Python networkx.write_graphml() Examples

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) 

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) 

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) 

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) 

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) 

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) 

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 

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) 

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) 

def test_write_interface(self):
        try:
            import lxml.etree
            assert_equal(nx.write_graphml, nx.write_graphml_lxml)
        except ImportError:
            assert_equal(nx.write_graphml, nx.write_graphml_xml) 

def test_preserve_multi_edge_data(self):
        """
        Test that data and keys of edges are preserved on consequent
        write and reads
        """
        G = nx.MultiGraph()
        G.add_node(1)
        G.add_node(2)
        G.add_edges_from([
            # edges with no data, no keys:
            (1, 2),
            # edges with only data:
            (1, 2, dict(key='data_key1')),
            (1, 2, dict(id='data_id2')),
            (1, 2, dict(key='data_key3', id='data_id3')),
            # edges with both data and keys:
            (1, 2, 103, dict(key='data_key4')),
            (1, 2, 104, dict(id='data_id5')),
            (1, 2, 105, dict(key='data_key6', id='data_id7')),
        ])
        fh = io.BytesIO()
        nx.write_graphml(G, fh)
        fh.seek(0)
        H = nx.read_graphml(fh, node_type=int)
        assert_edges_equal(
            G.edges(data=True, keys=True), H.edges(data=True, keys=True)
        )
        assert_equal(G._adj, H._adj) 

def write_graphml(self, graphml_file):
        nx.write_graphml(
            self.G,
            graphml_file,

            # Determine if numeric types should be generalized. For example,
            # if edges have both int and float 'weight' attributes,
            # we infer in GraphML that both are floats.
            infer_numeric_types=True
        ) 

def write_graphml(G, path, encoding='utf-8',prettyprint=True):
    """Write G in GraphML XML format to path

    Parameters
    ----------
    G : graph
       A networkx graph
    path : file or string
       File or filename to write.
       Filenames 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_graphml(G, "test.graphml")

    Notes
    -----
    This implementation does not support mixed graphs (directed and unidirected
    edges together) hyperedges, nested graphs, or ports.
    """
    writer = GraphMLWriter(encoding=encoding,prettyprint=prettyprint)
    writer.add_graph_element(G)
    writer.dump(path) 

def write_graphml_xml(G, path, encoding='utf-8', prettyprint=True,
                      infer_numeric_types=False):
    """Write G in GraphML XML format to path

    Parameters
    ----------
    G : graph
       A networkx graph
    path : file or string
       File or filename to write.
       Filenames 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.
    infer_numeric_types : boolean
       Determine if numeric types should be generalized.
       For example, if edges have both int and float 'weight' attributes,
       we infer in GraphML that both are floats.

    Examples
    --------
    >>> G = nx.path_graph(4)
    >>> nx.write_graphml(G, "test.graphml")

    Notes
    -----
    It may be a good idea in Python2 to convert strings to unicode
    before giving the graph to write_gml. At least the strings with
    either many characters to escape.

    This implementation does not support mixed graphs (directed
    and unidirected edges together) hyperedges, nested graphs, or ports.
    """
    writer = GraphMLWriter(encoding=encoding, prettyprint=prettyprint,
                           infer_numeric_types=infer_numeric_types)
    writer.add_graph_element(G)
    writer.dump(path) 

def test_preserve_multi_edge_data(self):
        """
        Test that data and keys of edges are preserved on consequent
        write and reads
        """
        G = nx.MultiGraph()
        G.add_node(1)
        G.add_node(2)
        G.add_edges_from([
            # edges with no data, no keys:
            (1, 2),
            # edges with only data:
            (1, 2, dict(key='data_key1')),
            (1, 2, dict(id='data_id2')),
            (1, 2, dict(key='data_key3', id='data_id3')),
            # edges with both data and keys:
            (1, 2, 103, dict(key='data_key4')),
            (1, 2, 104, dict(id='data_id5')),
            (1, 2, 105, dict(key='data_key6', id='data_id7')),
        ])
        fh = io.BytesIO()
        nx.write_graphml(G, fh)
        fh.seek(0)
        H = nx.read_graphml(fh, node_type=int)
        assert_edges_equal(
            G.edges(data=True, keys=True), H.edges(data=True, keys=True)
        )
        assert_equal(G._adj, H._adj) 

def test_write_interface(self):
        try:
            import lxml.etree
            assert_equal(nx.write_graphml, nx.write_graphml_lxml)
        except ImportError:
            assert_equal(nx.write_graphml, nx.write_graphml_xml) 

def write_graphml_xml(G, path, encoding='utf-8', prettyprint=True,
                      infer_numeric_types=False):
    """Write G in GraphML XML format to path

