用python里的networkx函数找到指定边的参数值，写出最简洁的代码

假设要找到图G中从节点1到节点2的边的参数值，可以用以下代码：

import networkx as nx

# 创建图G
G = nx.Graph()
G.add_edge(1, 2, weight=3)

# 找到从节点1到节点2的边的参数值
edge_data = G.get_edge_data(1, 2)
weight = edge_data['weight']
print(weight)

其中，`get_edge_data(1, 2)`返回一个字典，包含从节点1到节点2的边的所有参数值。因为我们知道这条边的参数名为'weight'，所以可以用`edge_data['weight']`来获取其参数值。

相关问题

用python写出networkx中dijkstra_path的函数

以下是使用Python编写的NetworkX中的dijkstra_path函数：

import networkx as nx

def dijkstra_path(G, source, target, weight='weight'):
    dist = {}
    prev = {}
    Q = set()
    for v in G.nodes():
        dist[v] = float('inf')
        prev[v] = None
        Q.add(v)
    dist[source] = 0
    while len(Q) > 0:
        u = None
        for v in Q:
            if u is None or dist[v] < dist[u]:
                u = v
        if u == target:
            break
        Q.remove(u)
        for neighbor in G.neighbors(u):
            alt = dist[u] + G[u][neighbor].get(weight, 1)
            if alt < dist[neighbor]:
                dist[neighbor] = alt
                prev[neighbor] = u
    path = []
    u = target
    while prev[u] is not None:
        path.append(u)
        u = prev[u]
    path.append(u)
    path.reverse()
    return path

该函数接受三个参数：

- G：一个NetworkX图对象。
- source：源节点。
- target：目标节点。
- weight（可选）：权重属性的名称，如果没有指定，则默认为'weight'。

该函数使用Dijkstra算法计算从源节点到目标节点的最短路径，并返回路径的节点列表。如果不存在从源节点到目标节点的路径，则返回空列表。

请写出相应的python代码

当然，这里我会给你提供一个简单的Python版本的Ford-Fulkerson算法（最大流）和Dinic's Algorithm（也是求最大流的一种方法，适用于有向图中带权重的边）的例子。请注意，这仅是基础版，实际应用中可能会需要更复杂的库如networkx或Pandas。

**最大流 (Ford-Fulkerson) 示例：**

from collections import defaultdict

def ford_fulkerson(graph, src, dest):
    def augment_path(u, flow, graph, parent):
        path_flow = 0
        while u != dest:
            path_flow += graph[u][parent][flow]
            flow = min(flow, graph[parent][u])
            u = parent
        return path_flow

    graph = {node: {} for node in graph}
    max_flow = 0
    residual_graph = {node: {} for node in graph}

    for u, adjacencies in graph.items():
        for v, capacity in adjacencies.items():
            if v != src and v != dest:
                residual_graph[u][v] = capacity
                residual_graph[v][u] = 0
            else:
                residual_graph[u][v] = 0
                residual_graph[v][u] = -capacity

    while True:
        prev = {src: None}
        flow = dfs(residual_graph, src, dest, prev)
        if flow == 0:
            break
        max_flow += flow

    return max_flow

# 辅助函数：深度优先搜索（DFS）
def dfs(residual_graph, u, v, prev):
    if u not in residual_graph or v not in residual_graph[u]:
        return 0
    if u == v:
        return residual_graph[u][v]

    path_flow = 0
    for neighbor, flow in residual_graph[u].items():
        if flow > 0 and prev.get(neighbor) is None:
            new_flow = dfs(residual_graph, neighbor, v, {neighbor: u})
            if new_flow > 0:
                path_flow += new_flow
                residual_graph[u][neighbor] -= new_flow
                residual_graph[neighbor][u] += new_flow

    return path_flow

# 使用示例:
graph = {
    'A': {'B': 5, 'C': 3},
    'B': {'A': 5, 'C': 2, 'D': 4},
    'C': {'A': 3, 'B': 2, 'D': 1},
    'D': {'B': 4, 'C': 1, 'E': 6},
    'E': {'D': 6}
}

print(ford_fulkerson(graph, 'A', 'E'))  # 输出最大流

**Dinic's Algorithm 示例（适用于带权边）：**

import heapq

def dinic(graph, src, sink):
    INF = float('inf')
    n = len(graph)
    level = [None] * n
    q = []

    def bfs():
        nonlocal q
        level = [-1] * n
        level[src] = 0
        q.append(src)
        while q:
            curr = q.pop(0)
            for nei, cap in graph[curr].items():
                if level[nei] is None and cap > 0:
                    level[nei] = level[curr] + 1
                    q.append(nei)

    flow = 0
    while bfs():
        push = []
        for node in range(n):
            if level[node] is None:
                continue
            for nei, cap in graph[node].items():
                if cap > 0:
                    push.append((-cap, node, nei))
        if not push:
            break
        flow += heapq.heappop(push)[0]

        curr = sink
        while curr != src:
            graph[curr][level[curr]][curr], graph[level[curr]][curr][curr] = (
                0,
                graph[level[curr]][curr][curr] + graph[curr][level[curr]][curr],
            )
            curr = level[curr]

    return flow

# 使用示例:
graph = {
    'A': {'B': 5, 'C': 3},
    'B': {'A': 5, 'C': 2, 'D': 4, 'E': 8},
    'C': {'A': 3, 'B': 2, 'D': 1},
    'D': {'B': 4, 'C': 1, 'E': 0},
    'E': {'B': 8, 'D': 0, 'F': 3},
    'F': {'E': 3}
}

print(dinic(graph, 'A', 'F'))  # 输出最大流