Matplotlib.axes.Axes.get_adjustable() 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_adjustable() Function

The Axes.get_adjustable() function in axes module of matplotlib library is used to get which parameter the Axes is given to achieve a given aspect.

Syntax: Axes.get_adjustable(self)

Parameters: This method does not accepts any parameters.

Return value: This method returns the adjustable value.

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

Example 1:

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

fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.set_xscale("log")
ax1.set_yscale("log")
ax1.set_xlim(1e1, 1e3)
ax1.set_ylim(1e2, 1e3)
ax1.set_aspect(1)
ax1.set_title("Axes 1")

ax2.set_xscale("log")
ax2.set_yscale("log")
ax2.set_adjustable("datalim")
ax2.plot([1, 113, 10], [1, 119, 100], "o-")
ax2.set_xlim(1e-1, 1e2)
ax2.set_ylim(1e-1, 1e3)
ax2.set_aspect(1)
ax2.set_title("Axes 2")

w = ax1.get_adjustable()
w1 = ax2.get_adjustable()

ax1.text(20, 400,
         "     Value return by\n get_adjustable() : " +str(w))
ax2.text(1, 200,
         "   Value return by \nget_adjustable() : \n     " +str(w1))

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

Output:

Example 2:

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

n_angles = 40
n_radii = 10
min_radius = 2
radii = np.linspace(min_radius, 0.95, n_radii)

angles = np.linspace(0, 4 * np.pi, n_angles,
                     endpoint = False)
angles = np.repeat(angles[..., np.newaxis],
                   n_radii, axis = 1)

angles[:, 1::2] += np.pi / n_angles

x = (radii * np.cos(angles)).flatten()
y = (radii * np.sin(angles)).flatten()

triang = tri.Triangulation(x, y)

triang.set_mask(np.hypot(x[triang.triangles].mean(axis = 1),
                         y[triang.triangles].mean(axis = 1))
                < min_radius)
fig, (ax, ax1) = plt.subplots(1, 2)

ax.triplot(triang, 'bo-', lw = 1, color = "green")
ax.set_aspect('equal')
ax.set_title("Axes 1")

ax1.set_aspect('equal')
ax1.set_adjustable("datalim")
ax1.triplot(triang, 'bo-', lw = 1, color = "green")
ax1.set_title("Axes 2")

w = ax.get_adjustable()
w1 = ax1.get_adjustable()

ax.text(-1.15, -3.5,
        "     Value return by\n get_adjustable() : " +str(w))

ax1.text(-1, 2.5,
         "   Value return by \nget_adjustable() : \n     " +str(w1))

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

Output: