题意：如何从networkx中按照特定顺序提取图的边
问题背景：
I am trying to find the shortest path that passes through a set of points that are pretty much aligned. This needs to work in all directions, so I can't just sort them by x or y values.
我正在尝试找到一条通过一系列大致对齐的点的最短路径。这需要在所有方向上都能工作，所以我不能仅仅通过x或y值对它们进行排序。
The solution I came up with was to make a graph using networkx, find the minimum spanning tree, and export the shortest path. I want to end up with a shapely LineString (not MultiLineString)
我提出的解决方案是使用networkx创建一个图，找到最小生成树，并导出最短路径。我希望最终得到一个shapely LineString（而不是MultiLineString）。
It turns out networkx plots exactly what I want, but I did not find a way to export the edges in the correct order to build a LineString, it always makes a mess. In order to do that, I need the vertices (or the nodes) in the correct order, as shown on the plot.
事实证明，networkx正好绘制了我想要的内容，但我没有找到一种方法来按正确的顺序导出边以构建LineString，它总是搞得一团糟。为了做到这一点，我需要按照绘图上所示的正确顺序来获取顶点（或节点）。
Can anyone help me ? Here's the example code below:
有人能帮助我吗？下面是代码示例：
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from scipy.spatial.distance import pdist, squareform
points_line = np.array([[ 0.46734317, 149.36430674],
[ 0.46734547, 149.36334419],
[ 0.46744031, 149.36238631],
[ 0.4676268 , 149.36144199],
[ 0.46734317, 149.36430674],
[ 0.46743343, 149.36526506],
[ 0.4676154 , 149.36621026],
[ 0.46788741, 149.36713358],
[ 0.46824692, 149.36802648],
[ 0.94443392, 150.40378967],
[ 0.4676268 , 149.36144199],
[ 1.55364459, 144.98283937],
[ 0.94443392, 150.40378967],
[ 0.68606383, 157.76059211],
[ 0.68606634, 157.76135963],
[ 1.55364459, 144.98283937],
[ 1.6347943 , 136.57997287],
[ 0.92188273, 132.24795534],
[ 0.92178416, 132.24715728],
[ 0.92175003, 132.24635387],
[ 0.90765426, 125.94462804],
[ 0.90769726, 125.94367903],
[ 0.68606634, 157.76135963],
[ 1.08596441, 167.35367069],
[ 0.66299718, 175.68436124],
[ 0.90769726, 125.94367903],
[ 1.8455184 , 115.86662374],
[ 1.22148527, 103.42945831],
[ 1.22148224, 103.42852062],
[ 1.22156706, 103.42758676],
[ 1.22173897, 103.42666495],
[ 1.89690364, 100.55965775],
[ 1.23628246, 92.47574962],
[ 1.23624942, 92.47487388],
[ 1.23629318, 92.47399861],
[ 1.23641341, 92.47313053],
[ 2.28757468, 86.74385772],
[ 2.28778124, 86.74296467],
[ 2.2880687 , 86.74209429],
[ 2.28843466, 86.74125389],
[ 2.28887603, 86.74045053],
[ 2.2893891 , 86.73969096],
[ 2.82731279, 86.01709145]])
# Create a graph
G = nx.Graph()
# Add nodes to the graph
for i, point in enumerate(points_line):
G.add_node(i, pos=point)
# Calculate pairwise distances between all points
distances = pdist(points_line)
distance_matrix = squareform(distances)
# Add edges to the graph with weights based on distances
for i in range(len(points_line)):
for j in range(i+1, len(points_line)):
G.add_edge(i, j, weight=distance_matrix[i][j])
# Calculate the minimum spanning tree
mst = nx.minimum_spanning_tree(G)
# Extract edges from the minimum spanning tree
edges = list(mst.edges())
# Extract consecutive points from the edges to form the sequence of points
consecutive_points = []
for edge in edges:
consecutive_points.append(points_line[edge[0]])
consecutive_points.append(points_line[edge[1]])
# Convert the consecutive points to a numpy array
consecutive_points_array = np.array(consecutive_points)
# Find indices where the next point is not the same as the current point
indices = np.concatenate(([True], np.any(np.diff(consecutive_points_array, axis=0) != 0, axis=1)))
# Filter out the points based on the indices
filtered_points_array = consecutive_points_array[indices]
# Plot the original points and the minimum spanning tree
pos = nx.get_node_attributes(G, 'pos')
positions = dict(zip(mst.nodes, 'pos'))
plt.figure(figsize=(12, 8))
nx.draw_networkx_nodes(G, pos, node_size=20)
nx.draw_networkx_edges(mst, pos, edge_color='r', width=1)
plt.title("Minimum Spanning Tree")
plt.axis('eI tried all of the exporting functions built in networkx to try and get to a numpy array, but it had repeating nodes and it did not work even after cleaning. Tried some solutions from LLMs but none worked.
我尝试了networkx中所有内置的导出函数，试图得到一个numpy数组，但是结果中有重复的节点，即使在清理之后也无法正常工作。我也尝试了一些来自大型语言模型（LLMs）的解决方案，但都没有成功。
问题解决：
A simple option would be to remove_edges_from the tree to exclude overlapping nodes (points with a weight/distance of 0), then reindex the points_line array with all_simple_paths (i.e, a list that holds nodes' indices) :
一个简单的方法是从树中移除边以排除重叠的节点（权重/距离为0的点），然后使用all_simple_paths（即包含节点索引的列表）对points_line数组进行重新索引：
from shapely import LineString
mst.remove_edges_from(
[(u, v) for (u, v, w) in mst.edges(data=True) if w["weight"] == 0]
)
# must be exactly two, e.g: 24, 42
extremities = [n for n, d in mst.degree() if d == 1]
ls = LineString(points_line[list(nx.all_simple_paths(mst, *extremities))].squeeze())
