Matplotlib.axis.Tick.set_clip_on() 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_on() Function

The Tick.set_clip_on() function in axis module of matplotlib library is used to set whether the artist uses clipping. 

Syntax: Tick.set_clip_on(self, b) 

Parameters: This method accepts the following parameters. 

• b: This parameter contains the boolean value.

Return value: This method does not return any value. 

Below examples illustrate the matplotlib.axis.Tick.set_clip_on() function in matplotlib.axis:
Example 1:

Python3

# Implementation of matplotlib function
from matplotlib.axis import Tick
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Ellipse


delta = 5.0

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_alpha(0.1)
    ax.add_artist(e)

ax.set_xlim(-1, 5)
ax.set_ylim(-1, 5)
Tick.set_clip_on(ax, b = False)

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

plt.show()

Output:

Example 2:

Python3

# Implementation of matplotlib function
from matplotlib.axis import Tick
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.transforms as mtransforms


x0 = 1.05

arrow_style ="simple, head_length = 15, \
head_width = 25, tail_width = 10"
rect_style ="simple, tail_width = 25"
line_style ="simple, tail_width = 1"

fig, ax = plt.subplots()

trans = mtransforms.blended_transform_factory(ax.transAxes,
                                              ax.transData)

y_tail = 0
y_head = 10
arrow1 = mpatches.FancyArrowPatch((x0, y_tail),
                                  (x0, y_head),
                                   arrowstyle = arrow_style,
                                   transform = trans)

Tick.set_clip_on(arrow1, b = False)
ax.add_patch(arrow1)

ax.set_xlim(0, 10)
ax.set_ylim(0, 10)

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

plt.show()

Output: