# 1.获取边属性 edge_weights = nx.get_edge_attributes(G, 'weight') edge_weights {(1, 2): 1, (2, 3): 2, (4, 3): 1.5, (4, 5): 2.5} # 2.自定义边的可视化样式,下面是基于weight来设置边的宽度 edge_widths = [edge_weights.get((u, v), 1) for u, v in G.edges()] ...
for i, edge in enumerate(G.edges()): G.edges[i]['weight']=weights[edge] 但我收到此错误: --- TypeError Traceback (most recent call last) <ipython-input-48-6119dc6b7af0> in <module>() 10 11 for i, edge in enumerate(G.edges()): ---> 12 G.edges[i]['weight']=weights[...
nodes(data=True) ] # Get edge weights from graph edge_weights = [ e[2]["weight"] for e in network.edges(data=True) ] # Build up graph figure #pos = nx.random_layout(network) pos = nx.spring_layout(network) nx.draw_networkx_edges(network, pos, alpha=0.3 , width=edge_weights, ...
# 添加边,并设置边的 weight 属性 G.add_edge(1, 2, weight=2) # 获取边的权重属性 edge_weights = nx.get_edge_attributes(G, 'weight') # 绘制图,可以设置边的宽度来反映权重 nx.draw(G, width=[edge_weights[(u, v)] for u, v in G.edges()]) plt.show() 应用场景 节点大小和边权重的...
# The Graph diagram does not show the edge weights. # However, we can get the weights by printing all the # edges along with the weights by the command below print(list(G.edges(data=True))) 输出: [(1,2,{'weight':19}),
Edges must have a 'weight' property, represented as edge width """importnetworkxasnximportmatplotlib.pyplotasplt# Create a color list corresponding to nodes.node_colors = [ n[1]["color"]forninnetwork.nodes(data=True) ]# Get edge weights from graphedge_weights = [ e[2]["weight"]forein...
edge_weights=nx.get_edge_attributes(G,'length')# 可视化图 pos=nx.spring_layout(G)# 选择布局算法,这里使用弹簧布局 nx.draw(G,pos,with_labels=True,font_weight='bold',node_size=700,node_color='skyblue',font_color='black')# 绘制权重标签 ...
edge_weights = [G.edges[v, w]["weight"] for v, w in G.edges] 将边权值传递给spring_layout(),将强连接的节点推的更近。 # 将边权值传递给spring_layout(),将强连接节点推得更近 weighted_pos = nx.spring_layout(G, pos=karate_pos, k=0.3, weight="weight") ...
G.add_edge('A','B') G.add_edge('B','C') # 4. 进行图的绘制 pos = nx.spring_layout(G)# 选择布局算法; nx.draw(G, pos, with_labels=True) plt.show() 绘制分支节点的有向图 # -*- coding: utf-8 -*- importnetworkxasnx ...
"""weights = np.real([*nx.get_edge_attributes(G,'weight').values()]) pos = nx.shell_layout(G) nx.draw(G, pos, node_color='#A0CBE2', with_labels=True, edge_color=weights, width=4, edge_cmap=plt.cm.Blues, ax=ax) plt.show()### HAMILTONIANS AND DATA###...