Matplotlib.axes.Axes.get_autoscaley_on() 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_autoscaley_on() Function

The Axes.get_autoscaley_on() function in axes module of matplotlib library is used to get whether autoscaling for the y-axis is applied on plot commands.

Syntax: Axes.get_autoscaley_on(self)

Parameters: This method does not accepts any parameters.

Return value: This method returns the boolean value.

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

Example 1:

# ImpleIn Reviewtation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(0, 20, 300)
xdata = np.sin(np.pi * t / 12)
ydata = np.cos(4 * np.pi * t / 21)

fig, ax = plt.subplots()

ax.plot(xdata, ydata, 'g-')
w = ax.get_autoscaley_on()
ax.text(-0.5, -0.75, "Value return by get_autoscaley_on : "
        + str(w),
        fontweight ="bold")
fig.suptitle('matplotlib.axes.Axes.get_autoscaley_on() function \
Example\n', fontweight ="bold")
fig.canvas.draw()
plt.show()

Output:

Example 2:

# ImpleIn Reviewtation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(16, 365, (365-16)*4)
xdata = np.sin(2 * np.pi * t / 15)
ydata = np.cos(2 * np.pi * t / 12)

fig, (ax, ax1) = plt.subplots(1, 2)

ax.plot(xdata, ydata, 'g-', alpha = 0.6)
ax.set_title("Axes 1")
ax1.plot(xdata, ydata, 'g-', alpha = 0.6)
ax1.set_autoscaley_on(False)
ax1.set_title("Axes 2")

w = ax.get_autoscaley_on()
w1 = ax1.get_autoscaley_on()
ax.text(-0.5, 0, "Value return : " + str(w),
        fontweight ="bold")
ax1.text(-0.5, 0.5, "Value return : " + str(w1),
         fontweight ="bold")

fig.suptitle('matplotlib.axes.Axes.get_autoscaley_on() \
function Example\n', fontweight ="bold")
fig.canvas.draw()
plt.show()

Output: