我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用networkx.convert_node_labels_to_integers()。
def depart(self, G, rng): n1 = G.order() n2 = self.env.embedding.depart_number(G, rng) if G.order() <= n2: old = G.nodes() else: old = rng.choice(G.nodes(), n2, replace = False) old = old.tolist() for v in old: self.stats["%s_patient_departed" % G.node[v]["bp"]] += 1 self.stats["%s_donor_departed" % G.node[v]["bd"]] += 1 G.remove_nodes_from(old) self.stats["departed"] += n2 return nx.convert_node_labels_to_integers(G)
def depart(self, G, rng): n1 = G.order() n2 = rng.binomial(n1, 1.0 / self.k) if G.order() <= n2: old = G.nodes() else: old = rng.choice(G.nodes(), n2, replace = False) old = old.tolist() for v in old: self.stats["%s_patient_departed" % G.node[v]["bp"]] += 1 self.stats["%s_donor_departed" % G.node[v]["bd"]] += 1 G.remove_nodes_from(old) self.stats["departed"] += n2 return nx.convert_node_labels_to_integers(G)
def get_couling_derivate_matrix(self, h, twist_number, s): if type(twist_number) == InPhase or type(twist_number) == Twist2D: if type(twist_number) == InPhase: mx = 0 my = 0 else: mx = twist_number.get_mx() my = twist_number.get_my() dhdx = h.get_derivative() dphi_x = (2 * np.pi * mx) / self.nx dphi_y = (2 * np.pi * my) / self.nx g = networkx.grid_2d_graph(self.ny, self.nx, periodic=True) g = networkx.convert_node_labels_to_integers(g, ordering='sorted') c = _networkx2mat(g, self.n) a = _build_2d_dhdx_matrix(dhdx, self.nx, self.ny, self.n, dphi_x, dphi_y, s) d = c * a return d else: raise Exception('Topology not compatible with state')
def depart(self, G, rng): n1 = G.order() n2 = rng.binomial(n1, 1.0 / self.k) old = rng.choice(G.nodes(), n2, replace = False).tolist() G.remove_nodes_from(old) self.stats["departed"] += n2 return nx.convert_node_labels_to_integers(G)
def __load_edgelist(self): """ Load the graph """ self.G = nx.read_edgelist(self.params["edgelist_path"],create_using=nx.DiGraph()) # self.G = nx.convert_node_labels_to_integers(self.G, first_label=0,ordering="sorted") return
def __getitem__(self, index): #TODO: Manually have to check the convert_node_labels_to_integers function g = nx.convert_node_labels_to_integers(nx.read_graphml(os.path.join(self.root, self.ids[index]))) target = self.classes[index] h = self.vertex_transform(g) g, e = self.edge_transform(g) target = self.target_transform(target) return (g, h, e), target
def get_couling_derivate_matrix(self, h, twist_number, s): if type(twist_number) == InPhase: dhdx = h.get_derivative() dphi_x = 0.0 dphi_y = 0.0 g = networkx.grid_2d_graph(self.ny, self.nx, periodic=False) g = networkx.convert_node_labels_to_integers(g, ordering='sorted') c = _networkx2mat(g, self.n) a = _build_2d_dhdx_matrix(dhdx, self.nx, self.ny, self.n, dphi_x, dphi_y, s) d = c * a return d else: raise Exception('Topology not compatible with state')
def core_substitution(graph, orig_cip_graph, new_cip_graph): """ graph is the whole graph.. subgraph is the interfaceregrion in that we will transplant new_cip_graph which is the interface and the new core """ graph=_edge_to_vertex(graph) assert( set(orig_cip_graph.nodes()) - set(graph.nodes()) == set([]) ), 'orig_cip_graph not in graph' # select only the interfaces of the cips new_graph_interface_nodes = [n for n, d in new_cip_graph.nodes(data=True) if 'core' not in d] new_cip_interface_graph = nx.subgraph(new_cip_graph, new_graph_interface_nodes) original_graph_interface_nodes = [n for n, d in orig_cip_graph.nodes(data=True) if 'core' not in d] original_interface_graph = nx.subgraph(orig_cip_graph, original_graph_interface_nodes) # get isomorphism between interfaces, if none is found we return an empty graph iso = get_good_isomorphism(graph, orig_cip_graph, new_cip_graph, original_interface_graph, new_cip_interface_graph) if len(iso) != len(original_interface_graph): # print iso # draw.display(orig_cip_graph) # draw.display(new_cip_graph) #draw.graphlearn([orig_cip_graph, new_cip_graph],size=10) logger.log(5,"grammar hash collision, discovered in 'core_substution' ") return None # ok we got an isomorphism so lets do the merging graph = nx.union(graph, new_cip_graph, rename=('', '-')) # removing old core # original_graph_core_nodes = [n for n, d in orig_cip_graph.nodes(data=True) if 'core' in d] original_graph_core_nodes = [n for n, d in orig_cip_graph.nodes(data=True) if 'core' in d] for n in original_graph_core_nodes: graph.remove_node(str(n)) # merge interfaces for k, v in iso.iteritems(): #graph.node[str(k)][ # 'intgggerface'] = True # i am marking the interface only for the backflow probability calculation in graphlearn, this is probably deleteable because we also do this in merge, also this line is superlong Ooo merge(graph, str(k), '-' + str(v)) # unionizing killed my labels so we need to relabel graph=eg._revert_edge_to_vertex_transform(graph) re = nx.convert_node_labels_to_integers(graph) graph_clean(re) return re
