Matplotlib.axis.Tick.set_clip_path() function in Python

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. It is an amazing visualization library in Python for 2D plots of arrays and used for working with the broader SciPy stack.

Matplotlib.axis.Tick.set_clip_path() Function

The Tick.set_clip_path() function in axis module of matplotlib library is used to set the artist’s clip path. 

Syntax: Tick.set_clip_path(self, path, transform=None) 

Parameters: This method accepts the following parameters. 

• path: This parameter is the clip path.
• transform: This parameter in which Path is converted to a TransformedPath using transform.

Return value: This method does not return any value. 

Below examples illustrate the matplotlib.axis.Tick.set_clip_path() function in matplotlib.axis:
Example 1:
Input Image

Python3

# Implementation of matplotlib function
from matplotlib.axis import Tick
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.cbook as cbook


with cbook.get_sample_data('geeksforgeeks-logo1.PNG') as image_file:
    image = plt.imread(image_file)

fig, ax = plt.subplots()
ax.imshow(image)
patch = patches.Rectangle((10, 10),
                          560,
                          500,
                          transform = ax.transData)

ax.set_clip_path(patch)

fig.suptitle('matplotlib.axis.Tick.set_clip_path() \
function Example', fontweight ="bold")

plt.show()

Output:

Example 2:

Python3

# Implementation of matplotlib function
from matplotlib.axis import Tick
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.patches import PathPatch


delta = 0.025

x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)

Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2

path = Path([[0, 1], [1, 0], [0, -1],
            [-1, 0], [0, 1]])
patch = PathPatch(path, facecolor ='none')

fig, ax = plt.subplots()
ax.add_patch(patch)

ax.imshow(Z,
               interpolation ='bilinear',
               cmap = cm.gray,
               origin ='lower',
               extent =[-3, 3, -3, 3],
               clip_path = patch,
               clip_on = True)
ax.set_clip_path(patch)

fig.suptitle('matplotlib.axis.Tick.set_clip_path() \
function Example', fontweight ="bold")

plt.show()

Output: