Matplotlib.axes.Axes.set_adjustable() in Python

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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.set_adjustable() Function

The Axes.set_adjustable() function in axes module of matplotlib library is used to define which parameter the Axes will change to achieve a given aspect.

Syntax: Axes.set_adjustable(self, adjustable, share=False)

Parameters: This method accepts the following parameters.

adjustable : This defines which parameter will be adjusted to meet the required aspect.

share: This parameter is used to apply the settings to all shared Axes.

Return value: This method does not return any value.

Below examples illustrate the matplotlib.axes.Axes.set_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("adjustable = box") 
  
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("adjustable = datalim") 
  
fig.suptitle('matplotlib.axes.Axes.set_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("adjustable = box") 
   
ax1.set_aspect('equal') 
ax1.set_adjustable("datalim") 
ax1.triplot(triang, 'bo-', lw = 1, color = "green") 
ax1.set_title("adjustable = datalim") 
   
fig.suptitle('matplotlib.axes.Axes.set_adjustable() \ 
function Example\n', fontweight ="bold") 
fig.canvas.draw() 
plt.show() 