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:
Python3
# 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:
Python3
# 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:
