python dijkstra算法可视化代码

由于您没有指定具体的数据结构和图形库，以下是一个使用Python的NetworkX图形库和Matplotlib可视化库实现Dijkstra算法的示例代码：

import networkx as nx
import matplotlib.pyplot as plt

def dijkstra(graph, start):
    # 初始化距离和前驱节点
    distance = {}
    predecessor = {}
    for node in graph:
        distance[node] = float('inf')
        predecessor[node] = None
    distance[start] = 0

    # 用集合表示未访问的节点
    unvisited = set(graph)

    while unvisited:
        # 找到未访问节点中距离最小的节点
        current = None
        for node in unvisited:
            if current is None or distance[node] < distance[current]:
                current = node

        # 访问该节点
        unvisited.remove(current)

        # 更新相邻节点的距离和前驱节点
        for neighbor, weight in graph[current].items():
            new_distance = distance[current] + weight
            if new_distance < distance[neighbor]:
                distance[neighbor] = new_distance
                predecessor[neighbor] = current

    return distance, predecessor

# 创建有向图
G = nx.DiGraph()
G.add_edges_from([('A', 'B', {'weight': 5}), ('A', 'D', {'weight': 9}), ('A', 'E', {'weight': 2}),
                  ('B', 'C', {'weight': 2}), ('C', 'D', {'weight': 3}), ('D', 'F', {'weight': 2}),
                  ('E', 'F', {'weight': 3}), ('F', 'G', {'weight': 2})])

# 运行Dijkstra算法
distance, predecessor = dijkstra(G, 'A')

# 绘制图形
pos = nx.spring_layout(G)
nx.draw_networkx_nodes(G, pos)
nx.draw_networkx_edges(G, pos)
nx.draw_networkx_labels(G, pos)

# 绘制最短路径
path = ['G']
while predecessor[path[-1]] is not None:
    path.append(predecessor[path[-1]])
path.reverse()
edge_labels = {}
for i in range(len(path)-1):
    edge_labels[(path[i], path[i+1])] = distance[path[i+1]] - distance[path[i]]
nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=8)
nx.draw_networkx_edges(G, pos, edgelist=[(path[i], path[i+1]) for i in range(len(path)-1)], edge_color='r', width=2)

plt.axis('off')
plt.show()

该代码将创建一个有向图，并使用Dijkstra算法计算从节点A到其他节点的最短路径，并将最短路径绘制为红色。输出的图形如下所示：