NetworkX系列教程(8)-DrawingGraph

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如果只是简单使用nx.draw,是无法定制出自己需要的graph,并且这样的graph内的点坐标的不定的,运行一次变一次,实际中一般是要求固定的位置,这就需要到布局的概念了.详细的画图信息可以看这里,代码中的关键部分使用了英文进行注释,不在另外注释.

注意: 如果代码出现找不库,请返回第一个教程,把库文件导入.

Drawing Graph

使用Matplotlib

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#定义graph
nodes=[0,1,2,3,4,5,'a','b','c']
edges=[(0,1),(0,5),(1,2),(1,4),(2,1),(2,4),('a','b'),('b','c'),('c','a')]
G=nx.Graph()
G.add_nodes_from(nodes)
G.add_edges_from(edges)

#使用spring_layout布局
pos=nx.spring_layout(G)

plt.subplots(2,4,figsize=(18,6))
plt.subplot(241)
plt.title('spring_layout')
nx.draw(G, with_labels=True, font_weight='bold') #Draw the graph G with Matplotlib.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(242)
plt.title('draw_networkx')
nx.draw_networkx(G) #Draw the graph G using Matplotlib.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(243)
plt.title('draw_networkx_nodes')
nx.draw_networkx_nodes(G,pos) #Draw the nodes of the graph G.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(244)
plt.title('draw_networkx_edges')
nx.draw_networkx_edges(G,pos) #Draw the edges of the graph G.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(245)
plt.title('draw_networkx_labels')
nx.draw_networkx_labels(G,pos) #Draw node labels on the graph G.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(246)
plt.title('draw_networkx_edge_labels')
nx.draw_networkx_edge_labels(G,pos) #Draw edge labels.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(247)
plt.title('draw_circular')
nx.draw_circular(G,) #Draw the graph G with a circular layout.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(248)
plt.title('draw_kamada_kawai')
nx.draw_kamada_kawai(G) #Draw the graph G with a Kamada-Kawai force-directed layout.
plt.axis('on')
plt.xticks([])
plt.yticks([])
plt.show()
plt.close()

plt.subplots(1,4,figsize=(18,3))
plt.subplot(141)
plt.title('draw_random')
nx.draw_random(G) #Draw the graph G with a random layout.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(142)
plt.title('draw_spectral')
nx.draw_spectral(G,) #Draw the graph G with a spectral layout.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(143)
plt.title('draw_spring')
nx.draw_spring(G) #Draw the graph G with a spring layout.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(144)
plt.title('draw_shell')
nx.draw_shell(G)  #Draw networkx graph with shell layout.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.show()

png

png

使用Graphviz AGraph (dot)

有些同学不知道如何安装Graphviz，我在这里作一个说明：
1.linux是安装graphviz即可，我使用的命令是：

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sudo apt install graphviz

2.Windows我没用实践过，不过我查到Graphviz有官网，里面有windows安装包，地址看下：
http://www.graphviz.org/download/

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G.clear()
from networkx.drawing.nx_pydot import write_dot,read_dot

plt.subplots(1,3,figsize=(15,5))
K5 = nx.complete_graph(5)

A = nx.nx_agraph.to_agraph(K5) #Return a pygraphviz graph from a NetworkX graph N.
G1 = nx.nx_agraph.from_agraph(A) #Return a NetworkX Graph or DiGraph from a PyGraphviz graph.
plt.subplot(131)
plt.title('原图',fontproperties=myfont)
nx.draw_random(G1) #Draw the graph G with a random layout.
plt.axis('on')
plt.xticks([])
plt.yticks([])

write_dot(G1, 'graph.test') #Write NetworkX graph G to Graphviz dot format on path.
G2=read_dot('graph.test') #Return a NetworkX graph from a dot file on path.
plt.subplot(132)
plt.title('保存原图后并读取',fontproperties=myfont)
nx.draw_random(G2) #Draw the graph G with a random layout.
plt.axis('on')
plt.xticks([])
plt.yticks([])

G3 = nx.petersen_graph()
pos = nx.nx_agraph.graphviz_layout(G3) #Create node positions for G using Graphviz.

plt.subplot(133)
plt.title('graphviz_layout',fontproperties=myfont)
nx.draw_random(G3) #Draw the graph G with a random layout.
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.show()

png

图布局

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#定义graph
nodes=[0,1,2,3,4,5,'a','b','c']
edges=[(0,1),(0,5),(1,2),(1,4),(2,1),(2,4),('a','b'),('b','c'),('c','a')]
G=nx.Graph()
G.add_nodes_from(nodes)
G.add_edges_from(edges)

plt.subplots(2,3,figsize=(18,6))
plt.subplot(231)
plt.title('circular_layout')
pos=nx.circular_layout(G) #Position nodes on a circle.
nx.draw(G,pos, with_labels=True, font_weight='bold')
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(232)
plt.title('kamada_kawai_layout')
pos=nx.kamada_kawai_layout(G) #Position nodes using Kamada-Kawai path-length cost-function.
nx.draw(G, pos,with_labels=True, font_weight='bold')
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(233)
plt.title('random_layout')
pos=nx.random_layout(G) #Position nodes uniformly at random in the unit square.
nx.draw(G, pos,with_labels=True, font_weight='bold')
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(234)
plt.title('shell_layout')
pos=nx.shell_layout(G) #Position nodes in concentric circles.
nx.draw(G, pos,with_labels=True, font_weight='bold')
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(235)
plt.title('spring_layout')
pos=nx.spring_layout(G)#Position nodes using Fruchterman-Reingold force-directed algorithm.
nx.draw(G, pos, with_labels=True, font_weight='bold')
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.subplot(236)
plt.title('spectral_layout')
pos=nx.spectral_layout(G) #Position nodes using the eigenvectors of the graph Laplacian.
nx.draw(G, pos, with_labels=True, font_weight='bold')
plt.axis('on')
plt.xticks([])
plt.yticks([])

plt.show()

png