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: