Question

我正在尝试使用matplotlib为图形创建networkx动画，但在尝试读取包含一系列节点的矩阵时遇到困难，这表示要遵循的路径，并在每一行的末尾，它具有该路径的总重量。所以它看起来像这样：

[node1, node2, node3, ..., node9, weight1   ]
[node1, node3, node2, ..., node9, weight2   ](the nodes 1 to 9 are permuted and 
.                             .     .          then drawed in pairs)
.                             .     .
.                             .     .
[node9, node8, node4, ..., node1, weight(9!)]

我是Python的新手，我习惯于在C中工作，所以我想做的更像是：

检查矩阵中的第一行，读取节点并将其保存在元组数组中：

nPath=[(node1,node2),(node2,node3),(node3,node4),...,(node8,node9),(node9,node1)]
使用该行的权重并测试它是否为最短行。
在图表中显示路径，然后返回并尝试使用第2行。
重复。

到目前为止，这是我的代码：

import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import itertools as it
import matplotlib.animation as animation

#Contains the combinations of each pair of nodes and their weight.
with open("pair_weight.txt","r") as file:
    W_edges = np.loadtxt(file, dtype=int, delimiter='   ')

G = nx.Graph()
G.add_weighted_edges_from(W_edges)

fig, ax = plt.subplots(figsize=(6,4))

#Contains the total waight of each path 1 to 9.
with open("Path_Weights.txt","r") as file:
    W_paths = np.loadtxt(file, dtype=int)

#contains all 9! permutations of the nodes
with open("Permutations.txt","r") as file:
    nPath = np.loadtxt(file, dtype=int, delimiter=' ')

nPaths = np.c_[nPath, W_paths]

pos = nx.kamada_kawai_layout(G)

labels = {}    
for node in G.nodes():
    if node in pos: 
        labels[node] = node

npaths = []

def update_path(num):
    for i in range(0,8):
        if i==8:          #This is were i'm trying to do it like in c.
            npaths.append[(nPaths.item((num, i))),(nPaths.item((num,0)))]
    npaths.append[(nPaths.item((num, i))),(nPaths.item((num,i+1)))]
    nx.draw_networkx_edges(G, pos, width=2, edgelist=npaths, edge_color='r')


    nx.draw_networkx_nodes(G, pos, node_size=900, node_color='skyblue', node_shape='o', alpha=0.7, edgecolor='deepskyblue') 
    nx.draw_networkx_labels(G, pos, labels, font_size=14, font_color='k', alpha=5)
    nx.draw_networkx_edges(G, pos, edge_color='gray')

ani = animation.FuncAnimation(fig, update_path, frames=6, interval=1000, repeat=True)
plt.show()

Answer 1

如果nPath（不包含s）是包含节点路径的矩阵，请假设(P, N) P个N个节点的路径，你可以创建一个数组，比如说nPathEdges，形状为(P, N, 2)，其中nPathEdges[i, j]包含{{1}的j - 边（表示为[node1, node2]）i - 路径，像这样：

nPathEdges = np.stack([nPath, np.roll(nPath, -1, axis=1)], axis=2)

然后你可以迭代这个矩阵和权重，如：

w_min = np.inf
for edges, w in zip(nPathEdges, W_paths):
    # You can have it as a list of tuples like this,
    # although as far as I can tell NetworkX should work fine with a NumPy array too
    edges = [tuple(e) for e in edges]
    nx.draw_networkx_edges(G, pos, width=2, edgelist=npaths, edge_color='r')
    if w < w_min:
        w_min = w
        # Do something for minimum weight...

如何将np.matrix的每一行保存到9对touple数组？

1 个答案: