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: