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, 0.65bottom, height = 0.1, 0.65spacing = 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 = 0.05lim = 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:
