Python NetworkX-根据属性选项的数量自动设置节点颜色 [英] Python NetworkX -- set node color automatically based on number of attribute options
本文介绍了Python NetworkX-根据属性选项的数量自动设置节点颜色的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在使用NetworkX分析和可视化社交网络.通常，网络中的节点具有与之关联的属性信息，例如划分.但是，我并不总是知道属性划分可能有多少个选项.例如，有时在网络中可能只有3个分区表示为节点属性，而其他时候可能有30个分区.
I am using NetworkX to analyze and visualize social networks. Often, the nodes within the network have attribute information associated with them, such as division. However, I do not always know how many options there might be for the attribute division. For example, sometimes there might be only 3 divisions represented as node attributes within the network, other times there might be 30 divisions.
我已经弄清楚了如何根据节点属性设置节点颜色(请参见下面的代码).但是，在此示例中，我知道节点属性组的选项有5种不同，并自动设置每种颜色.
I've figured out how to set node colors based on node attributes (see code below). However, in this example, I knew how different options there were for the node attribute group (5 options), and set each color automatically.
当节点属性只有3个或5个选项时，选择节点属性颜色并不难，但是当有更多选项时，这变得不现实.
When there are only 3 or 5 options for node attributes, it's not difficult to pick the node attribute colors, but this becomes unrealistic when there are many more options.
我想弄清楚的是如何根据提供的节点属性选项的数量自动选择最佳的节点属性颜色.
有时我有5种颜色属性选择，有时我可能有30种节点属性颜色选择，并且我不想单独设置每个节点的颜色.
我不确定这是否应该使用colormap函数来完成，还是仅通过颜色度量(例如度中心性)用于颜色节点.
I'm not sure if this is something that I should be able to do with the colormap function, or if that is only for color nodes by numeric measures such as degree centrality.
NETWORKX代码
NETWORKX CODE
import networkx as nx
pylab inline
# create an empty graph
g = nx.Graph()
# open csv edgelist and read edges into graph
for line in open('phils_network_edgelist.csv', 'rb'):
edge = line.rstrip().split(',')
g.add_edge(edge[0], edge[1])
# draw network without node color
nx.draw(g, with_labels=False, node_size=25)
# read in node attributes as list of tuples
group_attr = []
for line in open('phils_network_attribute_group.csv', 'rb'):
group_attr.append(tuple(line.rstrip().split(',')))
# convert list of tuples into a dict
group_attr_dict = dict(set(sorted(group_attr)))
# set nodes attributes
nx.set_node_attributes(g, "group", group_attr_dict)
# create empty list for node colors
node_color = []
# for each node in the graph
for node in g.nodes(data=True):
# if the node has the attribute group1
if 'group1' in node[1]['group']:
node_color.append('blue')
# if the node has the attribute group1
elif 'group2' in node[1]['group']:
node_color.append('red')
# if the node has the attribute group1
elif 'group3' in node[1]['group']:
node_color.append('green')
# if the node has the attribute group1
elif 'group4' in node[1]['group']:
node_color.append('yellow')
# if the node has the attribute group1
elif 'group5' in node[1]['group']:
node_color.append('orange')
# draw graph with node attribute color
nx.draw(g, with_labels=False, node_size=25, node_color=node_color)
网络数据
NETWORK DATA
In[58]:
g.nodes(data=True)
Out[58]:
[('BD', {'group': 'group5'}),
('WC', {'group': 'group3'}),
('BA', {'group': 'group4'}),
('WM', {'group': 'group3'}),
('JR', {'group': 'group1'}),
