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<p><span style="font-size:16px;"><strong>（1） np.mashgrid()<span style="font-family:'宋体';">函数：</span></strong></span><span style="font-family:Calibri;">-----</span><span style="font-family:'宋体';">生成网络点坐标矩阵，可以是二维网络矩阵，也可以是三维网络矩阵。其中，每个交叉点就是</span><strong>网络点</strong>，描述这些网络点的矩阵就是<strong>坐标矩阵</strong><span style="font-family:'宋体';">（横坐标矩阵</span>X<span style="font-family:'宋体';">中的每个元素与纵坐标矩阵</span><span style="font-family:Calibri;">Y</span><span style="font-family:'宋体';">中对应位置元素，共同构成一个点的完整坐标）。</span></p>
<p>背景示例：网络点与坐标矩阵的解释如下：</p>
<p>import numpy as np</p>
<p>import matplotlib.pyplot as plt</p>
<p>x=np.array([[0,1,2],[0,1,2]]) #<span style="font-family:'宋体';">最简单的方法是，可以把横纵坐标矩阵</span><span style="font-family:Calibri;">X,Y</span><span style="font-family:'宋体';">写出来，生成坐标点</span></p>
<p>y=np.array([[0,0,0],[1,1,1]])</p>
<p>plt.plot(x,y,color='red',marker='.',markersize=10,linestyle='-.') #<span style="font-family:'宋体';">点的形状为原点，点设置大一些，线性为电划线。</span></p>
<p>plt.grid(True)</p>
<p>plt.show() </p>
<p><span style="font-family:'宋体';">注意：按照矩阵给坐标点信息，</span>matplotlib<span style="font-family:'宋体';">会把横坐标矩阵中，每一列的点当做同一条线。正如上例中把</span><span style="font-family:Calibri;">plot</span><span style="font-family:'宋体';">的</span><span style="font-family:Calibri;">linestyle=</span>’ ‘<span style="font-family:'宋体';">改为</span>linestyle=’-.’<span style="font-family:'宋体';">就会发现</span>A-D,B-E,C-F<span style="font-family:'宋体';">是连接的。</span></p>
<p><span style="font-family:'宋体';"> <img alt="" src="https://beijingoptbbs.oss-cn-beijing.aliyuncs.com/cs/5606289-fe9832a069dfeae5a358ddd3ef0ce2fe.png"><img alt="" src="https://beijingoptbbs.oss-cn-beijing.aliyuncs.com/cs/5606289-5387e668afc9bde4fe872c9d118bf9b3.png"></span></p>
<p align="justify"><span style="color:#ff0000;font-size:16px;"><strong><span style="font-family:'宋体';">对于很多网络点的情况，可用如下</span>meshgrid()<span style="font-family:'宋体';">函数方法：</span></strong></span></p>
<p align="justify"> <span style="font-family:'宋体';">因注意到坐标矩阵其中有大量的重复</span>---X<span style="font-family:'宋体';">的每一行都一样，</span><span style="font-family:Calibri;">Y</span><span style="font-family:'宋体';">的每一列都一样。故基于此规律性，</span><span style="font-family:Calibri;">numpy</span><span style="font-family:'宋体';">提供的</span><span style="font-family:Calibri;">np.meshgrid()</span><span style="font-family:'宋体';">函数可以快速生成坐标矩阵</span><span style="font-family:Calibri;">X,Y</span></p>
<p align="justify">x=np.linspace(-0.5,2.,10) </p>
<p align="justify">y=np.linspace(-1.5,4.,10)</p>
<p align="justify">X,Y=np.meshgrid(x,y) #<span style="font-family:'宋体';">输入的</span><span style="font-family:Calibri;">x,y</span><span style="font-family:'宋体';">是网络点的横纵坐标</span><strong>列向量（非矩阵）</strong><span style="font-family:'宋体';">，输出的</span>X<span style="font-family:'宋体';">，</span><span style="font-family:Calibri;">Y</span><span style="font-family:'宋体';">就是坐标矩阵。</span></p>
<p align="justify">plt.plot(X,Y,color='limegreen',marker='.',linestyle='')</p>
<p align="justify">plt.grid(True)</p>
<p align="justify">plt.show()</p>
<p align="justify"> <img alt="" src="https://beijingoptbbs.oss-cn-beijing.aliyuncs.com/cs/5606289-b988f30e2be5b47e83f95eae3211996b.png"></p>
<p align="justify"><span style="color:#ff0000;font-size:16px;"><strong>（2） Python<span style="font-family:'宋体';">可视化库</span><span style="font-family:Calibri;">matplotlib.pyplot</span><span style="font-family:'宋体';">里</span><span style="font-family:Calibri;">contour()</span><span style="font-family:'宋体';">与</span><span style="font-family:Calibri;">contourf()</span><span style="font-family:'宋体';">函数</span></strong></span></p>
<p align="justify"><span style="font-family:'宋体';"><strong>区别</strong>：</span>contour()<span style="font-family:'宋体';">和</span><span style="font-family:Calibri;">counterf() </span><span style="font-family:'宋体';">函数功能相同，都是画三维等高线图的，不同点在于</span><s |
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