Python Networkx重量标签定位 [英] Python Networkx Weight Labels Positioning

问题描述

下面的代码对图形中的边缘权重产生非常狡猾"的标签放置.请看图片.我希望有一个更好的放置位置(靠近每条线的中点)，同时仍然可以利用节点的自动定位功能-也就是说，我不想手动定位节点.

The code below produces a very "dodgy" placement of the labels for edge weights in a graph. Please see image. I would like to have a better placement (close to the midpoint of each line), while still taking advantage of the automatic positioning of the nodes - i.e. I don't want to have to manually position the nodes.

请问有什么想法吗?另外还有一个警告-The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.，如果有人知道怎么办，这将是一个很好的解决方法.

Any ideas please? Also there is a warning - The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead. which would be good to address if anyone knows how.

import matplotlib.pyplot as plt
import networkx as nx
import numpy as np

G = nx.Graph()
G.add_nodes_from(["A", "B", "C"])
G.add_edge("A", "B", weight=5)
G.add_edge("B", "C", weight=7)
G.add_edge("C", "A", weight=2)

pos = nx.spring_layout(G)
weights = nx.get_edge_attributes(G, "weight")
nx.draw_networkx(G, with_labels=True)
nx.draw_networkx_edge_labels(G, pos, edge_labels=weights)

plt.show()

推荐答案

来自

draw_networkx(G, pos=None, arrows=True, with_labels=True, **kwds)
Parameters:   
[...]
pos (dictionary, optional) – A dictionary with nodes as keys and positions as values. If not specified a spring layout positioning will
be computed. See networkx.layout for functions that compute node
positions.

因此，如果您未显式传递pos，则会生成spring_layout，但这与您生成的布局不同

So, if you do not pass pos explicitly, a spring_layout is generated, but this won't be identical to the layout that you generate through

pos = nx.spring_layout(G)

，因为两次调用nx.spring_layout(G)会得出不同的结果:

, because calling nx.spring_layout(G) twice gives different results:

for a in [0,1]:
    pos = nx.spring_layout(G)
    print(pos)

输出:

{'A': array([ 0.65679786, -0.91414348]), 'B': array([0.34320214, 0.5814527 ]), 'C': array([-1.        ,  0.33269078])}
{'A': array([-0.85295569, -0.70179415]), 'B': array([ 0.58849111, -0.29820585]), 'C': array([0.26446458, 1.        ])}

因此，将相同的pos传递给两个绘图函数可以解决此问题:

So, passing the same pos to both drawing functions solves the problem:

pos = nx.spring_layout(G)
weights = nx.get_edge_attributes(G, "weight")
nx.draw_networkx(G, pos, with_labels=True)
nx.draw_networkx_edge_labels(G, pos, edge_labels=weights)

这篇关于Python Networkx重量标签定位的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！