    Parameters
    ----------
    G : graph
       A networkx graph
    path : file or string
       File or filename to write.
       Filenames 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.
    infer_numeric_types : boolean
       Determine if numeric types should be generalized.
       For example, if edges have both int and float 'weight' attributes,
       we infer in GraphML that both are floats.

    Examples
    --------
    >>> G = nx.path_graph(4)
    >>> nx.write_graphml(G, "test.graphml")

    Notes
    -----
    It may be a good idea in Python2 to convert strings to unicode
    before giving the graph to write_gml. At least the strings with
    either many characters to escape.

    This implementation does not support mixed graphs (directed
    and unidirected edges together) hyperedges, nested graphs, or ports.
    """
    writer = GraphMLWriter(encoding=encoding, prettyprint=prettyprint,
                           infer_numeric_types=infer_numeric_types)
    writer.add_graph_element(G)
    writer.dump(path) 

def test_write_read_simple_directed_graphml(self):
        G=self.simple_directed_graph
        fh=io.BytesIO()
        nx.write_graphml(G,fh)
        fh.seek(0)
        H=nx.read_graphml(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) 

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) 

def write_graph(self, graph_path):
        nx.write_graphml(self.G, graph_path) 

def construct_graph(frame, ligands=None, prefix="frame", threshold=6.7, save_graph=True):
    atom_filter = "(name CB and protein) or (name CA and resname GLY)"
    if ligands:
        ligands = ligands.split(",")

        for ligand in ligands:
            arr = ligand.split(":")
            atom_filter += " or (name %s and resname %s)" % (arr[1], arr[0])

    atoms = frame.topology.select(atom_filter)

    nodes_range = len(atoms)

    nodes = range(0, len(atoms))
    edges = []

    for i in range(nodes_range - 1):
        for j in range(i + 1, nodes_range):
            dist = calc_distance(frame, atoms[i], atoms[j]) * 10
            if dist < threshold:
                edges.append((i, j))

    protein_graph = nx.Graph()
    protein_graph.add_nodes_from(nodes)
    protein_graph.add_edges_from(edges)

    if save_graph:
        nx.write_gml(protein_graph, "%s_graph.gml" % prefix)
        nx.write_graphml(protein_graph, "%s_graph.graphml" % prefix)

    return protein_graph 

def test_alignment_to_graph_conversion(self):
		"""Are fasta alignment files correctly converted to graphs?"""

		test_fasta_aln = './unitTestFiles/alignedSeq.fa'

		test_start_dict = {'seq1':1,'seq2':-50,'seq3':1}

		fasta_object = input_parser(test_fasta_aln)

		new_graph = fasta_alignment_to_subnet(test_fasta_aln, true_start=test_start_dict, node_prefix='X', orientation={}, re_link_nodes=True, add_seq=True)

		nx.write_graphml(new_graph, './unitTestFiles/alignedSeq.xml')

		seq1_seq = extract_original_seq(new_graph, 'seq1')
		seq2_seq =  extract_original_seq(new_graph, 'seq2')
		seq3_seq =  extract_original_seq(new_graph, 'seq3')

		does_pass = True

		print seq1_seq
		print 'GAGATTAGGAGTAGATAGATAGATATTTAGAGCCCGGAAAATTTATATTATTTAAT'
		print seq2_seq
		print reverse_compliment('GATTAGGAGTAGATAGATAGATATTTAGAGAGAGAAAATTTATATTATTT')
		print seq3_seq
		print 'GAGATTAGGAGTAGATAGATAGTATTTAGAGAGAGAAAATTTATATTATTTAAT'

		if seq1_seq != 'GAGATTAGGAGTAGATAGATAGATATTTAGAGCCCGGAAAATTTATATTATTTAAT':
			does_pass = False
			print 'seq1 fails'

		if seq2_seq != reverse_compliment('GATTAGGAGTAGATAGATAGATATTTAGAGAGAGAAAATTTATATTATTT'):
			does_pass = False
			print 'seq2 fails'

		if seq3_seq != 'GAGATTAGGAGTAGATAGATAGTATTTAGAGAGAGAAAATTTATATTATTTAAT':
			does_pass = False
			print 'seq3 fails'		

		self.assertTrue(does_pass) 

def write_graphml(self, path):

        """
        Write a GraphML file.

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

        nx.write_graphml(self.graph, path) 

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)
	''' 

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) 

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") 

def write_graphml(self, path):

        """
        Serialize the graph as .graphml.

        Args:
            path (str)
        """

        nx.write_graphml(self.graph, path) 

def gera_graphml_G(self, G, path):
        nx.write_graphml(G, path) 

def androcg_main(verbose,
                 APK,
                 classname,
                 methodname,
                 descriptor,
                 accessflag,
                 no_isolated,
                 show,
                 output):
    from androguard.core.androconf import show_logging
    from androguard.core.bytecode import FormatClassToJava
    from androguard.misc import AnalyzeAPK
    import networkx as nx
    import logging
    log = logging.getLogger("androcfg")
    if verbose:
        show_logging(logging.INFO)

    a, d, dx = AnalyzeAPK(APK)

    entry_points = map(FormatClassToJava,
                       a.get_activities() + a.get_providers() +
                       a.get_services() + a.get_receivers())
    entry_points = list(entry_points)

    log.info("Found The following entry points by search AndroidManifest.xml: "
             "{}".format(entry_points))

    CG = dx.get_call_graph(classname,
                           methodname,
                           descriptor,
                           accessflag,
                           no_isolated,
                           entry_points,
                           )

    write_methods = dict(gml=_write_gml,
                         gexf=nx.write_gexf,
                         gpickle=nx.write_gpickle,
                         graphml=nx.write_graphml,
                         yaml=nx.write_yaml,
                         net=nx.write_pajek,
                         )

    if show:
        plot(CG)
    else:
        writer = output.rsplit(".", 1)[1]
        if writer in ["bz2", "gz"]:
            writer = output.rsplit(".", 2)[1]
        if writer not in write_methods:
            print("Could not find a method to export files to {}!"
                  .format(writer))
            sys.exit(1)

        write_methods[writer](CG, output) 

def  outputNetwork(clargs, grph):
    outDict = collections.OrderedDict([
    ('1', "edge list and node attribute list"),
    ('2', "edge list"),
    ('3', "node attribute list"),
    ('4', "graphml (SLOW)"),
    ])
    try:
        import metaknowledge.contour
    except ImportError:
        import metaknowledge
    else:
        outDict['0'] = "view graph"
        outDict.move_to_end('0', last = False)
    print("The network contains {} nodes and {} edges.".format(len(grph.nodes()), len(grph.edges())))
    outID = int(inputMenu(outDict, header = "What type of output to you want? "))
    if outID == 0:
        metaknowledge.contour.quickVisual(grph)
        outputNetwork(clargs, grph)
    elif outID == 1:
        while True:
            try:
                outName = getOutputName(clargs, '', checking = False)
                metaknowledge.writeGraph(grph, outName, overwrite = False)
            except OSError:
                if clargs.name:
                    metaknowledge.writeGraph(grph, outName, overwrite = True)
                    break
                else:
                    overWrite = yesorNo("{}, overwrite (y/n)? ")
                    if overWrite:
                        metaknowledge.writeGraph(grph, outName, overwrite = True)
                        break
                    else:
                        pass
            else:
                break

    elif outID == 2:
        outName = getOutputName(clargs, '.csv')
        metaknowledge.writeEdgeList(grph, outName)
    elif outID == 3:
        outName = getOutputName(clargs, '.csv')
        metaknowledge.writeNodeAttributeFile(grph, outName)
    else:
        outName = getOutputName(clargs, '.graphml')
        nx.write_graphml(grph, outName)