('JS', {'group': 'group3'}),
('JL', {'group': 'group4'}),
('JM', {'group': 'group2'}),
('JK', {'group': 'group2'}),
('JF', {'group': 'group2'}),
('JG', {'group': 'group2'}),
('JA', {'group': 'group2'}),
('JB', {'group': 'group4'}),
('JC', {'group': 'group4'}),
('RR', {'group': 'group3'}),
('RS', {'group': 'group3'}),
('TTI', {'group': 'group3'}),
('RB', {'group': 'group1'}),
('RL', {'group': 'group3'}),
('RO', {'group': 'group4'}),
('LHA', {'group': 'group2'}),
('LHI', {'group': 'group1'}),
('GF', {'group': 'group2'}),
('GB', {'group': 'group4'}),
('EM', {'group': 'group2'}),
('HR', {'group': 'group5'}),
('BS', {'group': 'group3'}),
('HH', {'group': 'group4'}),
('HA', {'group': 'group1'}),
('PS', {'group': 'group1'}),
('PW', {'group': 'group1'}),
('PB', {'group': 'group1'}),
('PC', {'group': 'group5'}),
('MFR', {'group': 'group4'}),
('JMA', {'group': 'group5'}),
('PN', {'group': 'group4'}),
('PL', {'group': 'group3'}),
('ZL', {'group': 'group4'}),
('EB', {'group': 'group2'}),
('ET', {'group': 'group3'}),
('EW', {'group': 'group1'}),
('ER', {'group': 'group3'}),
('MF', {'group': 'group3'}),
('MA', {'group': 'group4'}),
('MM', {'group': 'group2'}),
('MN', {'group': 'group4'}),
('MH', {'group': 'group3'}),
('MK', {'group': 'group2'}),
('JLA', {'group': 'group2'}),
('MP', {'group': 'group1'}),
('MS', {'group': 'group4'}),
('MR', {'group': 'group4'}),
('FI', {'group': 'group5'}),
('CJ', {'group': 'group4'}),
('CO', {'group': 'group5'}),
('CM', {'group': 'group4'}),
('CB', {'group': 'group2'}),
('CG', {'group': 'group2'}),
('CF', {'group': 'group5'}),
('CD', {'group': 'group3'}),
('CS', {'group': 'group2'}),
('CP', {'group': 'group2'}),
('CV', {'group': 'group2'}),
('KC', {'group': 'group1'}),
('KB', {'group': 'group3'}),
('SY', {'group': 'group2'}),
('KF', {'group': 'group2'}),
('KD', {'group': 'group3'}),
('KH', {'group': 'group1'}),
('SW', {'group': 'group1'}),
('KL', {'group': 'group2'}),
('KP', {'group': 'group3'}),
('KW', {'group': 'group1'}),
('SM', {'group': 'group2'}),
('SB', {'group': 'group4'}),
('DJ', {'group': 'group2'}),
('DD', {'group': 'group2'}),
('DV', {'group': 'group5'}),
('BJ', {'group': 'group3'}),
('DR', {'group': 'group2'}),
('KWI', {'group': 'group4'}),
('TW', {'group': 'group2'}),
('TT', {'group': 'group2'}),
('LH', {'group': 'group3'}),
('LW', {'group': 'group3'}),
('TM', {'group': 'group3'}),
('LS', {'group': 'group3'}),
('LP', {'group': 'group2'}),
('TG', {'group': 'group3'}),
('JCU', {'group': 'group2'}),
('AL', {'group': 'group1'}),
('AP', {'group': 'group3'}),
('AS', {'group': 'group3'}),
('IM', {'group': 'group4'}),
('AW', {'group': 'group3'}),
('HHI', {'group': 'group1'})]
In [59]:
g.edges(data=True)
Out[59]:
[('BD', 'ZL', {}),
('BD', 'JCU', {}),
('BD', 'DJ', {}),
('BD', 'BA', {}),
('BD', 'CB', {}),
('BD', 'CG', {}),
('BD', 'AS', {}),
('BD', 'MH', {}),
('BD', 'AP', {}),
('BD', 'HH', {}),
('BD', 'TM', {}),
('BD', 'CF', {}),
('BD', 'CP', {}),
('BD', 'DR', {}),
('BD', 'CV', {}),
('BD', 'EB', {}),
('WC', 'JCU', {}),
('WC', 'JS', {}),
('BA', 'JR', {}),
('BA', 'JB', {}),
('BA', 'RR', {}),
('BA', 'RS', {}),
('BA', 'LH', {}),
('BA', 'PC', {}),
('BA', 'TTI', {}),
('BA', 'PL', {}),
('BA', 'JCU', {}),
('BA', 'CF', {}),
('BA', 'EB', {}),
('BA', 'GF', {}),
('BA', 'AS', {}),
('BA', 'IM', {}),
('BA', 'BJ', {}),
('BA', 'CS', {}),
('BA', 'KH', {}),
('BA', 'SW', {}),
('BA', 'MH', {}),
('BA', 'MR', {}),
('BA', 'HHI', {}),
('WM', 'EM', {}),
('WM', 'JCU', {}),
('WM', 'CO', {}),
('WM', 'LP', {}),
('WM', 'AW', {}),
('WM', 'KD', {}),
('WM', 'TT', {}),
('WM', 'JS', {}),
('WM', 'PB', {}),
('WM', 'JM', {}),
('WM', 'MFR', {}),
('WM', 'RB', {}),
('WM', 'MR', {}),
('WM', 'DV', {}),
('WM', 'TG', {}),
('WM', 'JF', {}),
('WM', 'JMA', {}),
('WM', 'FI', {}),
('WM', 'JB', {}),
('JR', 'GF', {}),
('JR', 'MFR', {}),
('JR', 'KH', {}),
('JR', 'JB', {}),
('JS', 'EM', {}),
('JS', 'PS', {}),
('JS', 'MF', {}),
('JS', 'JCU', {}),
('JS', 'KD', {}),
('JS', 'MH', {}),
('JS', 'TTI', {}),
('JS', 'RB', {}),
('JS', 'TG', {}),
('JL', 'KB', {}),
('JL', 'MN', {}),
('JL', 'LW', {}),
('JL', 'CS', {}),
('JL', 'ET', {}),
('JL', 'ER', {}),
('JM', 'EM', {}),
('JM', 'PS', {}),
('JM', 'KD', {}),
('JM', 'CD', {}),
('JM', 'JK', {}),
('JM', 'TG', {}),
('JM', 'RO', {}),
('JM', 'CV', {}),
('JK', 'HR', {}),
('JK', 'PS', {}),
('JF', 'EM', {}),
('JF', 'PS', {}),
('JF', 'LP', {}),
('JF', 'LHA', {}),
('JF', 'CD', {}),
('JF', 'RB', {}),
('JF', 'JG', {}),
('JF', 'KF', {}),
('JG', 'CJ', {}),
('JG', 'SY', {}),
('JG', 'KF', {}),
('JG', 'LHA', {}),
('JG', 'CD', {}),
('JG', 'RB', {}),
('JG', 'BS', {}),
('JA', 'CS', {}),
('JB', 'KC', {}),
('JB', 'JCU', {}),
('JB', 'MA', {}),
('JB', 'AW', {}),
('JB', 'KWI', {}),
('JB', 'KH', {}),
('JB', 'CF', {}),
('JB', 'EB', {}),
('JB', 'PB', {}),
('JB', 'MFR', {}),
('JB', 'KW', {}),
('JB', 'RB', {}),
('JB', 'MR', {}),
('JB', 'RL', {}),
('JB', 'FI', {}),
('JB', 'JMA', {}),
('JC', 'SM', {}),
('RR', 'MS', {}),
('RR', 'SW', {}),
('RR', 'LH', {}),
('RS', 'LH', {}),
('TTI', 'JCU', {}),
('TTI', 'SW', {}),
('TTI', 'CF', {}),
('RB', 'EM', {}),
('RB', 'PS', {}),
('RB', 'SY', {}),
('RB', 'JCU', {}),
('RB', 'KD', {}),
('RB', 'CF', {}),
('RB', 'LHI', {}),
('RB', 'CD', {}),
('RB', 'MH', {}),
('RB', 'CJ', {}),
('RB', 'TG', {}),
('RB', 'EB', {}),
('RO', 'PS', {}),
('LHA', 'CJ', {}),
('LHA', 'SY', {}),
('LHA', 'KF', {}),
('LHA', 'CD', {}),
('LHI', 'PS', {}),
('LHI', 'CJ', {}),
('GF', 'KC', {}),
('GF', 'MA', {}),
('GB', 'HR', {}),
('GB', 'MM', {}),
('GB', 'LS', {}),
('EM', 'LP', {}),
('EM', 'DV', {}),
('EM', 'TG', {}),
('HR', 'MM', {}),
('HR', 'MH', {}),
('HR', 'EB', {}),
('HR', 'LS', {}),
('BS', 'CD', {}),
('HH', 'ZL', {}),
('HH', 'CB', {}),
('HH', 'CP', {}),
('HH', 'DR', {}),
('HH', 'CV', {}),
('HA', 'SM', {}),
('PS', 'KD', {}),
('PS', 'CF', {}),
('PS', 'TG', {}),
('PW', 'CM', {}),
('PW', 'TW', {}),
('PW', 'TT', {}),
('PW', 'MH', {}),
('PW', 'AL', {}),
('PW', 'MP', {}),
('PW', 'CS', {}),
('PW', 'HHI', {}),
('PW', 'EW', {}),
('PB', 'CO', {}),
('PB', 'KH', {}),
('PB', 'CF', {}),
('PB', 'MFR', {}),
('PB', 'AW', {}),
('PB', 'MA', {}),
('PC', 'CS', {}),
('PC', 'JCU', {}),
('PC', 'SW', {}),
('MFR', 'KC', {}),
('MFR', 'JCU', {}),
('MFR', 'KH', {}),
('MFR', 'MH', {}),
('MFR', 'MR', {}),
('JMA', 'KWI', {}),
('JMA', 'AW', {}),
('PN', 'SB', {}),
('PL', 'HHI', {}),
('PL', 'MK', {}),
