我们从Python开源项目中，提取了以下19个代码示例，用于说明如何使用networkx.draw_networkx()。
def network(): global network_graph try: graph_json = request.json except IndexError: pass # some network nodes could be removed from the graph to avoid confusing the user # the graph contains network_json = loads(graph_json) G = json_graph.node_link_graph(network_json) fig = plt.figure() plt.axis('off') networkx.draw_networkx(G, node_size=80, node_color='c', font_size=8) network_graph = BytesIO() fig.savefig(network_graph, format='png') # redirect any attempts to non-existing pages to the main page
def graph_visualize(G, args): import networkx as nx import numpy as np # ??????????????????????????? pos = nx.spring_layout(G) # ?????? ???????????????????? plt.figure() nx.draw_networkx(G, pos, with_labels=False, alpha=0.4,font_size=0.0,node_size=10) plt.savefig(args.directory+"/graph/graph.png") nx.write_gml(G, args.directory+"/graph/graph.gml") # ?????? plt.figure() degree_sequence=sorted(nx.degree(G).values(),reverse=True) dmax=max(degree_sequence) dmin =min(degree_sequence) kukan=range(0,dmax+2) hist, kukan=np.histogram(degree_sequence,kukan) plt.plot(hist,"o-") plt.xlabel('degree') plt.ylabel('frequency') plt.grid() plt.savefig(args.directory+'/graph/degree_hist.png')
def plot_embedding2D(node_pos, node_colors=None, di_graph=None): node_num, embedding_dimension = node_pos.shape if(embedding_dimension > 2): print("Embedding dimension greater than 2, use tSNE to reduce it to 2") model = TSNE(n_components=2) node_pos = model.fit_transform(node_pos) if di_graph is None: # plot using plt scatter plt.scatter(node_pos[:, 0], node_pos[:, 1], c=node_colors) else: # plot using networkx with edge structure pos = {} for i in range(node_num): pos[i] = node_pos[i, :] if node_colors: nx.draw_networkx_nodes(di_graph, pos, node_color=node_colors, width=0.1, node_size=100, arrows=False, alpha=0.8, font_size=5) else: nx.draw_networkx(di_graph, pos, node_color=node_colors, width=0.1, node_size=300, arrows=False, alpha=0.8, font_size=12)
def draw_overlaid_graphs(original, new_graphs, print_text=False): """ Draws the new_graphs as graphs overlaid on the original topology :type new_graphs: list[nx.Graph] """ import matplotlib.pyplot as plt layout = nx.spring_layout(original) nx.draw_networkx(original, pos=layout) # want to overlay edges in different colors and progressively thinner # so that we can see what edges are in a tree line_colors = 'rbgycm' line_width = 2.0 ** (min(len(new_graphs), len(line_colors)) - 1) # use this for visualising on screen # line_width = 3.0 ** (min(len(new_graphs), len(line_colors)) - 1) # use this for generating diagrams for i, g in enumerate(new_graphs): if print_text: log.info(nx.info(g)) log.info("edges:", list(g.edges())) nx.draw_networkx(g, pos=layout, edge_color=line_colors[i % len(line_colors)], width=line_width) # advance to next line width line_width /= 1.7 # use this for visualising on screen # line_width /= 2.0 # use this for generating diagrams plt.show()
def draw(self, layout='circular', figsize=None): """Draw all graphs that describe the DGM in a common figure Parameters ---------- layout : str possible are 'circular', 'shell', 'spring' figsize : tuple(int) tuple of two integers denoting the mpl figsize Returns ------- fig : figure """ layouts = { 'circular': nx.circular_layout, 'shell': nx.shell_layout, 'spring': nx.spring_layout } figsize = (10, 10) if figsize is None else figsize fig = plt.figure(figsize=figsize) rocls = np.ceil(np.sqrt(len(self.graphs))) for i, graph in enumerate(self.graphs): ax = fig.add_subplot(rocls, rocls, i+1) ax.set_title('Graph ' + str(i+1)) ax.axis('off') ax.set_frame_on(False) g = graph.nxGraph weights = [abs(g.edge[i][j]['weight']) * 5 for i, j in g.edges()] nx.draw_networkx(g, pos=layouts[layout](g), ax=ax, edge_cmap=plt.get_cmap('Reds'), width=2, edge_color=weights) return fig
def draw(self, layout='circular', figsize=None): """Draw graph in a matplotlib environment Parameters ---------- layout : str possible are 'circular', 'shell', 'spring' figsize : tuple(int) tuple of two integers denoting the mpl figsize Returns ------- fig : figure """ layouts = { 'circular': nx.circular_layout, 'shell': nx.shell_layout, 'spring': nx.spring_layout } figsize = (10, 10) if figsize is None else figsize fig = plt.figure(figsize=figsize) ax = fig.add_subplot(1, 1, 1) ax.axis('off') ax.set_frame_on(False) g = self.nxGraph weights = [abs(g.edge[i][j]['weight']) * 5 for i, j in g.edges()] nx.draw_networkx(g, pos=layouts[layout](g), ax=ax, edge_cmap=plt.get_cmap('Reds'), width=2, edge_color=weights) return fig
def graphNetworkx(self, buds_visible = False, filter_assym_edges = False, labels_visible = True, iterations = 1000): self.buds_visible = buds_visible self.filter_assym_edges = filter_assym_edges G = self.makeGraphData() if hasattr(self, 'nodeColorInfo'): nodeColors = self.useColorData(G, 'networkx') #print G.node if labels_visible: nx.draw_networkx(G, pos=nx.spring_layout(G, iterations = iterations), node_color = nodeColors, linewidths = 1) else: nx.draw(G, pos=nx.spring_layout(G, iterations = iterations), node_color = nodeColors, linewidths = 1) plt.show()
def plot_nx(bn,**kwargs): """ Draw BayesNet object from networkx engine """ g = nx.DiGraph(bn.E) pos = graphviz_layout(g,'dot') #node_size=600,node_color='w',with_labels=False nx.draw_networkx(g,pos=pos, **kwargs) plt.axis('off') plt.show()
def _build_graph(show=False): """Load word dependencies into graph using networkx. Enables easy traversal of dependencies for parsing particular patterns. One graph is created for each sentence. Args: show (bool): If set to True, labeled visualization of network will be opened via matplotlib for each sentence Returns: None: Global variable G is set from within function """ global G G = nx.Graph() node_labels, edge_labels = {}, {} for idx, dep in enumerate(A.deps): types = ["dependent", "governor"] # nodes, labels for x in types: G.add_node(str(dep[x]), word=dep[x + "Gloss"], pos=A.lookup[dep[x]]["pos"]) node_labels[str(dep[x])] = dep[x + "Gloss"] + " : " + A.lookup[dep[x]]["pos"] # edges, labels G.add_edge(str(dep[types[0]]), str(dep[types[1]]), dep=dep["dep"]) edge_labels[(str(dep[types[0]]), str(dep[types[1]]))] = dep["dep"] if show == True: pos = nx.spring_layout(G) nx.draw_networkx(G, pos=pos, labels=node_labels, node_color="white", alpha=.5) nx.draw_networkx_edge_labels(G, pos=pos, edge_labels=edge_labels) plt.show() ######################################### # Dependency / POS parsing functions #########################################
def draw_network(DG): nx.draw_networkx(DG, pos=nx.spring_layout(DG), node_color = 'red') plt.axis('off') # turn off axis plt.savefig('c-elegans.svg') # plt.show()