def plot_nx_graph_3d(graph, radii = None, colormap='jet', line_width = 2, opacity=.9): """Plot a 3d graph of the skeleton Arguments: radii: radii of the edges used in color code, if None uniform color Returns: mayavi scence """ # get graph positions g2 = nx.convert_node_labels_to_integers(graph, label_attribute = 'xyz'); xyz = np.array([x['xyz'] for x in g2.node.values()], dtype = 'int32'); # scalar colors if radii is not None: scalars = np.array([radii[tuple(x)] for x in xyz], dtype = 'float32'); else: #scalars = np.arange(5, xyz.shape[0]+5); scalars = np.ones(xyz.shape[0], dtype = 'float32'); #pts = mlab.points3d(xyz[:,0], xyz[:,1], xyz[:,2], # scalars, # scale_factor=node_size, # scale_mode='none', # colormap=graph_colormap, # resolution=20) pts = mlab.pipeline.scalar_scatter(xyz[:,0], xyz[:,1], xyz[:,2], scalars) pts.mlab_source.dataset.lines = np.array(g2.edges(), dtype = 'int32') pts.update() #tube = mlab.pipeline.tube(pts, tube_radius=edge_size) #lab.pipeline.surface(tube, color=edge_color) lines = mlab.pipeline.stripper(pts); mlab.pipeline.surface(lines, colormap = colormap, line_width = line_width, opacity = opacity) if radii is not None: mlab.colorbar(orientation = 'vertical', title='Radius [pix]'); mlab.axes() return lines
def reduce_graph_rings(self): ''' :return: ''' cycle_name_format = "R_{:}" index = 0 cycle = self.get_cycle() while cycle: cycle_name = cycle_name_format.format(index) self.graph.add_node(cycle_name) # ebunch = zip(cycle, (cycle[1:] + cycle[:1])) self.graph.remove_edges_from(cycle) for node1, node2 in cycle: if isinstance(node1, six.string_types): self.graph.add_edge(node1, cycle_name, attr_dict={"bond_features": Molecule.bond_features_between_contract_rings()}) continue neighbours = self.graph.neighbors(node1) if not neighbours: continue for neighbour in neighbours: edge_attrs = self.get_bond_features(neighbour, node1) self.graph.add_edge(neighbour, cycle_name, attr_dict={ "bond_features": edge_attrs}) self.graph.remove_edge(node1, neighbour) nx.set_node_attributes(self.graph, "atom_features", values={cycle_name: Molecule.atom_features_of_contract_rings(0)}) for node1, node2 in cycle: if not isinstance(node1, six.string_types): self.graph.remove_node(node1) index += 1 cycle = self.get_cycle() self.graph = nx.convert_node_labels_to_integers(self.graph, first_label=0) nx.draw(self.graph) self.no_of_atoms = len(self.graph)
def docs_to_networkx(dataset, cats, window_size=2, vocabulary_creation=True): ds = './datasets/%s/' % dataset Gs = [] labels = [] type_ = 2 vocab_creation = vocabulary_creation words = [] # for vocabulary for doc in os.listdir(ds): if 'train.txt' in doc: type_ = 1 if type_ == 1: if os.path.exists("ds/vocab.txt"): vocab_creation = False with open(ds + '/train.txt', 'r', encoding='iso-8859-1') as doc: dc = 1 for line in doc: label = line[0] labels.append(label) terms = extract_terms_from_sentence(line[1:], stopwords=stopwords.words('english'), lemmatize=True, stem=True, only_N_J=True) if vocab_creation: words.extend(terms) graph = terms_to_graph(terms, window_size) G = graph_to_networkx(graph, name=label + '_' + str(dc)) # G = nx.convert_node_labels_to_integers(G, first_label=1, label_attribute='label') nx.set_node_attributes(G, 'label', dict(zip(G.nodes(), G.nodes()))) Gs.append(G) dc += 1 else: if os.path.exists("ds/vocab.txt"): vocab_creation = False for cat in cats.keys(): for doc in os.listdir(ds + cat): terms = extract_terms_from_file(ds + cat + '/' + doc, stopwords=stopwords.words('english'), lemmatize=True, stem=True, only_N_J=True) if vocab_creation: words.extend(terms) graph = terms_to_graph(terms, window_size) G = graph_to_networkx(graph, name=cat + doc.split('.')[0]) # G = nx.convert_node_labels_to_integers(G, first_label=1, label_attribute='label') nx.set_node_attributes(G, name='label', values=dict(zip(G.nodes(), G.nodes()))) Gs.append(G) labels.append(cats[cat]) if vocab_creation: vocab = dict(Counter(words)) create_vocabulary_file(fname, vocab) return Gs, labels # needs fix or discard
