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.get_transformed_clip_path_and_affine() Function
The Tick.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: Tick.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.Tick.get_transformed_clip_path_and_affine() function in matplotlib.axis: Example 1: Image Used 
Python3
# Implementation of matplotlib function from matplotlib.axis import Tick 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((10, 10), 100, 100, transform = ax.transData) val = Tick.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.Tick.get_transformed_clip_path_and_affine()\n function Example\n""", fontweight ="bold") plt.show() |
Output: 
Example 2:
Python3
# Implementation of matplotlib function from matplotlib.axis import Tick 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 = Tick.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.Tick.get_transformed_clip_path_and_affine()\n 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. ]])
