NetworkX节点标记相对位置

时间:2017-05-10 14:11:01

标签: python matplotlib graph networkx

我正在努力解决以下问题。我想绘制一个大约100个节点的圆形图,我必须根据之前的分类手动定位它们。这些节点有一个指定的标签,用不同的文本长度描述它们,我想把这个标签放在节点附近。下图是我想要获得的(我绘制蓝色圆圈只是为了表明标签在外围完美对齐): https://i.stack.imgur.com/Qre0Z.png

到目前为止,我只是画了这个:https://i.stack.imgur.com/U7bZG.png

这是MWE:

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

n = 7
G = nx.complete_graph(n)
node_list = sorted(G.nodes())
angle = []
angle_dict = {}
for i, node in zip(xrange(n),node_list):
    theta = 2.0*np.pi*i/n
    angle.append((np.cos(theta),np.sin(theta)))
    angle_dict[node] = theta
pos = {}
for node_i, node in enumerate(node_list):
    pos[node] = angle[node_i]

labels = {0:'zero',1:'oneone',2:'twotwo',3:'threethreethree',4:'fourfourfourfour',5:'fivefivefivefivefive',6:'sixsixsixsixsixsix'}

# figsize is intentionally set small to condense the graph
f = plt.figure(figsize=(2,2))
r = f.canvas.get_renderer()
plt.axis('equal')
nx.draw(G,pos=pos,with_labels=True)
description = nx.draw_networkx_labels(G,pos,labels=labels)
for node, t in description.items():
    t.set_rotation(angle_dict[node]*360.0/(2.0*np.pi))
plt.show()

我想我必须添加和玩

x, y = t.get_position()
bb = t.get_window_extent(renderer=r)
radius = 1.0+2.0*bb.width/r.width
t.set_position((radius*x,radius*y))

在我设置标签旋转的循环中。但是,我不知道如何正确设置它以及如何避免裁剪画布。

1 个答案:

答案 0 :(得分:5)

为了显示轴外的标签,您需要使轴与图相比较小,例如:通过在轴周围引入大的余量。您还需要设置文本的剪辑状态,以使其不被轴切断。

根据边界框的宽度定位标签需要首先将显示坐标系的边界框转换为数据坐标。

完整的解决方案:

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

n = 7
G = nx.complete_graph(n)
node_list = sorted(G.nodes())
angle = []
angle_dict = {}
for i, node in zip(xrange(n),node_list):
    theta = 2.0*np.pi*i/n
    angle.append((np.cos(theta),np.sin(theta)))
    angle_dict[node] = theta
pos = {}
for node_i, node in enumerate(node_list):
    pos[node] = angle[node_i]

labels = {0:'zero',1:'oneone',2:'twotwo',3:'threethreethree',4:'fourfourfourfour',5:'fivefivefivefivefive',6:'sixsixsixsixsixsix'}

# figsize is intentionally set small to condense the graph
fig, ax = plt.subplots(figsize=(5,5))
margin=0.33
fig.subplots_adjust(margin, margin, 1.-margin, 1.-margin)
ax.axis('equal')

nx.draw(G,pos=pos,with_labels=True, ax=ax)
description = nx.draw_networkx_labels(G,pos,labels=labels)

r = fig.canvas.get_renderer()
trans = plt.gca().transData.inverted()
for node, t in description.items():
    bb = t.get_window_extent(renderer=r)
    bbdata = bb.transformed(trans)
    radius = 1.2+bbdata.width/2.
    position = (radius*np.cos(angle_dict[node]),radius* np.sin(angle_dict[node]))
    t.set_position(position)
    t.set_rotation(angle_dict[node]*360.0/(2.0*np.pi))
    t.set_clip_on(False)

plt.show()

enter image description here