宜城龙山 发表于 2022-11-29 11:20

【行空板 Python编程学习主控板评测三】Tonny环境下的行空板在数学函数图像的应用开发

<div class='showpostmsg'> 本帖最后由 宜城龙山 于 2022-12-1 00:54 编辑

<p>【行空板 Python编程学习主控板评测三】Tonny环境下的行空板在数学函数图像的应用开发</p>

<p>&nbsp;</p>

<p>行空板可运行Mind+、Jupyter编程、Thonny等多种编程环境,Thonny是一款轻量级Python编辑器,使用简单容易上手,可以通过内置的ssh remote功能直接连接行空板编程。下面主要介绍Thonny编程环境下利用matplotlib库开展行空板在数学函数图像绘制方面的应用开发。</p>

<p><span style="font-size:20px;"><span style="font-family:STHeiti;"><strong>一、准备</strong></span></span></p>

<p>1、安装</p>

<p>下载安装完成后,运行。界面如下:</p>

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" /></p>

<p>图1 Thonny编译器环境</p>

<p>2、配置</p>

<p>在Thonny环境下使用行空板,需作以下配置工作:</p>

<p>选择菜单中的选择解释器;选择②Remote Python 3(SSH);;③输入行空板的Host(IP),username(用户名),见图2所示。点击确认进入下一步。</p>

<p>使用usb线连接行空板与电脑时,默认IP是10.1.2.3,默认用户名是root,默认密码是dfrobot</p>

<p>如果板子正常连接则会提示输入密码,输入默认密码dfrobot,勾选Save password(记住密码),见图3。</p>

<p>如果提示Can&#39;t connect to &#39;10.1.2.3&#39; with user &#39;root&#39;: 由于连接方在一段时间后没有正确答复或连接的主机没有反应,连接尝试失败。那么检查一下板子是否正常连接电脑或者查看本文档<a href="https://wiki.unihiker.com/FAQ">常见问题</a>中无法连接的解决方法。</p>

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" /></p>

<p style="text-align: center;">图2 行空板连接设置示意图</p>

<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMAAAADwCAYAAABWpmuqAAAVoUlEQVR4nO2dX2hb2Z3HP1r6MvvQ
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SUVORK5CYII=
" /></p>

<p>图3 密码输入窗口</p>

<p>连接成功之后,将&ldquo;视图&rdquo;菜单中的&ldquo;Shell&rdquo;和&ldquo;文件&rdquo;勾选,板子中的文件即显示在左侧文件视图中,方便编写程序和查看行空板中的文件。如图4所示。</p>

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" /></p>

<p style="text-align: center;">图4 行空板连接成功后的界面</p>

<p>3、安装第三方库</p>

<p>matplotlib 是python最著名的绘图库,它提供了一整套和matlab相似的命令API,十分适合交互式地行制图。而且也可以方便地将它作为绘图控件,嵌入GUI应用程序中。是Python中类似MATLAB的绘图工具,熟悉 MATLAB也可以很快的上手 Matplotlib。</p>

<p>Matplotlib不是Thonny的默认安装包,需要自己动手安装,具体步骤如下:</p>

<p>在Thonny下选择菜单&ldquo;工具&rdquo;-&gt;&ldquo;管理包&rdquo;,管理包可对行空板的python包进行管理(安装、卸载、切换版本)。在搜索栏输入matplotlib ,点击Search on PyPI,开始搜索,显示搜索结果,见图5,点击matplotlib开始安装,安装完毕,在左边栏目中显示matplotlib,见图6,说明安装成功,便可使用。</p>

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" /></p>

<p style="text-align: center;">图5 Thonny管理包安装界面</p>

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" /></p>

<p style="text-align: center;">图6 matplotlib库安装成功界面</p>

<p><strong><span style="font-size:20px;"><span style="font-family:STHeiti;">二、Matplotlib功能简介</span></span></strong></p>

<p>Matplotlib是一个python绘图库,它以硬拷贝格式和跨平台的交互式环境生成出版物质量的图形。利用它可以以非常少的代码量方便地绘制出我们常用的一些图表。</p>

<p>Matplotlib可以绘制折线图、散点图、柱状图等几十种图表,满足科学研究的需要,是数据分析、可视化的不二之选。</p>

<p>1、Matplotlib总览</p>

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" /></p>

<p>Matplotlib能够绘制多达几十种的图表,但我们最常用的图表就是上述七种:折线图、散点图、条形图、直方图、饼图、三维图、箱体图;而且绝大多数人所用都超出不了这七种。详细可参考<a href="https://www.osgeo.cn/matplotlib/users/prev_whats_new/whats_new_1.5.html">Matplotlib 1.5中的新功能 &mdash; Matplotlib 3.3.3 文档 (osgeo.cn)</a></p>

