# figures, subplots, axes 和 ticks 对象 ## figures, axes 和 ticks 的关系 这些对象的关系可以用下面的图来表示: 示例图像: ![图1](./artists_figure.png) 具体结构: ![图2](./artists_tree.png) ## figure 对象 `figure` 对象是最外层的绘图单位,默认是以 `1` 开始编号(**MATLAB** 风格,`Figure 1, Figure 2, ...`),可以用 `plt.figure()` 产生一幅图像,除了默认参数外,可以指定的参数有: * `num` - 编号 * `figsize` - 图像大小 * `dpi` - 分辨率 * `facecolor` - 背景色 * `edgecolor` - 边界颜色 * `frameon` - 边框 这些属性也可以通过 `Figure` 对象的 `set_xxx` 方法来改变。 ## subplot 和 axes 对象 ### subplot `subplot` 主要是使用网格排列子图: In [1]: ```py %pylab inline subplot(2,1,1) xticks([]), yticks([]) text(0.5,0.5, 'subplot(2,1,1)',ha='center',va='center',size=24,alpha=.5) subplot(2,1,2) xticks([]), yticks([]) text(0.5,0.5, 'subplot(2,1,2)',ha='center',va='center',size=24,alpha=.5) show() ``` ```py Populating the interactive namespace from numpy and matplotlib ``` ![](data:image/png;base64,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) 更高级的可以用 `gridspec` 来绘图: In [2]: ```py import matplotlib.gridspec as gridspec G = gridspec.GridSpec(3, 3) axes_1 = subplot(G[0, :]) xticks([]), yticks([]) text(0.5,0.5, 'Axes 1',ha='center',va='center',size=24,alpha=.5) axes_2 = subplot(G[1,:-1]) xticks([]), yticks([]) text(0.5,0.5, 'Axes 2',ha='center',va='center',size=24,alpha=.5) axes_3 = subplot(G[1:, -1]) xticks([]), yticks([]) text(0.5,0.5, 'Axes 3',ha='center',va='center',size=24,alpha=.5) axes_4 = subplot(G[-1,0]) xticks([]), yticks([]) text(0.5,0.5, 'Axes 4',ha='center',va='center',size=24,alpha=.5) axes_5 = subplot(G[-1,-2]) xticks([]), yticks([]) text(0.5,0.5, 'Axes 5',ha='center',va='center',size=24,alpha=.5) show() ``` 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) ## axes 对象 `subplot` 返回的是 `Axes` 对象,但是 `Axes` 对象相对于 `subplot` 返回的对象来说要更自由一点。`Axes` 对象可以放置在图像中的任意位置: In [3]: ```py axes([0.1,0.1,.8,.8]) xticks([]), yticks([]) text(0.6,0.6, 'axes([0.1,0.1,.8,.8])',ha='center',va='center',size=20,alpha=.5) axes([0.2,0.2,.3,.3]) xticks([]), yticks([]) text(0.5,0.5, 'axes([0.2,0.2,.3,.3])',ha='center',va='center',size=16,alpha=.5) show() ``` ![](data:image/png;base64,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) In [4]: ```py axes([0.1,0.1,.5,.5]) xticks([]), yticks([]) text(0.1,0.1, 'axes([0.1,0.1,.8,.8])',ha='left',va='center',size=16,alpha=.5) axes([0.2,0.2,.5,.5]) xticks([]), yticks([]) text(0.1,0.1, 'axes([0.2,0.2,.5,.5])',ha='left',va='center',size=16,alpha=.5) axes([0.3,0.3,.5,.5]) xticks([]), yticks([]) text(0.1,0.1, 'axes([0.3,0.3,.5,.5])',ha='left',va='center',size=16,alpha=.5) axes([0.4,0.4,.5,.5]) xticks([]), yticks([]) text(0.1,0.1, 'axes([0.4,0.4,.5,.5])',ha='left',va='center',size=16,alpha=.5) show() ``` ![](data:image/png;base64,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) 后面的 `Axes` 对象会覆盖前面的内容。 ## ticks 对象 ticks 用来注释轴的内容,我们可以通过控制它的属性来决定在哪里显示轴、轴的内容是什么等等。