('PL', 'LH', {}),
('ZL', 'CB', {}),
('ZL', 'AP', {}),
('ZL', 'CP', {}),
('ZL', 'DR', {}),
('ZL', 'CV', {}),
('EB', 'JCU', {}),
('EB', 'DJ', {}),
('EB', 'CM', {}),
('EB', 'SW', {}),
('EB', 'MM', {}),
('EB', 'LS', {}),
('EB', 'CS', {}),
('EB', 'CP', {}),
('EB', 'CV', {}),
('ET', 'LW', {}),
('ET', 'ER', {}),
('ET', 'KB', {}),
('EW', 'TW', {}),
('EW', 'TT', {}),
('EW', 'HHI', {}),
('EW', 'AL', {}),
('ER', 'LW', {}),
('ER', 'KB', {}),
('MA', 'KW', {}),
('MA', 'AW', {}),
('MA', 'MR', {}),
('MM', 'LS', {}),
('MH', 'JCU', {}),
('MH', 'SY', {}),
('MH', 'DJ', {}),
('MH', 'CM', {}),
('MH', 'AL', {}),
('MH', 'SW', {}),
('MH', 'CF', {}),
('MH', 'LS', {}),
('MH', 'CS', {}),
('MH', 'TG', {}),
('MH', 'CP', {}),
('MH', 'CV', {}),
('MK', 'LH', {}),
('MK', 'KL', {}),
('MK', 'JLA', {}),
('MK', 'MS', {}),
('MK', 'CS', {}),
('JLA', 'CM', {}),
('JLA', 'KL', {}),
('JLA', 'MS', {}),
('JLA', 'CS', {}),
('JLA', 'SB', {}),
('JLA', 'HHI', {}),
('MP', 'TW', {}),
('MP', 'TT', {}),
('MP', 'HHI', {}),
('MS', 'CS', {}),
('MS', 'HHI', {}),
('FI', 'KW', {}),
('FI', 'AW', {}),
('FI', 'CF', {}),
('CJ', 'SY', {}),
('CJ', 'DD', {}),
('CJ', 'CD', {}),
('CO', 'AW', {}),
('CM', 'TW', {}),
('CM', 'TT', {}),
('CM', 'AL', {}),
('CM', 'CS', {}),
('CB', 'DJ', {}),
('CB', 'CP', {}),
('CB', 'CV', {}),
('CG', 'CF', {}),
('CF', 'JCU', {}),
('CF', 'AW', {}),
('CF', 'KH', {}),
('CF', 'LH', {}),
('CF', 'AP', {}),
('CF', 'AS', {}),
('CF', 'KW', {}),
('CF', 'CS', {}),
('CF', 'CV', {}),
('CD', 'SY', {}),
('CD', 'LP', {}),
('CD', 'KF', {}),
('CS', 'JCU', {}),
('CS', 'TW', {}),
('CS', 'TT', {}),
('CS', 'AS', {}),
('CS', 'LH', {}),
('CS', 'SB', {}),
('CS', 'HHI', {}),
('CP', 'DJ', {}),
('CP', 'AP', {}),
('CP', 'DR', {}),
('CP', 'CV', {}),
('CV', 'DJ', {}),
('CV', 'AP', {}),
('CV', 'DR', {}),
('KB', 'LW', {}),
('SY', 'KF', {}),
('KF', 'AP', {}),
('KD', 'TG', {}),
('SW', 'BJ', {}),
('SW', 'IM', {}),
('SW', 'LH', {}),
('KL', 'TT', {}),
('KP', 'TM', {}),
('KW', 'JCU', {}),
('SB', 'AL', {}),
('DJ', 'TG', {}),
('BJ', 'IM', {}),
('KWI', 'AW', {}),
('TW', 'TT', {}),
('TW', 'AL', {}),
('TW', 'HHI', {}),
('TT', 'AL', {}),
('TT', 'HHI', {}),
('LH', 'JCU', {}),
('JCU', 'AP', {}),
('JCU', 'AS', {}),
('AL', 'HHI', {})]
推荐答案
此处是如何使用颜色图的示例.这有点棘手.如果您需要自定义的离散色图，则可以尝试这样回答 Matplotlib离散色图
Here is an example of how to use a colormap. It's a little tricky. If you want a customized discrete colormap you can try this SO answer Matplotlib discrete colorbar
import matplotlib.pyplot as plt
# create number for each group to allow use of colormap
from itertools import count
# get unique groups
groups = set(nx.get_node_attributes(g,'group').values())
mapping = dict(zip(sorted(groups),count()))
nodes = g.nodes()
colors = [mapping[g.node[n]['group']] for n in nodes]
# drawing nodes and edges separately so we can capture collection for colobar
pos = nx.spring_layout(g)
ec = nx.draw_networkx_edges(g, pos, alpha=0.2)
nc = nx.draw_networkx_nodes(g, pos, nodelist=nodes, node_color=colors,
with_labels=False, node_size=100, cmap=plt.cm.jet)
plt.colorbar(nc)
plt.axis('off')
plt.show()
这篇关于Python NetworkX-根据属性选项的数量自动设置节点颜色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！
查看全文