<p>2、常用图表</p>

<table style="table-layout: auto">
        <tbody>
                <tr>
                        <td>
                        <p>图表类型</p>
                        </td>
                        <td>
                        <p>绘制代码</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>折线图</p>
                        </td>
                        <td>
                        <p>plt.plot(x,y)</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>散点图</p>
                        </td>
                        <td>
                        <p>plt.scatter(x,y)</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>条形图</p>
                        </td>
                        <td>
                        <p>plt.bar(x,y)、plt.barh(x,y)</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>直方图</p>
                        </td>
                        <td>
                        <p>plt.hist(data,group)</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>饼图</p>
                        </td>
                        <td>
                        <p>plt.pie(x,y)</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>三维线图</p>
                        </td>
                        <td>
                        <p>ax=plt.axes(projection=&#39;3d&#39;)、ax.plot3D(x,y,z))</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>三维点图</p>
                        </td>
                        <td>
                        <p>ax=plt.axes(projection=&#39;3d&#39;)、ax.scatter3D(x,y,z))</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>三维曲面图</p>
                        </td>
                        <td>
                        <p>ax=plt.axes(projection=&#39;3d&#39;)、ax.plot_surface(x,y,z)</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>箱体图</p>
                        </td>
                        <td>
                        <p>plt.boxplot(data,label)</p>
                        </td>
                </tr>
        </tbody>
</table>

<p>&nbsp;</p>

<table style="table-layout: auto">
        <tbody>
                <tr>
                        <td>
                        <p>图表属性</p>
                        </td>
                        <td>
                        <p>代码实现</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>标题</p>
                        </td>
                        <td>
                        <p>plt.title( )</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>坐标轴</p>
                        </td>
                        <td>
                        <p>plt.xlabel( ),plt.ylabel( )</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>刻度</p>
                        </td>
                        <td>
                        <p>plt.xticks( )、plt.yticks( )</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>网格</p>
                        </td>
                        <td>
                        <p>plt.grid( )</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>图列</p>
                        </td>
                        <td>
                        <p>plt.plot(x,y,label=&#39;str&#39;)、plt.legend( )</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>图表</p>
                        </td>
                        <td>
                        <p>plt.show( )</p>
                        </td>
                </tr>
                <tr>
                        <td>
                        <p>图形数据</p>
                        </td>
                        <td>
                        <p>plt.text( )</p>
                        </td>
                </tr>
        </tbody>
</table>

<p>&nbsp;</p>

<p><strong><span style="font-size:20px;"><span style="font-family:宋体;">三、实例</span></span></strong></p>

<p>1、饼图</p>

<p>import matplotlib.pyplot as plt</p>

<p>import seaborn as sns</p>

<p>sns.set_style({&#39;font.sans-serif&#39;:[&#39;simhei&#39;,&#39;Arial&#39;]})</p>

<p>labels = [&#39;大型&#39;,&#39;中型&#39;,&#39;小型&#39;, &#39;微型&#39;] #定义标签</p>

<p>sizes = #每块值</p>

<p>colors = [&#39;red&#39;,&#39;yellowgreen&#39;,&#39;lightskyblue&#39;,&#39;yellow&#39;] #每块颜色定义</p>

