Matplotlib.axes.Axes.set_prop_cycle() in Python
Last Updated :
19 Apr, 2020
Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.
matplotlib.axes.Axes.set_prop_cycle() Function
The Axes.set_prop_cycle() function in axes module of matplotlib library is used to set the property cycle of the Axes.
Syntax: Axes.set_prop_cycle(self, *args, **kwargs)
Parameters: This method accepts the following parameters.
- cycler : This parameter is used to set the given Cycler.
- label : This parameter is the property key.
- values : This parameter is the finite-length iterable of the property values.
Returns:This method does not returns any values.
Below examples illustrate the matplotlib.axes.Axes.set_prop_cycle() function in matplotlib.axes:
Example 1:
from cycler import cycler
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace( 0 , 200 , 10 )
yy = np.transpose([ 2 * np.sin(x + phi) for phi in x])
fig, ax1 = plt.subplots()
ax1.set_prop_cycle(color = [ 'magenta' , 'g' ,
'y' , 'k' ],
lw = [ 1 , 2 , 3 , 4 ])
ax1.plot(yy)
ax1.set_title(' matplotlib.axes.Axes.set_prop_cycle() \
Example\n ', fontsize = 12, fontweight =' bold')
plt.show()
|
Output:
Example 2:
from cycler import cycler
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace( 0 , 3 * np.pi)
offsets = np.linspace( 0 , 3 * np.pi, 8 ,
endpoint = False )
yy = np.transpose([ 2 * np.sin(x + phi) for phi in offsets])
plt.rc( 'lines' , linewidth = 4 )
plt.rc( 'axes' , prop_cycle = (cycler(color = [ 'r' , 'g' ,
'purple' ,
'orange' ]) +
cycler(linestyle = [ '-' ,
'--' ,
':' ,
'-.' ])))
fig, (ax0, ax1) = plt.subplots(nrows = 2 )
ax0.plot(yy)
ax0.set_title('Above example with set_prop_cycle() \
function\n\nSet default color cycle to rgby',
fontsize = 12 , fontweight = 'bold' )
ax1.set_prop_cycle(color = [ 'magenta' , 'g' ,
'y' , 'k' ],
lw = [ 1 , 2 , 3 , 4 ])
ax1.plot(yy)
ax1.set_title( 'Set axes color cycle to cmyk' ,
fontsize = 12 ,
fontweight = 'bold' )
plt.show()
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Output:
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