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.Axis.get_clip_path() Function
The Axis.get_clip_path() function in axis module of matplotlib library is used to get the clip path.
Syntax: Axis.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.Axis.get_clip_path() function in matplotlib.axis:
Example 1:
Image Used
# 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( '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 Axis.get_clip_path(im) is None :
im.set_clip_path(patch)
fig.suptitle( """matplotlib.axis.Axis.get_clip_path()
function Example\n""" , fontweight = "bold")
plt.show() |
Output:
Example 2:
# 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 )
ax.text( - 2.8 , 2 , str (Axis.get_clip_path(im)))
fig.suptitle( """matplotlib.axis.Axis.get_clip_path()
function Example\n""" , fontweight = "bold")
plt.show() |
Output: