问题描述
Year NodeName NodeSize
1990 A 50
1990 B 10
1990 C 100
1995 A 90
1995 B 70
1995 C 60
2000 A 150
2000 B 90
2000 C 100
2005 A 55
2005 B 90
2005 C 130
我希望将节点放在列中，这样每一年都是一列，每一行都是一个节点名称，并且节点大小反映了所指示的数量。
然后我在数据框中具有以下边缘，如下所示：
FromYear ToYear FromNode ToNode EdgeWidth
1990 1995 A B 60
1990 1995 A C 20
1990 1995 B A 10
1990 1995 C B 10
1995 2000 A B 60
1995 2000 B A 30
1995 2000 C A 10
1995 2000 C B 10
1995 2000 B C 70
2000 2005 A B 10
2000 2005 A C 60
2000 2005 B A 60
2000 2005 B C 25
2000 2005 C B 44
2000 2005 C A 10
其中第二个数据帧表示边缘信息。比如第一行，是从1990列下的节点A到1995列下的节点B的箭头，边的宽度与边宽列中的数字成线性关系。
似乎有很多关于 networkx 的教程，希望得到指导。
这是我希望它看起来像的粗略草图。如果可能的话，每行节点也应该是不同的颜色。我希望它是某种信息图，而不是显示节点之间多年来流动的典型网络。
import pandas as pd
nodes = pd.DataFrame(
[(1990,'A',50),(1990,'B',10),'C',100),(1995,90),70),60),(2000,150),(2005,55),130)],columns=['Year','NodeName','NodeSize'])
edges = pd.DataFrame(
[(1990,1995,20),2000,30),2005,25),44),10)],columns = ['FromYear','ToYear','FromNode','ToNode','EdgeWidth'])
解决方法
真的很简单。将 NodeName 转换为 y 坐标，将 Year 转换为 x 坐标，然后绘制一堆 Circle 和 FancyArrow 块。
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.patches import Circle,FancyArrow
nodes = pd.DataFrame(
[(1990,'A',50),(1990,'B',10),'C',100),(1995,90),70),60),(2000,150),(2005,55),130)],columns=['Year','NodeName','NodeSize'])
edges = pd.DataFrame(
[(1990,1995,20),2000,30),2005,25),44),10)],columns = ['FromYear','ToYear','FromNode','ToNode','EdgeWidth'])
# compute node coordinates: year -> x,letter -> y;
# np.unique(z,return_inverse=True) maps the unique and alphanumerically
# ordered elements in z to consecutive integers,# and returns the result as a second output argument
nodes['x'] = np.unique(nodes['Year'],return_inverse=True)[1]
nodes['y'] = np.unique(nodes['NodeName'],return_inverse=True)[1]
# A should be on top,C on bottom
nodes['y'] = np.max(nodes['y']) - nodes['y']
# Year NodeName NodeSize x y
# 0 1990 A 50 0 2
# 1 1990 B 10 0 1
# 2 1990 C 100 0 0
# 3 1995 A 90 1 2
# 4 1995 B 70 1 1
# 5 1995 C 60 1 0
# 6 2000 A 150 2 2
# 7 2000 B 90 2 1
# 8 2000 C 100 2 0
# 9 2005 A 55 3 2
# 10 2005 B 90 3 1
# 11 2005 C 130 3 0
# compute edge paths
edges = pd.merge(edges,nodes,how='inner',left_on=['FromYear','FromNode'],right_on=['Year','NodeName'])
edges = pd.merge(edges,left_on=['ToYear','ToNode'],'NodeName'],suffixes=['_start','_stop'])
# FromYear ToYear FromNode ToNode EdgeWidth Year_start NodeName_start NodeSize_start x_start y_start Year_stop NodeName_stop NodeSize_stop x_stop y_stop
# 0 1990 1995 A B 60 1990 A 50 0 2 1995 B 70 1 1
# 1 1990 1995 C B 10 1990 C 100 0 0 1995 B 70 1 1
# 2 1990 1995 A C 20 1990 A 50 0 2 1995 C 60 1 0
# 3 1990 1995 B A 10 1990 B 10 0 1 1995 A 90 1 2
# 4 1995 2000 A B 60 1995 A 90 1 2 2000 B 90 2 1
# 5 1995 2000 C B 10 1995 C 60 1 0 2000 B 90 2 1
# 6 1995 2000 B A 30 1995 B 70 1 1 2000 A 150 2 2
# 7 1995 2000 C A 10 1995 C 60 1 0 2000 A 150 2 2
# 8 1995 2000 B C 70 1995 B 70 1 1 2000 C 100 2 0
# 9 2000 2005 A B 10 2000 A 150 2 2 2005 B 90 3 1
# 10 2000 2005 C B 44 2000 C 100 2 0 2005 B 90 3 1
# 11 2000 2005 A C 60 2000 A 150 2 2 2005 C 130 3 0
# 12 2000 2005 B C 25 2000 B 90 2 1 2005 C 130 3 0
# 13 2000 2005 B A 60 2000 B 90 2 1 2005 A 55 3 2
# 14 2000 2005 C A 10 2000 C 100 2 0 2005 A 55 3 2
fig,ax = plt.subplots()
rescale_by = 1./600 # trial and error
# draw edges first
for _,edge in edges.iterrows():
x,y = edge[['x_start','y_start']]
dx,dy = edge[['x_stop','y_stop']].values - edge[['x_start','y_start']].values
ax.add_patch(FancyArrow(x,y,dx,dy,width=rescale_by*edge['EdgeWidth'],length_includes_head=True,color='orange'))
# draw nodes second such that they are plotted on top of edges
for _,node in nodes.iterrows():
ax.add_patch(Circle(node[['x','y']],rescale_by*node['NodeSize'],facecolor='w',edgecolor='k'))
ax.text(node['x'],node['y'],node['NodeSize'],ha='center',va='center')
# annotate nodes
for _,node in nodes[['NodeName','y']].drop_duplicates().iterrows():
ax.text(-0.5,node['NodeName'],fontsize=15,fontweight='bold',va='center')
for _,node in nodes[['Year','x']].drop_duplicates().iterrows():
ax.text(node['x'],-0.5,node['Year'],va='center')
# adjust axis limits to include labels
ax.autoscale_view()
_,xmax = ax.get_xlim()
ax.set_xlim(-1,xmax)
# style axis
ax.set_aspect('equal')
ax.axis('off')
plt.show()