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_animated() Function
The Axis.set_animated() function in axis module of matplotlib library is used to set the artist’s animation state.
Syntax: Axis.set_animated(self, b)
Parameters: This method accepts the following parameters.
- b: This parameter is the boolean value.
Return value: This method does not return any value.
Below examples illustrate the matplotlib.axis.Axis.set_animated() function in matplotlib.axis:
Example 1:
# Implementation of matplotlib function from matplotlib.axis import Axis
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
data = np.array([[ 1 , 2 , 3 , 4 , 5 ],
[ 7 , 4 , 9 , 2 , 3 ]])
fig = plt.figure()
ax = plt.axes(xlim = ( 0 , 20 ), ylim = ( 0 , 20 ))
line, = ax.plot([], [], 'r-' )
annotation = ax.annotate('',
xy = (data[ 0 ][ 0 ],
data[ 1 ][ 0 ]))
Axis.set_animated(annotation, True )
def init():
return line, annotation
def update(num):
newData = np.array([[ 1 + num,
2 + num / / 2 ,
3 ,
4 - num / / 4 ,
5 + num],
[ 7 , 4 ,
9 + num / / 3 ,
2 , 3 ]])
line.set_data(newData)
return line, annotation
anim = animation.FuncAnimation(fig,
update,
frames = 25 ,
init_func = init,
interval = 60 ,
blit = True )
fig.suptitle('matplotlib.axis.Axis.set_animated() \ function Example\n', fontweight = "bold" )
plt.show() |
Output:
Example 2:
# Implementation of matplotlib function from matplotlib.axis import Axis
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
ax.set_xlim([ - 1 , 1 ])
ax.set_ylim([ - 1 , 1 ])
L = 50
theta = np.linspace( 0 , 2 * np.pi, L)
r = np.ones_like(theta)
x = r * np.cos(theta)
y = r * np.sin(theta)
line, = ax.plot( 1 , 0 , 'ro' )
annotation = ax.annotate(
'annotation' , xy = ( 1 , 0 ), xytext = ( - 1 , 0 ),
arrowprops = { 'arrowstyle' : "->" }
) Axis.set_animated(annotation, False )
def update(i):
new_x = x[i % L]
new_y = y[i % L]
line.set_data(new_x, new_y)
annotation.set_position(( - new_x, - new_y))
annotation.xy = (new_x, new_y)
return line, annotation
ani = animation.FuncAnimation(
fig, update, interval = 50 , blit = False
) fig.suptitle('matplotlib.axis.Axis.set_animated() \ function Example\n', fontweight = "bold" )
plt.show() |
Output: