如何在networkx图形中获取边权重的位置？

Question

目前^{}库中有一个函数用于获取所有节点的位置：^{}。从单据中引用，返回：

dict : A dictionary of positions keyed by node

并可用作：

G=nx.path_graph(4)
pos = nx.spring_layout(G)

我希望类似于的内容访问加权图的边权重位置。它应该返回将放置边缘权重编号的位置，最好是在边缘的中心和边缘的正上方。（上面我指的是图形的“外部”，因此对于水平放置的正方形图形的最底部边缘，它将刚好位于边缘下方）

所以问题是，有没有类似于spring_layout的内置功能来实现这一点？如果没有，你自己怎么做

Answer 1

可以使用nx.draw_edge_labels返回一个字典，其中边作为键，(x, y, label)作为值

import matplotlib.pyplot as plt
import networkx as nx

# Create a graph
G = nx.path_graph(10)

# Add 2 egdes with labels
G.add_edge(0, 8, name='n1')
G.add_edge(2, 7, name='n2')

# Get the layout
pos = nx.spring_layout(G)

# Draw the graph
nx.draw(G, pos=pos)

# Draw the edge labels
edge_labels = nx.draw_networkx_edge_labels(G, pos)

现在您可以看到变量edge_labels

print(edge_labels)
# {(0, 1): Text(0.436919941201627, -0.2110471432994752, '{}'),
#  (0, 8): Text(0.56941037628304, 0.08059107891826373, "{'name': 'n1'}"),
#  (1, 2): Text(0.12712625526483384, -0.2901338796021985, '{}'),
#  (2, 3): Text(-0.28017240645783603, -0.2947104829441387, '{}'),
#  (2, 7): Text(0.007024254096114596, -0.029867791669433513, "{'name': 'n2'}"),
#  (3, 4): Text(-0.6680363649371021, -0.26708812849092933, '{}'),
#  (4, 5): Text(-0.8016944207643129, -0.0029986274715349814, '{}'),
#  (5, 6): Text(-0.5673817462107436, 0.23808073918504968, '{}'),
#  (6, 7): Text(-0.1465270298295821, 0.23883392944036055, '{}'),
#  (7, 8): Text(0.33035539545007536, 0.2070939421162053, '{}'),
#  (8, 9): Text(0.7914739158501038, 0.2699223242747882, '{}')}

现在，要获得edge(2,7)的位置，只需执行以下操作

print(edge_labels[(2,7)].get_position())
# Output: (0.007024254096114596, -0.029867791669433513)

您可以阅读有关文档here的更多信息

如果要提取所有边的x,y坐标，可以尝试以下操作：

edge_label_pos = { k: v.get_position()
                  for k, v in edge_labels.items()}
#{(0, 1): (0.436919941201627, -0.2110471432994752),
# (0, 8): (0.56941037628304, 0.08059107891826373),
# (1, 2): (0.12712625526483384, -0.2901338796021985),
# (2, 3): (-0.28017240645783603, -0.2947104829441387),
# (2, 7): (0.007024254096114596, -0.029867791669433513),
# (3, 4): (-0.6680363649371021, -0.26708812849092933),
# (4, 5): (-0.8016944207643129, -0.0029986274715349814),
# (5, 6): (-0.5673817462107436, 0.23808073918504968),
# (6, 7): (-0.1465270298295821, 0.23883392944036055),
# (7, 8): (0.33035539545007536, 0.2070939421162053),
# (8, 9): (0.7914739158501038, 0.2699223242747882)}