Matplotlib.axis.Tick.get_clip_path() in Python
Last Updated :
01 May, 2022
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Matplotlib is a library in Python and it is a 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.get_clip_path() Function
The Tick.get_clip_path() function in axis module of matplotlib library is used to get the clip-path.
Syntax: Tick.get_clip_path(self)
Parameters: This method does not accepts any parameter.
Return value: This method return the clip path.
Below examples illustrate the matplotlib.axis.Tick.get_clip_path() function in matplotlib.axis:
Example 1:
Image Used-

# 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('loggf.PNG') as image_file:
image = plt.imread(image_file)
fig, ax = plt.subplots()
im = ax.imshow(image)
patch = patches.Rectangle((10, 10),
560,
500,
transform=ax.transData)
if Tick.get_clip_path(im) is None:
im.set_clip_path(patch)
fig.suptitle("""matplotlib.axis.Tick.set_clip_path()
function Example\n""", fontweight="bold")
plt.show()
Output:

Example 2:
# 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)
im = ax.imshow(Z,
interpolation='bilinear',
cmap=cm.gray,
origin='lower',
extent=[-3, 3, -3, 3],
clip_path=patch,
clip_on=True)
ax.text(-2.8, 2, str(Tick.get_clip_path(im)))
fig.suptitle("""matplotlib.axis.Tick.set_clip_path()
function Example\n""", fontweight="bold")
plt.show()
Output:
