我有这样的图结构
graph = thisdict['data']['graph']dict_edges = graph['edges']edges = []for edge in dict_edges: edges.append((edge['source']['node_id'], edge['target']['node_id']))print('源节点:\t\t\t 目标节点:\n') for edge in edges: print(str(edge))print('\n')dict_nodes = graph['nodes']nodes = {}for node in dict_nodes: nodes[node['id']] = node['name']print('节点ID:\t\t 节点名称:\n')for key, value in nodes.items(): print("'%s':'%s'" %(key, value))输出结果:
源节点: 目标节点:('61697b94f74c92a808641ba3', '61697b95f74c92a808641ba4')('61697b94f74c92a808641ba3', '61697b96f74c92a808641ba5')('61697b95f74c92a808641ba4', '61697b96f74c92a808641ba6')('61697b96f74c92a808641ba6', '61697b97f74c92a808641ba7')('61697b96f74c92a808641ba5', '61697b97f74c92a808641ba7')('61697b97f74c92a808641ba7', '61697b98f74c92a808641ba8')('61697b98f74c92a808641ba8', '61697b98f74c92a808641ba9')节点ID: 节点名称:'61697b94f74c92a808641ba3':'S3连接器''61697b95f74c92a808641ba4':'加载器1''61697b96f74c92a808641ba5':'加载器2' '61697b96f74c92a808641ba6':'采样器1''61697b97f74c92a808641ba7':'连接器''61697b98f74c92a808641ba8':'采样器2''61697b98f74c92a808641ba9':'分离器'我编写了以下代码来绘制图:
nx_graph = nx.Graph()plt.figure(figsize=(3,3))for key, value in nodes.items(): nx_graph.add_node(key, layer = nodes.values()) #我需要将每个节点名称放入一个单独的层，因此我应该有6个层for edge in edges: nx_graph.add_edge(*edge)pos = nx.multipartite_layout(nx_graph, subset_key="layer")nx.draw(nx_graph, pos, labels=nodes, with_labels=True)plt.show()它显示了一个错误，提示： TypeError: 不支持的操作数类型：’dict_values’ 和 ‘float’ 进行减法运算
我需要将每个节点名称放入一个单独的层，因此我应该有6个层。层的排序应如下所示:
S3连接器 --> 加载器1 & 加载器2 加载器1将提供给采样器1 加载器2和采样器1将在连接器处会合 连接器将提供给采样器2 采样器2将提供给分离器回答：
错误发生的原因是在for循环中添加节点时，您传递了layer = nodes.values()，而不是layer=value。然而，纠正这一点仍然不会给您想要的布局，因为您实际上需要以某种方式指定层。根据您对6个层的描述，我通过一个单独的字典将它们添加为属性。
我对您实际想要做的事情做了一些假设，通过将图改为DiGraph并决定sampler 1节点的位置。
这是一个独立的代码块及其输出:
import networkx as nximport matplotlib.pyplot as pltnodes = {'61697b94f74c92a808641ba3':'S3连接器','61697b95f74c92a808641ba4':'加载器1','61697b96f74c92a808641ba5':'加载器2','61697b96f74c92a808641ba6':'采样器1','61697b97f74c92a808641ba7':'连接器','61697b98f74c92a808641ba8':'采样器2','61697b98f74c92a808641ba9':'分离器'}edges = [('61697b94f74c92a808641ba3', '61697b95f74c92a808641ba4'),('61697b94f74c92a808641ba3', '61697b96f74c92a808641ba5'),('61697b95f74c92a808641ba4', '61697b96f74c92a808641ba6'),('61697b96f74c92a808641ba6', '61697b97f74c92a808641ba7'),('61697b96f74c92a808641ba5', '61697b97f74c92a808641ba7'),('61697b97f74c92a808641ba7', '61697b98f74c92a808641ba8'),('61697b98f74c92a808641ba8', '61697b98f74c92a808641ba9')]layers = {'61697b94f74c92a808641ba3': 1,'61697b95f74c92a808641ba4': 2,'61697b96f74c92a808641ba5':2,'61697b96f74c92a808641ba6':3,'61697b97f74c92a808641ba7':4,'61697b98f74c92a808641ba8':5,'61697b98f74c92a808641ba9':6}nx_graph = nx.DiGraph() # 改为DiGraph（在视觉上添加箭头）plt.figure(figsize=(8,8)) # 扩大图形for key, value in nodes.items(): nx_graph.add_node(key, name=value, layer=layers[key])for edge in edges: nx_graph.add_edge(*edge)pos = nx.multipartite_layout(nx_graph, subset_key="layer")nx.draw(nx_graph, pos=pos, labels=nodes, with_labels=True)plt.show()
