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.Tick.set_path_effects() Function
The Tick.set_path_effects() function in axis module of matplotlib library is used to set the path effects.
Syntax: Tick.set_path_effects(self, path_effects)
Parameters: This method accepts the following parameters.
- path_effects: This parameter is the AbstractPathEffect.
Return value: This method does not return any value.
Below examples illustrate the matplotlib.axis.Tick.set_path_effects() function in matplotlib.axis:
Example 1:
# Implementation of matplotlib function from matplotlib.axis import Tick
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patheffects as path_effects
fig, ax = plt.subplots()
t = ax.text( 0.02 , 0.5 ,
'GeeksForGeeks' ,
fontsize = 40 ,
weight = 1000 ,
va = 'center' )
Tick.set_path_effects(t, [path_effects.PathPatchEffect(offset = ( 4 , - 4 ),
hatch = 'xxxx' ,
facecolor = 'lightgreen' ),
path_effects.PathPatchEffect(edgecolor = 'white' ,
linewidth = 1.1 ,
facecolor = 'yellow' )])
fig.suptitle('matplotlib.axis.Tick.set_path_effects() \ function Example', fontweight = "bold" )
plt.show() |
Output:
Example 2:
# Implementation of matplotlib function from matplotlib.axis import Tick
import matplotlib.pyplot as plt
import matplotlib.patheffects as PathEffects
import numpy as np
fig, ax1 = plt.subplots()
ax1.imshow([[ 1 , 2 ], [ 2 , 3 ]])
txt = ax1.annotate( "Fourth Qaud" ,
( 1. , 1. ),
( 0. , 0 ),
arrowprops = dict (arrowstyle = "->" ,
connectionstyle = "angle3" ,
lw = 2 ),
size = 20 , ha = "center" ,
path_effects = [PathEffects.withStroke(linewidth = 3 ,
foreground = "r" )])
Tick.set_path_effects(txt.arrow_patch, [ PathEffects.Stroke(linewidth = 5 ,
foreground = "r" ),
PathEffects.Normal()])
ax1.grid( True , linestyle = "-" )
pe = [PathEffects.withStroke(linewidth = 3 ,
foreground = "r" )]
for l in ax1.get_xgridlines() + ax1.get_ygridlines():
Tick.set_path_effects(l, pe)
fig.suptitle('matplotlib.axis.Tick.set_path_effects() \ function Example', fontweight = "bold" )
plt.show() |
Output: