Matplotlib.axis.Axis.set_smart_bounds() function in Python
Last Updated :
08 Jun, 2020
Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. It is an amazing visualization library in Python for 2D plots of arrays and used for working with the broader SciPy stack.
Matplotlib.axis.Axis.set_smart_bounds() Function
The Axis.set_smart_bounds() function in axis module of matplotlib library is used to set the axis to have smart bounds.
Syntax: Axis.set_smart_bounds(self, value)
Parameters: This method accepts the following parameters.
- value: This parameter is the bool value.
Return value: This method does not return any value.
Below examples illustrate the matplotlib.axis.Axis.set_smart_bounds() function in matplotlib.axis:
Example 1:
Python3
from matplotlib.axis import Axis
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
x = np.linspace( - np.pi, np.pi, 100 )
y = 2 * np.sin(x)
ax = fig.add_subplot()
ax.set_title( 'centered spines' )
ax.plot(x, y)
ax.spines[ 'left' ].set_position( 'center' )
ax.spines[ 'right' ].set_color( 'none' )
ax.spines[ 'bottom' ].set_position( 'center' )
ax.spines[ 'top' ].set_color( 'none' )
ax.spines[ 'left' ].set_smart_bounds( True )
ax.spines[ 'bottom' ].set_smart_bounds( True )
ax.xaxis.set_ticks_position( 'bottom' )
ax.yaxis.set_ticks_position( 'left' )
ax.grid()
fig.suptitle(
, fontweight = "bold")
plt.show()
|
Output:
Example 2:
Python3
from matplotlib.axis import Axis
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
x = np.linspace( - np.pi, np.pi, 100 )
y = 2 * np.sin(x)
ax = fig.add_subplot()
ax.set_title( 'Spines at data (3, 2)' )
ax.plot(x, y)
ax.spines[ 'left' ].set_position(( 'data' , 3 ))
ax.spines[ 'right' ].set_color( 'none' )
ax.spines[ 'bottom' ].set_position(( 'data' , 2 ))
ax.spines[ 'top' ].set_color( 'none' )
ax.spines[ 'left' ].set_smart_bounds( True )
ax.spines[ 'bottom' ].set_smart_bounds( True )
ax.xaxis.set_ticks_position( 'bottom' )
ax.yaxis.set_ticks_position( 'left' )
ax.grid()
fig.suptitle(
, fontweight = "bold")
plt.show()
|
Output:
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