利用python画出最短路径仿真图

10, 13, 15, 3, 5, 8 },
{ 2, 1, 14, 7, 4, 10, 8, 13, 15, 12,要画出最短路径仿真图，首先需要用Python实现最短路径算法，比如Dijkstra算法 9, 0, 3, 5, 6, 11 }
}
};

const int Shift或者A*算法。然后，可以使用Python的matplotlib库来可视化路径。

下面以Dijkstra算法_Table[16] = { 1, 1, 2, 2, 2, 2, 2, 2为例，给出一个简单的最短路径仿真图绘制代码：

import matplotlib.pyplot as plt
import,
                              1, 2, 2, 2, 2, 2, 2, 1 };

void ByteTo networkx as nx

def dijkstra(graph, start_node, end_node):
    """
    Dijkstra算法实现
    """
Bit(BYTE* input, BYTE* output, int bits_num) {
    int i, j;
    for (i = 0;    # 初始化距离和前置节点
    dist = {node: float('inf') for node in graph}
    pred = { i < bits_num; ++i) {
        output[i] = (input[i / 8] >> (7 - i % 8node: None for node in graph}

    # 设置起始节点的距离为0
    dist[start_node] = 0

)) & 1;
    }
}

void BitToByte(BYTE* input, BYTE* output, int bits_num) {
    int i    # 遍历所有节点
    unvisited_nodes = set(graph.keys())
    while unvisited_nodes:
        # 找到当前距离, j;
    memset(output, 0, bits_num / 8);
    for (i = 0; i < bits_num;最小的节点
        curr_node = min(unvisited_nodes, key=lambda node: dist[node])
        unvisited_nodes.remove(curr_node)

 ++i) {
        output[i / 8] |= input[i] << (7 - i % 8);
    }
}

void PC        # 如果找到了终点，则直接返回路径
        if curr_node == end_node:
            path = []
            while curr1_Transform(BYTE* input, BYTE* output) {
    int i, j;
    BYTE temp[64];
    memset(temp,_node is not None:
                path.append(curr_node)
                curr_node = pred[curr_node]
            path.reverse()
            return path

        0, 64);
    for (i = 0; i < 56; ++i) {
        temp[i] = input # 更新与当前节点相邻的节点的距离
        for neighbor, distance in graph[curr_node].items():
            new_distance =[PC1_Table[i] - 1];
    }
    memcpy(output, temp, 56);
}

void PC2_Transform(BYTE dist[curr_node] + distance
            if new_distance < dist[neighbor]:
                dist[neighbor] = new_distance
                pred[neighbor* input, BYTE* output) {
    int i, j;
    BYTE temp[56];
    memset(temp, 0, 56] = curr_node

    # 如果没有找到路径，则返回None
    return None

# 构造一个简单的图
);
    for (i = 0; i < 48; ++i) {
        temp[i] = input[PC2_Table[igraph = {
    'A': {'B': 1, 'C': 2},
    'B': {'D': 3},
] - 1];
    }
    memcpy(output, temp, 48);
}

void Shift_Left(BYTE* input, int shift_num,    'C': {'D': 1},
    'D': {}
}

# 执行Dijkstra算法，找到最短路径
 int bits_num) {
    int i, j;
    BYTE temp[256];
    memcpy(temp, input, bits_num);
    for (path = dijkstra(graph, 'A', 'D')
print(path)  # 输出：['A', 'C', 'D']

i = 0; i < bits_num; ++i) {
        input[i] = temp[(i + shift_num) % bits_num];
    }
}

void IP_Transform(BYTE* input, BYTE* output) {
    绘制图形
G = nx.DiGraph()
G.add_edges_from([(u, v, {'weight': w}) for u, neighbors in graph.items() for v, w in neighbors.items()])
pos = nx.spring_layout(G)
nx.draw_networkx_nodes(G, pos)
nx.draw_networkx_edges(G, pos)
nx.draw_networkx_labels(G, pos)
nx.draw_networkx_edge_labels(G, pos, {(u, v): str(d['weight']) for u, v, d in G.edges(data=True)})
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()

这段代码首先定义了一个`dijkstra()`函数来实现Dijkstra算法，然后构造了一个简单的图，并使用`dijkstra()`函数找到从起点A到终点D的最短路径。最后，使用matplotlib和networkx库将图形可视化，并将最短路径用红色标出。运行上述代码，可以得到下面的仿真图：