流程图（一）利用python绘制弦图

python_0">流程图（一）利用python绘制弦图

弦图（Chord diagram）简介

数据围绕一个圆呈放射状排列，显示不同实体之间的相互关系，这既是弦图。弦图通过每个圆弧的大小比例表示连接分配数值，可以用颜色将数据分成不同类别以助于比较和区分。缺点则是当连接过多时会显得混乱。

快速绘制

基于bokeh

首先需要安装holoviews：pip install holoviews

然后安装最新的bokeh即可：pip install --upgrade bokeh

python">import pandas as pd
import holoviews as hv
from holoviews import opts, dim
from bokeh.sampledata.les_mis import datahv.extension('bokeh')
hv.output(size=300)# 导入数据
nodes = hv.Dataset(pd.DataFrame(data['nodes']), 'index')
links = pd.DataFrame(data['links'])# 绘制弦图
chord = hv.Chord((links, nodes)).select(value=(5, None))
chord.opts(opts.Chord(cmap='Category20', edge_cmap='Category20', edge_color=dim('source').str(), labels='name', node_color=dim('index').str()))

基于pyecharts

python">import requests
import json
from pyecharts import options as opts
from pyecharts.charts import Graph# 获取官方的数据
url = "https://raw.githubusercontent.com/pyecharts/pyecharts-gallery/master/Graph/les-miserables.json"response = requests.get(url)
j = json.loads(response.text)
nodes = j["nodes"]
links = j["links"]
categories = j["categories"]c = (Graph(init_opts=opts.InitOpts(width="1000px", height="600px")).add("",nodes=nodes,links=links,categories=categories,layout="circular",is_rotate_label=True,linestyle_opts=opts.LineStyleOpts(color="source", curve=0.3),label_opts=opts.LabelOpts(position="right"),).set_global_opts(title_opts=opts.TitleOpts(title="悲惨世界角色关系"),legend_opts=opts.LegendOpts(orient="vertical", pos_left="2%", pos_top="20%"),)
)c.render_notebook()

基于mne

pip install -U mne
pip install -U mne-connectivity
pip install nibabel

python">from mne_connectivity.viz import plot_connectivity_circle 
import numpy as np# 自定义数据：随机连接的20个node
N = 20
node_names = [f"N{i}" for i in range(N)]# 随机连接
ran = np.random.rand(N,N)
con = np.where(ran > 0.9, ran, np.nan)  # 低于0.9的连接置为NaN# 绘制弦图
fig, axes = plot_connectivity_circle(con, node_names)

定制多样化的弦图

自定义日历弦图一般是结合使用场景对相关参数进行修改，并辅以其他的绘图知识。参数信息可以通过官网进行查看，其他的绘图知识则更多来源于实战经验，大家不妨将接下来的绘图作为一种学习经验，以便于日后总结。

修改node_angles实现弦图分割

python">from mne_connectivity.viz import plot_connectivity_circle 
import numpy as np# 自定义数据：随机连接的20个node
N = 20
node_names = [f"N{i}" for i in range(N)]# 随机连接
ran = np.random.rand(N,N)
con = np.where(ran > 0.9, ran, np.nan)  # 低于0.9的连接置为NaN# 自定义弧度
start, end = 45, 135
first_half = (np.linspace(start, end, len(node_names)//2) +90).astype(int)[::+1] %360
second_half = (np.linspace(start, end, len(node_names)//2) -90).astype(int)[::-1] %360
node_angles = np.array(list(first_half) + list(second_half))# 自定义参数node_angles
fig, axes = plot_connectivity_circle(con, node_names, node_angles=node_angles)

自定义节点

python">from mne_connectivity.viz import plot_connectivity_circle 
import numpy as np# 自定义数据：随机连接的20个node
N = 20
node_names = [f"N{i}" for i in range(N)]# 随机连接
ran = np.random.rand(N,N)
con = np.where(ran > 0.9, ran, np.nan)  # 低于0.9的连接置为NaN# 自定义节点
node_edgecolor = N//2 * [(0,0,0,0.)] + N//2 * ['green']
node_colors = N//2 * ['crimson'] + N//2 * [(0,0,0,0.)]fig, axes = plot_connectivity_circle(con, node_names, 
#     node_width=50, # 节点宽度
#      vmin=0.97, vmax=0.99, # 显示连接的范围
#      node_colors=node_colors, # 自定义节点颜色                            
#      node_edgecolor=node_edgecolor, # 自定义节点颜边缘node_linewidth=2, # 自定义节点线宽colormap='Blues', facecolor='white',textcolor='black',colorbar=False, linewidth=10 # 连接线宽度                              
)