??在上一篇 python matplotlib入門（二） Matplotlib 作圖生命周期 中，其中一個重要環(huán)節(jié)是 自定義圖像（Customizing Matplotlib)，從某種角度來講，其實這幾乎包括了我們繪圖80%的工作，這篇博客就來探討如何DIY我們的圖像。

??rcParams

??可以在python腳本中動態(tài)更改默認的rc設置，或者從python shell以交互方式更改。所有rc設置都存儲在一個名為matplotlib.rcParams的類字典變量中，該變量對于matplotlib包是全局的, 其本質是從本地文件matplotlibrc讀取數(shù)據(jù) 。

import matplotlib as mpl# 修改方式一 mpl.rcParams['lines.linewidth'] = 2 mpl.rcParams['lines.color'] = 'r' # 修改方式二 mpl.rc('lines', linewidth=4, color='g')# 恢復默認參數(shù) mpl.rcdefaults()

??上面我提到一句話， 其本質是從本地文件matplotlibrc讀取數(shù)據(jù) ， 所以接下來我們找到根源，深入探索。

??matplotlib使用matplotlibrc配置文件來自定義各種屬性，我們稱之為rc settings或rc params, 它可以控制matplotlib中幾乎每個屬性的默認值：圖形大小，dpi，線寬，顏色和樣式，軸，軸和網(wǎng)格屬性，文本和字體屬性等。 matplotlib按以下順序在四個位置查找matplotlibrc：

matplotlibrc in the current working directory, usually used for specific customizations that you do not want to apply elsewhere.

$MATPLOTLIBRC if it is a file, else $MATPLOTLIBRC/matplotlibrc.

It next looks in a user-specific place, depending on your platform:

• On Linux and FreeBSD, it looks in .config/matplotlib/matplotlibrc (or $XDG_CONFIG_HOME/matplotlib/matplotlibrc) if you’ve customized your environment.
• On other platforms, it looks in .matplotlib/matplotlibrc.

*INSTALL*/matplotlib/mpl-data/matplotlibrc, where *INSTALL* is something like /usr/lib/python3.5/site-packages on Linux, and maybe C:\Python35\Lib\site-packages on Windows. Every time you install matplotlib, this file will be overwritten, so if you want your customizations to be saved, please move this file to your user-specific matplotlib directory.

# 查找文件位置>>> import matplotlib >>> matplotlib.matplotlib_fname() '/home/foo/.config/matplotlib/matplotlibrc'

?? 在我電腦中，只有第四個路徑匹配項，即在安裝包的時候生成的，部分內(nèi)容如下:

# 路徑 D:\Python\Lib\site-packages\matplotlib\mpl-data#### MATPLOTLIBRC FORMAT## This is a sample matplotlib configuration file - you can find a copy ## of it on your system in ## site-packages/matplotlib/mpl-data/matplotlibrc. If you edit it ## there, please note that it will be overwritten in your next install. ## If you want to keep a permanent local copy that will not be ## overwritten, place it in the following location: ## unix/linux: ## $HOME/.config/matplotlib/matplotlibrc or ## $XDG_CONFIG_HOME/matplotlib/matplotlibrc (if $XDG_CONFIG_HOME is set) ## other platforms: ## $HOME/.matplotlib/matplotlibrc ## ## See http://matplotlib.org/users/customizing.html#the-matplotlibrc-file for ## more details on the paths which are checked for the configuration file. ## ## This file is best viewed in a editor which supports python mode ## syntax highlighting. Blank lines, or lines starting with a comment ## symbol, are ignored, as are trailing comments. Other lines must ## have the format ## key : val ## optional comment ## ## Colors: for the color values below, you can either use - a ## matplotlib color string, such as r, k, or b - an rgb tuple, such as ## (1.0, 0.5, 0.0) - a hex string, such as ff00ff - a scalar ## grayscale intensity such as 0.75 - a legal html color name, e.g., red, ## blue, darkslategray##### CONFIGURATION BEGINS HERE## The default backend; one of GTK GTKAgg GTKCairo GTK3Agg GTK3Cairo ## MacOSX Qt4Agg Qt5Agg TkAgg WX WXAgg Agg Cairo GDK PS PDF SVG ## Template. ## You can also deploy your own backend outside of matplotlib by ## referring to the module name (which must be in the PYTHONPATH) as ## 'module://my_backend'. ## ## If you omit this parameter, it will always default to "Agg", which is a ## non-interactive backend. backend : TkAgg## Note that this can be overridden by the environment variable ## QT_API used by Enthought Tool Suite (ETS); valid values are ## "pyqt" and "pyside". The "pyqt" setting has the side effect of ## forcing the use of Version 2 API for QString and QVariant.## The port to use for the web server in the WebAgg backend. #webagg.port : 8988## The address on which the WebAgg web server should be reachable #webagg.address : 127.0.0.1## If webagg.port is unavailable, a number of other random ports will ## be tried until one that is available is found. #webagg.port_retries : 50## When True, open the webbrowser to the plot that is shown #webagg.open_in_browser : True## if you are running pyplot inside a GUI and your backend choice ## conflicts, we will automatically try to find a compatible one for ## you if backend_fallback is True #backend_fallback: True#interactive : False #toolbar : toolbar2 ## None | toolbar2 ("classic" is deprecated) #timezone : UTC ## a pytz timezone string, e.g., US/Central or Europe/Paris## Where your matplotlib data lives if you installed to a non-default ## location. This is where the matplotlib fonts, bitmaps, etc reside #datapath : /home/jdhunter/mpldata#### LINES ## See http://matplotlib.org/api/artist_api.html#module-matplotlib.lines for more ## information on line properties. #lines.linewidth : 1.5 ## line width in points #lines.linestyle : - ## solid line #lines.color : C0 ## has no affect on plot(); see axes.prop_cycle #lines.marker : None ## the default marker #lines.markeredgewidth : 1.0 ## the line width around the marker symbol #lines.markersize : 6 ## markersize, in points #lines.dash_joinstyle : round ## miter|round|bevel #lines.dash_capstyle : butt ## butt|round|projecting #lines.solid_joinstyle : round ## miter|round|bevel #lines.solid_capstyle : projecting ## butt|round|projecting #lines.antialiased : True ## render lines in antialiased (no jaggies)

?? 樣式表

??rcParams是自定義控制樣式最全面也是最細致的方法，但不見得是最佳的方法。因為我們很多時候并不想花費這么多的時間去設置一堆堆參數(shù)，于是會想到能不能使用模板呢？呵，聰明又懶惰的人類！怎么可能沒有呢！使用樣式模板很簡單：

plt.style.use('ggplot') # ggplot是其中一種預設樣式,會使用R的同學應該非常熟悉

print(plt.style.available) # 查看所有預設樣式

自定義樣式

??可以通過調(diào)用style.use以及樣式表的路徑或URL來創(chuàng)建自定義樣式并使用它們。此外，如果將?.mplstyle文件添加到mpl_configdir / stylelib，則可以通過調(diào)用style.use（<style-name> ）重用自定義樣式表。默認情況下，mpl_configdir應為?/ .config / matplotlib，但你可以使用matplotlib.get_configdir（）查看你的位置，你可能需要創(chuàng)建此目錄。

組合樣式

?? 即使用多種樣式

>>> import matplotlib.pyplot as plt >>> plt.style.use(['dark_background', 'presentation'])

臨時樣式

# 臨時樣式通過這種上下文的方式實現(xiàn)，在python中非常常見# ps:在R語言中也有類似的用法 with plt.style.context(('dark_background')):plt.plot(np.sin(np.linspace(0, 2 * np.pi)), 'r-o') plt.show()

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