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Matplotlib.axes.Axes.set_adjustable() in Python
  • Last Updated : 19 Apr, 2020

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()

Output:

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