<p>explode = (0, 0, 0.02, 0) #将某一块分割出来,值越大分割出的间隙越大</p>

<p>patches,text1,text2 = plt.pie(sizes,</p>

<p>explode=explode,</p>

<p>labels=labels,</p>

<p>colors=colors,</p>

<p>labeldistance = 1.2,#图例距圆心半径倍距离</p>

<p>autopct = &#39;%3.2f%%&#39;, #数值保留固定小数位</p>

<p>shadow = False, #无阴影设置</p>

<p>startangle =90, #逆时针起始角度设置</p>

<p>pctdistance = 0.6) #数值距圆心半径倍数距离</p>

<p># x,y轴刻度设置一致,保证饼图为圆形</p>

<p>plt.axis(&#39;equal&#39;)</p>

<p>plt.legend()</p>

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" /></p>

<p>行空板显示的字体不正确。</p>

<p>2、正弦余弦曲线</p>

<p>函数为:y=sin(x)和y=cos(x)</p>

<pre>
<code class="language-python">import numpy as np

import matplotlib.pyplot as mp

#从-pi到pi拆1000个点
x = np.linspace(-np.pi, np.pi, 1000)
sinx = np.sin(x)
# 画一个y = cos(x)/2
cosx = np.cos(x) / 2
# 修改x轴的刻度
vals = [-np.pi, -np.pi/2, 0, np.pi/2, np.pi]
texts = [r'$-\pi$', r'$-\frac{\pi}{2}$', '0',
r'$\frac{\pi}{2}$', r'$\pi$']
mp.xticks(vals, texts) #设置x轴坐标刻度
# 设置坐标轴
ax = mp.gca()
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['left'].set_position(('data', 0))
ax.spines['bottom'].set_position(('data', 0))
mp.yticks([-1.0, -0.5, 0.5, 1.0]) #设置y轴刻度
#设置绘制的两条线的属性
mp.plot(x, sinx, linestyle='-.', linewidth=2,
color='dodgerblue', alpha=0.8,
label=r'$y = sin(x)$')
mp.plot(x, cosx, linestyle='--', linewidth=2,
color='orangered', alpha=0.8,
label=r'$y = \frac{1}{2}cos(x)$')
# 绘制特殊点
mp.scatter(, ,
s=, marker='o', edgecolor='red',
facecolor='green', zorder=3) #zorder是图层顺序
# 为特殊点添加备注
mp.annotate(r'$[\frac{\pi}{2}, 1]$',
xycoords='data', xy=(np.pi/2, 1),
textcoords='offset points', xytext=(20, 30),
fontsize=14, arrowprops=dict(
arrowstyle='-&gt;',
connectionstyle='angle3'))
mp.legend() #显示图例
mp.show()</code></pre>

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" /></p>

<p>3、反比例函数</p>

<p>函数为:y=10/x</p>

<p>import matplotlib.pyplot as plt</p>

<p>import numpy as np</p>

<p>x = np.linspace(-11, 11, 110)</p>

<p>print(x)</p>

<p>y =10/x</p>

<p>plt.plot(x, y)</p>

<p>ax = plt.gca()</p>

<p>ax.spines[&#39;bottom&#39;].set_position((&#39;data&#39;,0))</p>

<p>ax.spines[&#39;left&#39;].set_position((&#39;data&#39;,0))</p>

<p>plt.show()</p>

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" /></p>

<p>4、阿基米德螺线</p>

<p>import numpy as np</p>

<p>import matplotlib.pyplot as plt</p>

<p>#推荐b=2</p>

<p>b = 2.0</p>

<p>n = 4</p>

<p>#几条螺旋线</p>

<p>#2&pi;代表一圈</p>

<p>theta = np.linspace(0, 2 * np.pi, num=100)</p>

<p>plt.subplot(111, projection=&#39;polar&#39;)</p>

<p>y = b * theta</p>

<p>for i in range(n):</p>

<p>x = theta + 2 * np.pi * (i / n)</p>

<p>plt.plot(x, y)</p>

<p>plt.show()</p>

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" /></p>

<p>5、等高线</p>

<pre>
<code class="language-python">import numpy as np
import matplotlib.pyplot as mp
n = 1000
# 生成网格化坐标矩阵
x, y = np.meshgrid(np.linspace(-3, 3, n),np.linspace(-3, 3, n))
# 根据每个网格点坐标,通过某个公式计算z高度坐标
z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y**2)
mp.figure('Contour', facecolor='lightgray')
mp.title('Contour', fontsize=20)
mp.xlabel('x', fontsize=14)
mp.ylabel('y', fontsize=14)
mp.tick_params(labelsize=10)
mp.grid(linestyle=':')
# 绘制等高线图
mp.contourf(x, y, z, 8, cmap='jet')
# 显示颜色效果
cntr = mp.contour(x, y, z, 8, colors='black', linewidths=0.5)
# 显示等高线
# 为等高线图添加高度标签
mp.clabel(cntr, inline_spacing=1, fmt='%.1f', fontsize=10) # inline_spacing参数是数字与等高线之间的间隙
mp.show()</code></pre>

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" /></p>

<p><span style="font-size:20px;"><strong><span style="font-family:宋体;">四、总结</span></strong></span></p>

<p>Python的 Matplotlib库具有超强的绘图功能,这里近列举了几个在行空板上运行的应用实例,整体感觉良好,但运行速度不快,显示区域有限。作为青少年学习之用还是不错的。</p>
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