def centrality(DG): in_degree_centrality = nx.in_degree_centrality(DG) out_degree_centrality = nx.out_degree_centrality(DG) with open('/home/sun/PycharmProjects/Network/in_degree_centrality.csv', 'w') as f: for k, v in in_degree_centrality.items(): f.write(str(k) + ': ' + str(v) + '\n') f.close() with open('/home/sun/PycharmProjects/Network/out_degree_centrality.csv', 'w') as f: for k, v in out_degree_centrality.items(): f.write(str(k) + ': ' + str(v) + '\n') f.close() # def main(): # data = '/home/sun/PycharmProjects/Network/C-elegans-frontal.txt' # # data = 'www.adj' # DG = create_network(data) # # # draw_network(DG) # # clustering_coefficient(DG) # # centrality(DG) # degree_distribution(DG) # # if __name__ == '__main__': # main() # # # DG = nx.DiGraph() # # DG.add_edge(1,2) # # print(DG.edges()) # # # pos = nx.nx_agraph.graphviz_layout(DG) # # nx.draw_networkx(DG, pos = nx.spring_layout(DG)) # # plt.show() # # plt.ishold() # # plt.draw(DG)
def prune_homology_graph(df, chim_dir): to_remove = [] df['brk_left_cut'] = df['name'].str.split(":").str[0:3].str.join(sep=":") df['brk_right_cut'] = df['name'].str.split(":").str[3:6].str.join(sep=":") left_nodes = set(df[df['brk_left_cut'].duplicated()]['brk_left_cut']) right_nodes = df[df['brk_right_cut'].duplicated()]['brk_right_cut'] all_nodes = list(zip(left_nodes, itertools.repeat("left"))) + list(zip(right_nodes, itertools.repeat("right"))) for node, hom_side in all_nodes: node_members = df[((df['brk_' + hom_side + '_cut'] == node))]['name'] node_graph = nx.Graph() node_graph.add_nodes_from(node_members, exprs=10) for jxn1, jxn2 in itertools.combinations(node_members, 2): pair_score = get_pairwise_hom(jxn1, jxn2, chim_dir, hom_side) if pair_score != 0: node_graph.add_edge(jxn1, jxn2, weight=pair_score) # nx.draw_networkx(node_graph, pos=nx.shell_layout(node_graph), node_size=100) # plt.show() adj_mat = nx.to_pandas_dataframe(node_graph) node_compare = adj_mat[adj_mat.sum()> 0].index.tolist() if len(node_compare) > 0: node_homdf = df[df['name'].isin(node_compare)][['name', 'TPM_Fusion', 'TPM_Left', 'TPM_Right']].set_index('name') node_homdf['max_pairs'] = node_homdf[['TPM_Left','TPM_Right']].max(axis=1) node_homdf = node_homdf.sort_values(['TPM_Fusion', 'max_pairs'] , ascending=False) node_remove = node_homdf.iloc[1:].index.tolist() to_remove.extend(node_remove) # use list of to_remove to mark homologous fusions return to_remove
def plotgraph(g): """Plot a grid graph, using (i,j) node labels as positions""" plt.figure(figsize=fig_size) positions = dict(zip(g.nodes(),g.nodes())) nx.draw_networkx(g,positions,with_labels=False,node_size=50) # plotting of measurement results:
def graph_plot(G, graph_name, nodes_color_fn=_pos_coloring, node_position_path="./data", node_position_file=True, show=True): if node_position_file: spring_pos = pickle.load(open(path_join(node_position_path, graph_name, 'node_pos.bin'), "rb")) else: spring_pos = nx.spring_layout(G) pickle.dump(spring_pos, open(path_join(node_position_path, graph_name, 'node_pos.bin'), "wb")) spring_pos_values = np.array(list(spring_pos.values())) norm_pos = np.linalg.norm(spring_pos_values, axis=1) nodes_color = nodes_color_fn(G, norm_pos) plt.figure(figsize=(5, 5)) plt.axis("off") nx.draw_networkx(G, node_color=nodes_color, pos=spring_pos, camp=plt.get_cmap(CAMP), nodelist=sorted(G.nodes())) if show: plt.show() else: plt.clf() plt.close() return nodes_color
def drawDAG(self, chr): """ draw a dag :param chr: chromosome name (str) :return: on disk """ labels = {} for node in self.orders[chr]: #labels[node] = node[3] if node.data[1] != "REF": if node.data[1] == "DEL": labels[node] = "-" elif node.data[1] == "DUP" or node.data[1] == "DUP_COPY": labels[node] = "+" elif node.data[1] == "INV": labels[node] = "~" else: labels[node] = ">" else: labels[node] = " " sourceNode = self.orders[chr][0] sinkNode = self.orders[chr][-1] labels[sourceNode] = "Source" labels[sinkNode] = "Sink" # extract dag for each chr dag = self.DAGs[chr] pos = self.hierarchy_pos(dag, sourceNode) plt.figure() nx.draw_networkx(dag, pos=pos, node_size=30, labels=labels, font_size = 4)#, with_labels=False, arrows=False) plt.title(chr) plt.axis('off') plt.gcf() plt.savefig(self.prefix + "/DAGs/dag." + chr + ".pdf") plt.clf() plt.close()
def plot(self, file_name=None): """ Plot the DAG of the pipeline """ import matplotlib.pyplot as plt nx.draw_networkx(self.dag) if not file_name: file_name = os.path.join(self.output_dir, self.name + '.png') plt.savefig(file_name)
def draw_axes(self, ax=None, id_highlight=None): # plt.ion() # interactive mode on (doesn't work with pycharm) g = self.__nxgraph # new call format for networkx 1.11 (uses pygraphviz, which does not support Python 3!) # pos = nx.drawing.nx_agraph.graphviz_layout(g, prog='dot') # old call format for networkx 1.10 # pos = nx.graphviz_layout(g, prog='dot') # new call format for networkx 1.11 (uses pydot) pos = nx.drawing.nx_pydot.graphviz_layout(g, prog='dot') if ax is None: ax = self.get_axes(self.id_axes) assert len(ax) == self.required_axes nx.draw(g, pos, ax=ax[0], hold=True, with_labels=True, arrows=True) # nx.draw_networkx(self.__graph) # plot the roots if id_highlight is None: id_highlight = self.__id_roots for r in id_highlight: ax[0].scatter(pos[r][0], pos[r][1], s=500) for id_node, node in self.nodes_iter(data=True): c = "{}".format(node.layer) ax[0].text(pos[id_node][0], pos[id_node][1], "\n" + c, va='top', ha='center', color='blue') # plt.show() # this call blocks further execution until window is closed
def draw(g: nx.DiGraph): import matplotlib.pyplot as plt nx.draw_networkx(g, with_labels=True) plt.show()
def network(self, type, start, end, factor=1.0, color=None, src=None): """create network of crimes Args: type: data type start: start character end: end character factor: size, default 1 color: colour, default None src: default None """ self.load() df = pd.read_sql_query(con=self.con, sql=GEN_SEARCH_QUERY.format("Data", "Type = '{0}' " "AND Latitude is NOT NULL " "AND Longitude is NOT NULL " "AND date(Date) > date('{1}') " "AND date(Date) <= date('{2}')".format(type, start, end))) dg = nx.Graph() queue = deque() crimes = [Vertex(row["Case"], row["Type"], row["Longitude"], row["Latitude"]) for index, row in df.iterrows()] if len(crimes) == 0: print("none found") return copy = [vertex for vertex in crimes] if src is None: queue.append(crimes[0]) else: queue.append(list(filter(lambda x: x.id == src, crimes))) visited = [] for crime in crimes: dg.add_node(crime.id) while len(queue) > 0: current = queue.popleft() crimes.remove(current) for crime in crimes: if crime.distance_to(current) < factor and crime not in visited: dg.add_edge(current.id, crime.id, weight=crime.distance_to(current)) queue.append(crime) visited.append(crime) visited.append(current) if len(queue) == 0 and len(crimes) > 0: queue.append(crimes[0]) pos = {copy[i].id: self.base_map(copy[i].lon, copy[i].lat) for i in range(len(copy))} if color is None: color = "red" nx.draw_networkx(dg, pos=pos, node_size=5, with_labels=False, ax=self.ax, node_color=color) plt.show()
