Python中的 Matplotlib.axis.Axis.set_clip_path()函数

Matplotlib是Python中的一个库，它是 NumPy 库的数值数学扩展。它是Python中用于数组二维图的惊人可视化库，用于处理更广泛的 SciPy 堆栈。

Matplotlib.axis.Axis.set_clip_path()函数

matplotlib库的axis模块中的axis.set_clip_path()函数用于设置美工的剪辑路径。

Syntax: Axis.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. 

下面的示例说明了 matplotlib.axis 中的 matplotlib.axis.Axis.set_clip_path()函数：
示例 1：

输入图像

Python3

# Implementation of matplotlib function
from matplotlib.axis import Axis
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() 
im = ax.imshow(image) 
patch = patches.Rectangle((10, 10), 
                          560, 
                          500,  
                          transform = ax.transData) 
   
im.set_clip_path(patch) 
 
fig.suptitle('matplotlib.axis.Axis.set_clip_path() \
function Example\n', fontweight ="bold") 
   
plt.show()

输出：

示例 2：

Python3

# Implementation of matplotlib function
from matplotlib.axis import Axis
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) 
im.set_clip_path(patch) 
 
fig.suptitle('matplotlib.axis.Axis.set_clip_path() \
function Example\n', fontweight ="bold") 
   
plt.show()

输出：