在边界框中绘制 networkx 图
Plot a networkx graph in a bounding box
我想在边界为 (100, 100) 的二维网格中生成 NetworkX 图，即
x 轴范围从 (0,100) 和 y 轴范围从 (0,100).
import matplotlib.pyplot as plt
import networkx as nx
H = nx.gnm_random_graph(n=8, m=9, seed=5) # generate a random graph
pos = nx.spring_layout(H, iterations=500) # find good positions for nodes
nx.draw(H)
print(pos)
plt.show()
我想知道如何缩放存储在pos中的节点坐标
这样所有节点都位于边界内。
一种方法是使用 nx.spring_layout
的 scale 和 center 参数
pos = nx.spring_layout(H, iterations=500, scale=50, center=(50, 50)) # find good positions for nodes
# verify positions are withing boundaries
positions_array = np.array(list(pos.values()))
print(((0 <= positions_array) & (positions_array <= 100)).all())
输出
True
相应的 pos 结果（对于一个 运行）是：
{0: array([69.42667319, 77.61143687]), 1: array([81.63688581, 10.64742778]), 2: array([56.9561371, 0. ]), 3: array([71.3536673 , 41.34120577]), 4: array([16.61575096, 54.76920418]), 5: array([17.06331411, 87.49081365]), 6: array([41.68768891, 28.49815199]), 7: array([45.25988262, 99.64175977])}
我想在边界为 (100, 100) 的二维网格中生成 NetworkX 图，即 x 轴范围从 (0,100) 和 y 轴范围从 (0,100).
import matplotlib.pyplot as plt
import networkx as nx
H = nx.gnm_random_graph(n=8, m=9, seed=5) # generate a random graph
pos = nx.spring_layout(H, iterations=500) # find good positions for nodes
nx.draw(H)
print(pos)
plt.show()
我想知道如何缩放存储在pos中的节点坐标
这样所有节点都位于边界内。
一种方法是使用 nx.spring_layout
scale 和 center 参数
pos = nx.spring_layout(H, iterations=500, scale=50, center=(50, 50)) # find good positions for nodes
# verify positions are withing boundaries
positions_array = np.array(list(pos.values()))
print(((0 <= positions_array) & (positions_array <= 100)).all())
输出
True
相应的 pos 结果（对于一个 运行）是：
{0: array([69.42667319, 77.61143687]), 1: array([81.63688581, 10.64742778]), 2: array([56.9561371, 0. ]), 3: array([71.3536673 , 41.34120577]), 4: array([16.61575096, 54.76920418]), 5: array([17.06331411, 87.49081365]), 6: array([41.68768891, 28.49815199]), 7: array([45.25988262, 99.64175977])}