# 使用 style 来配置 pyplot 风格 In [1]: ```py import matplotlib.pyplot as plt import numpy as np %matplotlib inline ``` `style` 是 `pyplot` 的一个子模块,方便进行风格转换, `pyplot` 有很多的预设风格,可以使用 `plt.style.available` 来查看: In [2]: ```py plt.style.available ``` Out[2]: ```py [u'dark_background', u'bmh', u'grayscale', u'ggplot', u'fivethirtyeight'] ``` In [3]: ```py x = np.linspace(0, 2 * np.pi) y = np.sin(x) plt.plot(x, y) plt.show() ``` 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) 例如,我们可以模仿 `R` 语言中常用的 `ggplot` 风格: In [4]: ```py plt.style.use('ggplot') plt.plot(x, y) plt.show() ``` ![](data:image/png;base64,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) 有时候,我们不希望改变全局的风格,只是想暂时改变一下分隔,则可以使用 `context` 将风格改变限制在某一个代码块内: In [5]: ```py with plt.style.context(('dark_background')): plt.plot(x, y, 'r-o') plt.show() ``` 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) 在代码块外绘图则仍然是全局的风格。 In [6]: ```py with plt.style.context(('dark_background')): pass plt.plot(x, y, 'r-o') plt.show() ``` ![](data:image/png;base64,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) 还可以混搭使用多种风格,不过最右边的一种风格会将最左边的覆盖: In [7]: ```py plt.style.use(['dark_background', 'ggplot']) plt.plot(x, y, 'r-o') plt.show() ``` ![](data:image/png;base64,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) 事实上,我们还可以自定义风格文件。 自定义文件需要放在 `matplotlib` 的配置文件夹 `mpl_configdir` 的子文件夹 `mpl_configdir/stylelib/` 下,以 `.mplstyle` 结尾。 `mpl_configdir` 的位置可以这样查看: In [8]: ```py import matplotlib matplotlib.get_configdir() ``` Out[8]: ```py u'c:/Users/Jin\\.matplotlib' ``` 里面的内容以 `属性:值` 的形式保存: ```py axes.titlesize : 24 axes.labelsize : 20 lines.linewidth : 3 lines.markersize : 10 xtick.labelsize : 16 ytick.labelsize : 16 ``` 假设我们将其保存为 `mpl_configdir/stylelib/presentation.mplstyle`,那么使用这个风格的时候只需要调用: ```py plt.style.use('presentation') ```