Matplotlib.axes.Axes.get_xbound() 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.get_xbound() Function

The Axes.get_xbound() function in axes module of matplotlib library is used to return the lower and upper numerical bounds of the x-axis in increasing order

Syntax: Axes.get_xbound(self)

Parameters: This method does not accepts any parameters.

Returns:This method returns the following

• lower, upper :This returns the current lower and upper x-axis bounds.

Note: This function can be used in place of get_xlim in various conditions.

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

Example 1:

# Implementation of matplotlib function
from matplotlib.widgets import Cursor
import numpy as np
import matplotlib.pyplot as plt

fig, [ax, ax1] = plt.subplots(2, 1)
t = 4*(np.random.rand(2, 100) - .5)
x = np.cos(2 * np.pi * t)
y = np.sin(2 * np.pi * t)

ax.plot(x, y, 'g')
lower, upper = ax.get_xbound()
ax.set_title('matplotlib.axes.Axes.get_xbound()\
 Example\n Original Window',
             fontsize = 14, fontweight ='bold')

ax1.plot(x, y, 'g')
ax1.set_xbound(1.5 * lower, 0.5 * upper)
ax1.set_title('Window After Using get_xbound() function',
             fontsize = 14, fontweight ='bold')
plt.show()

Output:

Example 2:

import numpy as np
import matplotlib.pyplot as plt

# Fixing random state for
# reproducibility
np.random.seed(19680801)

# the random data
x = np.random.randn(1000)
y = np.random.randn(1000)

# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
spacing = 0.005


rect_scatter = [left, bottom,
                width, height]

rect_histx = [left,
              bottom + height + spacing,
              width, 0.2]

rect_histy = [left + width + spacing,
              bottom, 0.2, height]

# start with a rectangular Figure
plt.figure()

ax_scatter = plt.axes(rect_scatter)
ax_scatter.tick_params(direction ='in',
                       bottom = True,
                       right = True)

ax_histx = plt.axes(rect_histx)
ax_histx.tick_params(direction ='in',
                     labeltop = True)

ax_histy = plt.axes(rect_histy)
ax_histy.tick_params(direction ='in',
                     labelleft = True)


# the scatter plot:
ax_scatter.scatter(2 * x, y * 2, color ="green")

# now determine nice limits by hand:
binwidth = 0.05
lim = np.ceil(np.abs([x, y]).max() / binwidth) * binwidth
ax_scatter.set_xbound((-0.5 * lim, 0.5 * lim))
ax_scatter.set_ybound((-0.25 * lim, 0.25 * lim))

bins = np.arange(-lim, lim + binwidth, binwidth)
ax_histx.hist(x, bins = bins,
              color ="green")
ax_histy.hist(y, bins = bins,
              color ="green",
              orientation ='horizontal')

ax_histx.set_xbound(ax_scatter.get_xbound())
ax_histy.set_ybound(ax_scatter.get_ybound())

plt.show()

Output: