在网络x中按重量着色边缘 [英] Colouring edges by weight in networkx

问题描述

我只找到类似于我想要的东西：

然而，我似乎无法将此应用于我的问题。我有一个带加权边的图，但是权重并不是唯一的（所以有15个边的权重为1）。我想根据自己的体重对边缘进行着色，重量越轻，颜色越浅。

我尝试应用上述问题中提出的方法，但根据我的理解，这要求权重在每条边上都是唯一的？

到目前为止，我已经按照不同边权重的升序列出了一个列表，并且想要使用这样来分类可能的边缘颜色。我试图避免通过重量来绘制边缘，因为我可能需要在未来绘制一个非常大的图形，边缘上有很大的权重范围。

如果目前还不清楚让我知道在评论中，我会给出更具体的信息。

谢谢！

编辑：
def draw_graph（target）：
nlist = [target] + G.neighbors（target）
H = nx.subgraph（G，nlist）
n = H.number_of_edges（）
colors = range（n）
labels，weights = colour_and_label_edges（H）

  pos = nx。 spring_layout（H）
 nx.draw（H，pos，node_color ='＃A0CBE2'，edge_color = colors，node_size = 100，edge_cmap = plt.cm.Blues，width = 0.5，with_labels = False）
 nx.draw_networkx_edge_labels（H，pos，edge_labels = labels）
 plt.savefig（Graphs / edge_colormap_％s.png％target）＃另存为b 
 plt.show（）＃display 
 $通过
 
 def colour_and_label_edges（graph）：
d = {} $ b $ for（u，v）in graph.edges（）：
d [u，v] =图表[U] [v] ['weight'] 
 temp = [] 
 for d.values（）中的val：
如果val不在temp中：
 temp.append（val）
 weight = sorted（temp，key = int）
 return d，权重
  

上面的代码是不完整的，但想法是这个函数给了我一个权重列表，如下所示：

[1,2,3,4,5,6 ，9,10,16,21,47,89,124,134,224]

然后我想用这个列表为每个权重指定一个颜色，重量越深颜色。 （这个例子相对于数据集使用了一个非常小的子图）。希望将其清除一点：S

解决方案

您可以使用边缘权重和颜色表来绘制它们。

  import matplotlib.pyplot as plt 
导入networkx为nx 
进口随机
 
 G = nx.gnp_random_graph（10,0.3）
 for g，u，v，d in G.edges（data = True）：
d ['weight '] = random.random（）
 
 edges，weights = zip（* nx.get_edge_attributes（G，'weight'）。items（））
 
 pos = nx。 spring_layout（G）
 nx.draw（G，pos，node_color ='b'，edgelist = edges，edge_color = weights，width = 10.0，edge_cmap = plt.cm.Blues）
 plt.savefig（ 'edges.png'）
  

I have only found something similar to what I want here:

Coloring networkx edges based on weight

However I can't seem to apply this to my problem. I have a graph with weighted edges, but the weights aren't unique (so there are like 15 edges with weight 1). I want to colour my edges based on the weight they have, the lower the weight the lighter the colour.

I tried to apply the method suggested in the above question, but from what I understand this requires the weights to be unique on each edge?

So far I've produced a list in ascending order of the different edge weights and wanted to use this to classify the possible edge colours. I'm trying to avoid drawing the edges by weight as I may need to draw a very large graph in the future with a huge range of weights on the edges.

If it's unclear let me know in comments and I'll give more specific info.

Thanks!

EDIT: def draw_graph(target): nlist = [target]+G.neighbors(target) H=nx.subgraph(G, nlist) n=H.number_of_edges() colours = range(n) labels,weights = colour_and_label_edges(H)

pos = nx.spring_layout(H)
nx.draw(H, pos, node_color='#A0CBE2',edge_color=colours, node_size=100, edge_cmap=plt.cm.Blues, width=0.5, with_labels=False)
nx.draw_networkx_edge_labels(H, pos, edge_labels=labels)
plt.savefig("Graphs/edge_colormap_%s.png" % target) # save as png
plt.show() # display
pass

def colour_and_label_edges(graph):
    d={}
    for (u,v) in graph.edges():
        d[u,v]=graph[u][v]['weight']
    temp=[]
    for val in d.values():
        if val not in temp:
            temp.append(val)
    weights = sorted(temp,key=int)
    return d, weights

The above code is incomplete, but the idea is the function gives me a list of the weights, as so:

[1, 2, 3, 4, 5, 6, 9, 10, 16, 21, 47, 89, 124, 134, 224]

I then want to use this list to assign each weight a colour, the higher the weight the darker the colour. (I've used a very small subgraph for this example relative to the data set). Hope that clears it up a little :S

解决方案

You can use the edge weights and a colormap to draw them. You might want t a different colormap from the one below.

import matplotlib.pyplot as plt
import networkx as nx
import random

G = nx.gnp_random_graph(10,0.3)
for u,v,d in G.edges(data=True):
    d['weight'] = random.random()

edges,weights = zip(*nx.get_edge_attributes(G,'weight').items())

pos = nx.spring_layout(G)
nx.draw(G, pos, node_color='b', edgelist=edges, edge_color=weights, width=10.0, edge_cmap=plt.cm.Blues)
plt.savefig('edges.png')

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