这学期即上《python》，又上《线性代数》课，都是“好课”，可按排的学时都不多。

用python语言编写了一个示例，调用networkx和matplotlib绘制结点关系图。

简单整理一下《线性代数》课程中的概念，看图可引导学生复习，回顾概念之间的关系。

（结点间联系其实很多，只画主要部分）

完整代码如下：

import networkx as nx

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimHei'] 

L=["矩阵","方阵","逆矩阵","伴随矩阵","行列式","特征值","特征向量","二次型","秩",

    "维","向量","范数","向量组","解集","增广矩阵","行最简形","基","余子阵",

    "线性方程组","线性组合","线性表示","线性相关","线性关系","线性空间",

    "线性算子","行空间","列空间","零空间","正交","转置","内积","初等变换",

    "线性运算","线性映射","线性变换","对换","数乘","倍加"]

E=[(12,0),(10,12),(0, 1),(1,4),(1,5),(1,6),(5,6),(12, 23),(10,23),(30,10)

   ,(10,28),(11,10),(1,2),(1,3),(1,7),(1,17),(18,14),(15,14),(15,31)

   ,(15,8),(14,31),(31,35),(31,36),(31,37),(18,20),(20,21),(12,21),(19,12)

   ,(13,8),(18,13),(23,34),(23,12),(0,8),(33,34),(34,22),(34,32),(0,24)

   ,(0,25),(0,26),(18,34),(0,27),(13,27),(23,16),(23,9),(0,29)]

G = nx.Graph()

G.add_nodes_from(L)

G.add_edges_from([(L[i[0]],L[i[1]]) for i in E])

nx.draw(G,with_labels=True, node_size=600,edge_color='black', font_size=18,

     node_color=['skyblue' if i<28 else 'lightcyan' for i in range(len(L))])

plt.show()

#draw_circular

python实现了很多常用数据结构，尤其是列表推导式、字典等，处理数据很方便。