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<link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/hint.css/2.4.1/hint.min.css"><p>matplotlib的一些使用整理笔记，详细使用参见<a href="https://matplotlib.org/" target="_blank" rel="noopener">官网</a>。<br><a id="more"></a></p>
<h2 id="交互绘图"><a href="#交互绘图" class="headerlink" title="交互绘图"></a>交互绘图</h2><p>matplotlib有交互模式，可以通过交互模式实现动态绘图。主要通过循环<strong>绘图==>清除上一幅图片==>绘图</strong>，来实现动态图表。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"></span><br><span class="line"><span class="comment"># plt.ion() 打开交互模式</span></span><br><span class="line"><span class="comment"># plt.ioff() 关闭交互模式</span></span><br><span class="line"><span class="comment"># plt.clf() 清除图像(figure)</span></span><br><span class="line"><span class="comment"># plt.cla() 清除Axes</span></span><br><span class="line"><span class="comment"># plt.pause() 交互模式下暂停并展示图表</span></span><br><span class="line"></span><br><span class="line">X, Y = np.random.rand(<span class="number">2</span>, <span class="number">10</span>)</span><br><span class="line">x, y = [], []</span><br><span class="line">plt.ion()</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> range(len(X)):</span><br><span class="line"> plt.clf() <span class="comment"># 不删除图表一样可以运行，但之前图表会一直占用内存</span></span><br><span class="line"> x.append(X[i])</span><br><span class="line"> y.append(Y[i])</span><br><span class="line"> plt.scatter(x, y)</span><br><span class="line"> plt.pause(<span class="number">0.2</span>)</span><br><span class="line">plt.ioff()</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure></p>
<h2 id="全局变量"><a href="#全局变量" class="headerlink" title="全局变量"></a>全局变量</h2><p>matplotlib提供了变更全局变量的方式。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> matplotlib <span class="keyword">as</span> mpl</span><br><span class="line"><span class="comment"># 方式一</span></span><br><span class="line">mpl.rcParams[<span class="string">'lines.linewidth'</span>] = <span class="number">2</span></span><br><span class="line">mpl.rcParams[<span class="string">'lines.color'</span>] = <span class="string">'r'</span></span><br><span class="line"><span class="comment"># 方式二</span></span><br><span class="line">mpl.rc(<span class="string">'lines'</span>, linewidth=<span class="number">2</span>, color=<span class="string">'r'</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 中文乱码指定字体</span></span><br><span class="line">plt.rcParams[<span class="string">'font.sans-serif'</span>] = [<span class="string">'SimHei'</span>]</span><br><span class="line"><span class="comment"># 正常显示负号</span></span><br><span class="line">plt.rcParams[<span class="string">'axes.unicode_minus'</span>] = <span class="keyword">False</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 如果不想每次都指定这些内容，可以将这些内容直接写入到默认配置文件matplotlibrc</span></span><br><span class="line">mpl.matplotlib_fname() <span class="comment"># 配置文件路径</span></span><br></pre></td></tr></table></figure></p>
<h2 id="风格"><a href="#风格" class="headerlink" title="风格"></a>风格</h2><p>matplotlib提供了不少的风格选项，可以自己指定，或者使用默认风格。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 输出可用风格</span></span><br><span class="line">print(plt.style.availabel)</span><br><span class="line"><span class="comment"># 指定全局风格</span></span><br><span class="line">plt.style.use(<span class="string">'ggplot'</span>)</span><br><span class="line"><span class="comment"># 只想临时使用某种风格，而不改变全局风格</span></span><br><span class="line"><span class="keyword">with</span> plt.style.context(<span class="string">'ggplot'</span>):</span><br><span class="line"> plt.plot(np.sin(np.linspace(<span class="number">0</span>, <span class="number">2</span> * np.pi)), <span class="string">'r-o'</span>)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure></p>
<h2 id="注释"><a href="#注释" class="headerlink" title="注释"></a>注释</h2><p>在图表中加入箭头类型的注释文本，具体使用参见<a href="https://matplotlib.org/tutorials/text/annotations.html#annotations-tutorial" target="_blank" rel="noopener">网址</a><br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">plt.annotate(text, xy, xytext, arrowprops)</span><br></pre></td></tr></table></figure></p>
<h2 id="坐标轴"><a href="#坐标轴" class="headerlink" title="坐标轴"></a>坐标轴</h2><p>坐标轴设定很多，这里就写一些常用的。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 坐标轴标签</span></span><br><span class="line">plt.xlabel(<span class="string">"X"</span>,fontsize=<span class="number">10</span>,fontweight=<span class="string">'bold'</span>)</span><br><span class="line"><span class="comment"># 坐标轴范围</span></span><br><span class="line">plt.axis(xlim=(xmin, xmax), ylim=(ymin, ymax))</span><br><span class="line">plt.axis([xmin, xmax, ymin, ymax])</span><br><span class="line">plt.xlim(xmin, xmax)</span><br><span class="line"><span class="comment"># 不显示坐标轴</span></span><br><span class="line">plt.axis(<span class="string">'off'</span>)</span><br><span class="line"><span class="comment"># 横纵坐标轴刻度相等</span></span><br><span class="line">plt.axis(<span class="string">'equal'</span>)</span><br><span class="line"><span class="comment"># 坐标轴刻度标签</span></span><br><span class="line">plt.xticks([<span class="number">0</span>,<span class="number">1</span>,<span class="number">2</span>,<span class="number">3</span>])</span><br></pre></td></tr></table></figure></p>
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