Python中的 Matplotlib.axis.Tick.set_clip_on()函数

Matplotlib是Python中的一个库，它是 NumPy 库的数值数学扩展。它是Python中用于二维数组图的惊人可视化库，用于处理更广泛的 SciPy 堆栈。

Matplotlib.axis.Tick.set_clip_on()函数

matplotlib库的axis模块中的Tick.set_clip_on()函数用于设置美工是否使用剪辑。

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. 

下面的示例说明了 matplotlib.axis 中的 matplotlib.axis.Tick.set_clip_on()函数：
示例 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()

输出：

示例 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() 

输出：