我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用networkx.path_graph()。
def refine_to_chain(g, from_attr, to_attr): '''can be used to refine basic blocks into blocks - the dual of contract_chains() assume g.node[n][attr] is a list returns a graph whose nodes are the refinement of the lists into paths the elements of the lists are held as to_attr the nodes become tuples (node_index, list_index)''' paths = [] for n in g.nodes_iter(): block = g.node[n][from_attr] size = len(block) path = nx.path_graph(size, create_using=nx.DiGraph()) nx.relabel_nodes(path, mapping={x:(n, x) for x in path.nodes()}, copy=False) path.add_edges_from(((n, size - 1), (s, 0)) for s in g.successors_iter(n)) paths.append(path) values = {(n, x): block for n in g.nodes_iter() for x, block in enumerate(g.node[n][from_attr])} res = nx.compose_all(paths) nx.set_node_attributes(res, to_attr, values) return res
def test_sampler(): lsggg=lsgg_test.get_grammar() graph = lsgg_test.edenize(nx.path_graph(4)) graph.node[3]['label']='5' score_estimator= sde( lsgg_test.edenize(nx.path_graph(4)) ) sampler = sample.Sampler(grammar=lsggg,score_estimator=score_estimator, n_steps=2) for i in range(2): graph, score = sampler.transform(graph).next() assert (0.000001 > abs( 0.319274373045 - score))
def get_grammar(): lsggg=lsgg.lsgg() g=prep_cip_extract(nx.path_graph(4)) lsggg.fit([g,g,g]) return lsggg
def test_extract_core_and_interface(): graph=nx.path_graph(4) prep_cip_extract(graph) res = lcu.extract_core_and_interface(root_node=3, graph=graph, radius=1,thickness=1) #gprint(res.graph) assert ( str(res) == "cip: int:16931, cor:695036, rad:1, thi:1, rot:3")
def test_neighbors(): # make a grammar lsggg = get_grammar() #make agraph g=nx.path_graph(4) g=edenize(g) g.node[3]['label']='5' stuff=list(lsggg.neighbors(g)) assert(6 == len(stuff))
def get_grammar(): lsggg=lsgg() g=prep_cip_extract(nx.path_graph(4)) lsggg.fit([g,g,g]) return lsggg
def communitySplits(self, graph, weight=None): """ Compute the splits for the formation of communities. Parameters ---------- graph - A networkx graph of digraph. weight (string) - If None, all edge weights are considered equal. Otherwise holds the name of the edge attribute used as weight Returns ------- The graph with weak edges removed. Usage ----- >>> G = nx.path_graph(10) >>> out = GirvanNewman(G) >>> comm = out.communities(G, weight=None) >>> for x in comm: print x """ nConnComp = nx.number_connected_components(graph) nComm = nConnComp while (nComm <= nConnComp): betweenness = nx.edge_betweenness_centrality(graph, weight=weight) if (len(betweenness.values()) != 0 ): max_betweenness = max(betweenness.values()) else: break for u,v in betweenness.iteritems(): if float(v) == max_betweenness: # print u,v graph.remove_edge(u[0], u[1]) nComm = nx.number_connected_components(graph) return graph
def test_simulation(): graph = nx.path_graph(5) sm = Simulation(embedding_graph=graph, process_type=Process, channel_type=Channel) sm.run()
def circular_layout(G, scale=1, center=None, dim=2, direction='CCW'): # dim=2 only """Position nodes on a circle. Parameters ---------- G : NetworkX graph or list of nodes scale : float Scale factor for positions center : array-like or None Coordinate pair around which to center the layout. dim : int Dimension of layout, currently only dim=2 is supported Returns ------- pos : dict A dictionary of positions keyed by node Examples -------- >>> G = nx.path_graph(4) >>> pos = nx.circular_layout(G) Notes ----- This algorithm currently only works in two dimensions and does not try to minimize edge crossings. """ G, center = _process_params(G, center, dim) if len(G) == 0: pos = {} elif len(G) == 1: pos = {nx.utils.arbitrary_element(G): center} else: # Discard the extra angle since it matches 0 radians. theta = np.linspace(0, 1, len(G) + 1)[:-1] * 2 * np.pi theta = theta.astype(np.float32) if direction == 'CCW': pos = np.column_stack([np.cos(theta), np.sin(theta)]) else: pos = np.column_stack([np.sin(theta), np.cos(theta)]) pos = rescale_layout(pos, scale=scale) + center pos = dict(zip(G, pos)) return pos
def read_gml(path, relabel=False): """Read graph in GML format from path. Parameters ---------- path : filename or filehandle The filename or filehandle to read from. relabel : bool, optional If True use the GML node label attribute for node names otherwise use the node id. Returns ------- G : MultiGraph or MultiDiGraph Raises ------ ImportError If the pyparsing module is not available. See Also -------- write_gml, parse_gml Notes ----- Requires pyparsing: http://pyparsing.wikispaces.com/ The GML specification says that files should be ASCII encoded, with any extended ASCII characters (iso8859-1) appearing as HTML character entities. References ---------- GML specification: http://www.infosun.fim.uni-passau.de/Graphlet/GML/gml-tr.html Examples -------- >>> G=nx.path_graph(4) >>> nx.write_gml(G,'test.gml') >>> H=nx.read_gml('test.gml') """ lines = (unescape(line.decode('ascii')) for line in path) G = parse_gml(lines, relabel=relabel) return G
def write_gml(G, path): """ Write the graph G in GML format to the file or file handle path. Parameters ---------- path : filename or filehandle The filename or filehandle to write. Filenames ending in .gz or .gz2 will be compressed. See Also -------- read_gml, parse_gml Notes ----- GML specifications indicate that the file should only use 7bit ASCII text encoding.iso8859-1 (latin-1). This implementation does not support all Python data types as GML data. Nodes, node attributes, edge attributes, and graph attributes must be either dictionaries or single stings or numbers. If they are not an attempt is made to represent them as strings. For example, a list as edge data G[1][2]['somedata']=[1,2,3], will be represented in the GML file as:: edge [ source 1 target 2 somedata "[1, 2, 3]" ] Examples --------- >>> G=nx.path_graph(4) >>> nx.write_gml(G,"test.gml") Filenames ending in .gz or .bz2 will be compressed. >>> nx.write_gml(G,"test.gml.gz") """ for line in generate_gml(G): line += '\n' path.write(line.encode('ascii', 'xmlcharrefreplace')) # fixture for nose tests
