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: