Matplotlib.axes.Axes.set_snap() in Python

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.

matplotlib.axes.Axes.set_snap() Function

The Axes.set_snap() function in axes module of matplotlib library is used to set the snapping behavior.

Syntax: Axes.set_snap(self, snap)

Parameters: This method accepts only one parameters.

• snap: This parameter contains the boolean value or None.

Returns: This method does not return any value.

Below examples illustrate the matplotlib.axes.Axes.set_snap() function in matplotlib.axes:

Example 1:

# Implementation of matplotlib function
import matplotlib.pyplot as plt
from mpl_toolkits.axisartist.axislines import Subplot

fig = plt.figure()

ax = Subplot(fig, 111)
fig.add_subplot(ax)

ax.axis["left"].set_visible(False)
ax.axis["top"].set_visible(False)

ax.set_snap(True)

fig.suptitle('matplotlib.axes.Axes.set_snap() \
function Example\n', fontweight ="bold")

plt.show()

Output:

Example-2:

# Implementation of matplotlib function
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
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)

ax.set_snap(None)

fig.suptitle('matplotlib.axes.Axes.set_snap() function\
 Example', fontweight ="bold")

plt.show()

Output: