布局功能,如nx.spring_layout
,返回一个字典,它的键是节点,其值是2元组(坐标)。这里的pos
字典可能看起来像一个例子:
In [101]: pos
Out[101]:
{(0, 0): array([ 0.70821816, 0.03766149]),
(0, 1): array([ 0.97041253, 0.30382541]),
(0, 2): array([ 0.99647583, 0.63049339]),
(0, 3): array([ 0.86691957, 0.86393669]),
(1, 0): array([ 0.79471631, 0.08748146]),
(1, 1): array([ 0.71731384, 0.35520076]),
(1, 2): array([ 0.69295087, 0.71089292]),
(1, 3): array([ 0.63927851, 1. ]),
(2, 0): array([ 0.42228877, 0. ]),
(2, 1): array([ 0.33250362, 0.3165331 ]),
(2, 2): array([ 0.31084694, 0.69246818]),
(2, 3): array([ 0.34141212, 0.9952164 ]),
(3, 0): array([ 0.16734454, 0.11357547]),
(3, 1): array([ 0.01560951, 0.33063389]),
(3, 2): array([ 0. , 0.63044189]),
(3, 3): array([ 0.12242227, 0.85656669])}
然后,您可以操纵这些坐标进一步,任何方式都可以。例如,由于在 和x
坐标y
通过spring_layout
返回是0和1之间,则可以 10倍层等级值添加到y
- 协调到节点分离成层:
for node in pos:
level = node // nodes_per_layer
pos[node] += (0,10*level)
import networkx as nx
import matplotlib.pyplot as plt
layers = 5
nodes_per_layer = 3
n = layers * nodes_per_layer
p = 0.2
G = nx.fast_gnp_random_graph(n, p, seed=2017, directed=True)
pos = nx.spring_layout(G, iterations=100)
for node in pos:
level = node // nodes_per_layer
pos[node] += (0,10*level)
nx.draw(G, pos, node_size=650, node_color='#ffaaaa', with_labels=True)
plt.show()
产生
你可以张贴的实例图像与所期望的结果? – edo