Matplotlib.axis.Axis.get_transformed_clip_path_and_affine() 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.Axis.get_transformed_clip_path_and_affine() Function

The Axis.get_transformed_clip_path_and_affine() function in axis module of matplotlib library is used to get the clip path with the non-affine part of its transformation applied, and the remaining affine part of its transformation. 

Syntax: Axis.get_transformed_clip_path_and_affine(self) 

Parameters: This method does not accepts any parameter. 

Return value: This method return the clip path with the non-affine part of its transformation applied, and the remaining affine part of its transformation. 

Below examples illustrate the matplotlib.axis.Axis.get_transformed_clip_path_and_affine() function in matplotlib.axis:

Example 1:

Image used:

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('image.PNG') as image_file:
    image = plt.imread(image_file)

fig, ax = plt.subplots()
im = ax.imshow(image)
patch = patches.Rectangle((50, 50), 200, 200,
                          transform = ax.transData)

# use of get_transformed_clip_path_and_affine() method
val = Axis.get_transformed_clip_path_and_affine(im)
ax.set_title("Value Return by get_transformed_clip_path_and_affine(): "
             + str(val))

fig.suptitle("""matplotlib.axis.Axis.get_transformed_clip_path_and_affine()
function Example\n""", fontweight ="bold")

plt.show()

Output:

Example 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)

# use of get_transformed_clip_path_and_affine() method
val = Axis.get_transformed_clip_path_and_affine(im)
print("Value Return by get_transformed_clip_path_and_affine(): ")
for i in val:
    print(i)

fig.suptitle("""matplotlib.axis.Axis.get_transformed_clip_path_and_affine()
function Example\n""", fontweight ="bold")

plt.show()

Output:

Value Return by get_transformed_clip_path_and_affine(): 
Path(array([[ 0.,  1.],
       [ 1.,  0.],
       [ 0., -1.],
       [-1.,  0.],
       [ 0.,  1.]]), None)
Affine2D(
    [[ 82.66666667   0.         328.        ]
     [  0.          61.6        237.6       ]
     [  0.           0.           1.        ]])