Matplotlib.pyplot.rc() in Python

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.

matplotlib.pyplot.rc()

matplotlib.pyplot.rc() function is used to the rc params. Grouping in rc is done through the ‘group'(eg, for lines). For lines in axes the group is linewidth. The group for axes is facecolor and so on. A list or tuple can also act as a group name(eg, xtick, ytick). Kwargs act as a name value pair which broadly is a dictionary, eg:

Syntax:

rc(‘lines’, linewidth=3, color=’g’)

It sets the current rc params and is same as



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

To save typing for interactive users the following aliases are available:

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

Therefore once could abbreviate the above rc commands as follows

rc(‘lines’, lw=3, c=’g’)

Note: One can use the pythons kwargs dictionary to store dictionaries of its default parameters. For example,

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

This helps in easily switching among different configurations. you can also use matplotlib.style.use(‘default’) or rcdefaults() to restore back the rc params after change.

Example 1:

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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()

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Output:
matplotlib.pyplot.rc()

Example 2:

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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()

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Output:
matplotlib.pyplot.rc()

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