matplotlib.pyplot.rc()函数

Matplotlib是Python中一个非常棒的二维数组绘图可视化库。Matplotlib是一个基于NumPy数组构建的多平台数据可视化库，用于更广泛的SciPy堆栈。

matplotlib.pyplot.rc()

matplotlib.pyplot.rc()函数用于设置rc参数。在rc中分组是通过“组”完成的(例如，行)。对于轴中的线，组为线宽。坐标轴的组是facecolor等等。列表或元组也可以作为组名(例如，xtick, ytick)。Kwargs充当名值对，广义上是一个字典，例如:

语法:

rc(‘lines’, linewidth=3, color=’g’)

它设置当前的rc参数：

rcParams[‘lines.linewidth’] = 3
rcParams[‘lines.color’] = ‘g’

为节省交互使用者的输入，现提供下列别名:

Alias	Property
‘lw’	‘linewidth’
‘ls’	‘linestyle’
‘c’	‘color’
‘fc’	“facecolor”
‘ec’	“edgecolor”
‘mew’	“markeredgewidth”
‘aa’	‘antialiased’

因此可以将上述rc命令缩写如下

rc(‘lines’, lw=3, c=’g’)

注意:可以使用python的kwargs字典来存储默认参数的字典。例如,

font = {‘family’ : ‘monospace’,
‘weight’ : ‘italic’,
‘size’ : ‘medium’}
# pass in the font dict as kwargs
rc(‘font’, **font)

这有助于在不同的配置之间轻松切换。你也可以使用matplotlib.style.use(‘ default ‘)或rcdefaults()来恢复更改后的rc参数。

示例1

from cycler import cycler
import numpy as np
import matplotlib.pyplot as plt
  
  
# setting up a custom cycler
sample_cycler = (cycler(color =['r', 'g', 
                                'b', 'y']) +
                 cycler(lw =[1, 2, 3, 4]))
  
# using the rc function
plt.rc('lines', linewidth = 4)
plt.rc('axes', prop_cycle = sample_cycler)
  
A = np.linspace(0, 2 * np.pi, 50)
line_offsets = np.linspace(0, 2 * np.pi, 4, 
                           endpoint = False)
  
B = np.transpose([np.sin(A + phi) for phi in line_offsets])
  
figure, (axes0, axes1) = plt.subplots(nrows = 2)
axes0.plot(B)
axes0.set_title('Set default color cycle to 1st plot')
  
axes1.set_prop_cycle(sample_cycler)
axes1.plot(B)
axes1.set_title('Set axes color cycle to 2nd plot')
  
# Adding space between the two plots.
figure.subplots_adjust(hspace = 0.4)
plt.show()

输出:

示例2

import matplotlib.pyplot as plt
  
  
plt.subplot(332)
plt.plot([1, 2, 3, 4])
  
# setting  the axes attributes 
# before the call to subplot
plt.rc('font', weight ='bold')
plt.rc('xtick.major', size = 5, pad = 7)
plt.rc('xtick', labelsize = 15)
  
# setting aliases for color, linestyle 
# and linewidth; gray, solid, thick
plt.rc('grid', c ='0.3', ls ='-', lw = 4)
plt.rc('lines', lw = 2, color ='g')
plt.subplot(312)
  
plt.plot([1, 2, 3, 4])
plt.grid(True)
  
# set changes to default value
plt.rcdefaults()
plt.subplot(313)
plt.plot([1, 2, 3, 4])
plt.grid(True)
plt.show()

输出: