory

	# Only need to realign nodes with more than one isolate in them
	for node, data in inGraph.nodes(data=True):
		# print(data)
		if len(data['ids'].split(',')) > 1:

			realign_node_list.append(node)

	# Realign the nodes. This is where multiprocessing will come in.
	for a_node in realign_node_list:

		inGraph = local_node_realign_new(inGraph, a_node, input_dict[1])

	nx.write_graphml(inGraph, 'intermediate_split_unlinked.xml')

	return inGraph 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def test_create_genome_alignment_graph(self):
		"""Is the correct block graph created?"""

		test_bbone_file = './unitTestFiles/globalAlignment_phyloTest.backbone'

		parsed_input_dict = parse_seq_file('./unitTestFiles/multiGenome.txt')



		created_test_graph = create_genome_alignment_graph(test_bbone_file, parsed_input_dict[0], parsed_input_dict[1])

		#nx.write_graphml(created_test_graph, './test_files/unit_tests/bbone_to_graph_test_out.xml')

		test_pass = True

		#print test_pass

		self.assertTrue(test_pass) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def save(self, path, **kwargs):
        # self.graph contains python lists in its meta obj, but write_graphml function does not know
        # how to serialize python lists, i.e. graphml does not support lists as a valid type for serialization.
        # As a workaround, we converts lists into comma-separated strings.
        # We use deepcopy to avoid modifying original self.graph object.
        dgraph = copy.deepcopy(self.graph)
        for n, nbrs in dgraph.adjacency():
            for nbr, eattr in nbrs.items():
                for entry, adjitem in eattr.items():
                    for prop_uri, obj_curies in adjitem.items():
                        if obj_curies is None:
                            adjitem[prop_uri] = ""
                        else:
                            if isinstance(obj_curies, list):
                                adjitem[prop_uri] = ','.join(obj_curies)
                            else:
                                adjitem[prop_uri] = str(obj_curies)

        nx.write_graphml(dgraph, path) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
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) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
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,weight=3)
        G.graph['node_default']={'color':'yellow'}
        G.graph['edge_default']={'weight':7}
        fh = io.BytesIO()
        nx.write_graphml(G,fh)
        fh.seek(0)
        H=nx.read_graphml(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()))
        assert_equal(G.graph,H.graph) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def test_unicode(self):
        G = nx.Graph()
        try: # Python 3.x
            name1 = chr(2344) + chr(123) + chr(6543)
            name2 = chr(5543) + chr(1543) + chr(324)
            node_type=str
        except ValueError: # Python 2.6+
            name1 = unichr(2344) + unichr(123) + unichr(6543)
            name2 = unichr(5543) + unichr(1543) + unichr(324)
            node_type=unicode
        G.add_edge(name1, 'Radiohead', attr_dict={'foo': name2})
        fd, fname = tempfile.mkstemp()
        nx.write_graphml(G, fname)
        H = nx.read_graphml(fname,node_type=node_type)
        assert_equal(G.adj, H.adj)
        os.close(fd)
        os.unlink(fname) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def gera_graphml_G(self, G, path):
        nx.write_graphml(G, path) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def write_graphml(self, path):

        """
        Serialize the graph as .graphml.

        Args:
            path (str)
        """

        nx.write_graphml(self.graph, path) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def dump_i_cfg(self):
        """Dump interim CFG for debugging purposes
        """

        print("[DEBUG] Dumping CFG")
        for u, v in self.i_cfg.edges():
            print("{} -> {}".format(u, v))

        print("[DEBUG] Writing GraphML...")
        # Labeling the nodes
        for node in self.i_cfg.nodes():
            self.i_cfg.node[node]['label'] = "{}".format(node)

        nx.write_graphml(self.i_cfg, r"D:\graphs\di.graphml") 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def generate_output_file(users_collection, edges_collection, keep_empty_nodes):
    graph = nx.DiGraph()
    nonempty_user_ids = set()

    for user in users_collection.find():
        nonempty_user_ids.add(user['id'])

        attrs_to_keep = [
            'name',
            'id',
            'geo_enabled',
            'followers_count',
            'protected',
            'lang',
            'utc_offset',
            'statuses_count',
            'friends_count',
            'screen_name',
            'url',
            'location',
            ]
        user_attrs = { key : user[key] or '' for key in attrs_to_keep }
        graph.add_node(user['id'], attr_dict=user_attrs)

    for edge in edges_collection.find():
        if keep_empty_nodes or edge['from'] in nonempty_user_ids and edge['to'] in nonempty_user_ids:
            graph.add_edge(edge['from'], edge['to'])

    nx.write_graphml(graph, sys.stdout) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def main():
	# Step 1: Build up a graph 
	G = build_graph_wikipedia_pagerank_example()
	out_file = 'wikipedia_pagerank_example.graphml'
	nx.write_graphml(G, out_file)

	'''
	in_file = 'wikipedia_pagerank_example_layout.graphml'
	G = read_graphml_with_position(in_file)
	''' 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import write_graphml [as 别名]
def write_graphml(self, path):

        """
        Write a GraphML file.

        Args:
            path (str): The file path.
        """

        nx.write_graphml(self.graph, path)