Matplotlib.axes.Axes.set_clip_box() in Python

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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_clip_box() Function

The Axes.set_clip_box() function in axes module of matplotlib library is used to set the artist’s clip Bbox.

Syntax: Axes.set_clip_box(self, clipbox)

Parameters: This method accepts only one parameters.

clipbox: This parameter is the Bbox.

Returns: This method does not return any value.

Below examples illustrate the matplotlib.axes.Axes.set_clip_box() function in matplotlib.axes:

Example 1:

# Implementation of matplotlib function 
import matplotlib.pyplot as plt 
import numpy as np 
from matplotlib.patches import Ellipse 
  
delta = 45.0
  
angles = np.arange(0, 360 + delta, delta) 
ells = [Ellipse((2, 2), 5, 2, a) for a in angles] 
  
fig, ax = plt.subplots() 
  
for e in ells: 
    e.set_clip_box(ax.bbox) 
    e.set_alpha(0.1) 
    ax.add_artist(e) 
  
plt.xlim(-1, 5) 
plt.ylim(-1, 5) 
    
    
fig.suptitle('matplotlib.axes.Axes.set_clip_box() \ 
function Example\n\n', fontweight ="bold") 
  
plt.show() 

Output:

Example-2:

# Implementation of matplotlib function 
import matplotlib.pyplot as plt 
import numpy as np 
from matplotlib.patches import Ellipse 
    
NUM = 200
    
ells = [Ellipse(xy = np.random.rand(2) * 10, 
                width = np.random.rand(),  
                height = np.random.rand(), 
                angle = np.random.rand() * 360) 
        for i in range(NUM)] 
    
fig, ax = plt.subplots(subplot_kw ={'aspect': 'equal'}) 
for e in ells: 
    ax.add_artist(e) 
    e.set_clip_box(ax.bbox) 
    e.set_alpha(np.random.rand()) 
    e.set_facecolor(np.random.rand(4)) 
    
ax.set_xlim(3, 7) 
ax.set_ylim(3, 7) 
    
    
fig.suptitle('matplotlib.axes.Axes.set_clip_box() \ 
function Example\n\n', fontweight ="bold") 
  
plt.show() 