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Matplotlib.axes.Axes.set_clip_box() in Python

Last Updated : 30 Apr, 2020
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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()


Output:



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