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.set_clip_path() Function
The Axis.set_clip_path() function in axis module of matplotlib library is used to set the artist’s clip path.
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.
Below examples illustrate the matplotlib.axis.Axis.set_clip_path() function in matplotlib.axis:
Example 1:
Input Image
Python3
# Implementation of matplotlib functionfrom matplotlib.axis import Axisimport matplotlib.pyplot as plt import matplotlib.patches as patches import matplotlib.cbook as cbook with cbook.get_sample_data('neveropen-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() |
Output:
Example 2:
Python3
# Implementation of matplotlib functionfrom matplotlib.axis import Axisimport 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() |
Output:
