How to add time control panel to FuncAnimation from matplotlib

Question

I am currently using matplotlib.animation.FuncAnimation()to display an animation of my work in the picture.

It works very well, and I understand the arguments that I use (interval, time range, ...) However, I was wondering if there is a way to implement (perhaps directly in the figure) a panel containing animation, scroll bar or something yet that allows me:

• Fast forward or backward to the time zone of interest.
• Show me where I am in the animation (10%, then 20%, ...).

Basically, this is a way to control the animation in python on a figure, how would I control it as a video file played by a video player?

If necessary, this looks like code for this animation:

    def init():
        im1.set_data(XYslice[0, :, :])
        im2.set_data(XZslice[0, Nplans/2:, :])
        return([im1, im2])

    def animate(t):
       im1.set_data(XYslice[t, :, :])
       im2.set_data(XZslice[t, Nplans/2:, :])
       return [im1, im2]

    anim = animation.FuncAnimation(fig, animate, np.arange(Ntime), interval=200,
                                 blit=True, init_func=init, repeat=True)

Answer 1

, . matplotlib widgets:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.mlab import bivariate_normal
from matplotlib.widgets import Slider, Button

#Setup figure and data
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.25)
delta = 0.5
t = np.arange(0.0, 100.0, 0.1)
x = np.arange(-3.0, 4.001, delta)
y = np.arange(-4.0, 3.001, delta)
X, Y = np.meshgrid(x, y)
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = (Z1 - Z2) * 5.
cmap = plt.cm.rainbow
im = ax.pcolormesh(X, Y, Z, cmap=cmap)
fig.colorbar(im)
axcolor = 'lightgoldenrodyellow'
axtime = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
stime = Slider(axtime, 'Time', 0.0, 100.0, valinit=50.0)

#Routines to reset and update sliding bar
def reset(event):
    stime.reset()

def update(val):
    time = stime.val/10.
    Z = (Z1 - Z2) * time
    im.set_array(Z.ravel())
    fig.canvas.draw()

#Bind sliding bar and reset button  
stime.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
button.on_clicked(reset)

plt.show()

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, , :

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.mlab import bivariate_normal
from matplotlib.widgets import Slider, Button

#Setup figure and data
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.25)
delta = 0.5
t = np.linspace(0.0, 100.0, 256)
x = np.linspace(-4.0, 4.001, 512)
y = np.linspace(-4.0, 4.001, 512)
X, Y = np.meshgrid(x, y)
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
XZslice = np.zeros((256,512,512))
for i in range(t.shape[0]):
    XZslice[i,:,:] = (Z1 - Z2) * t[i]/10.
cmap = plt.cm.rainbow
im = ax.pcolormesh(XZslice[128,:,:], cmap=cmap)
fig.colorbar(im)
axcolor = 'lightgoldenrodyellow'
axtime = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
stime = Slider(axtime, 'Time', 0.0, 100.0, valinit=50.0)

#Routines to reset and update sliding bar
def reset(event):
    stime.reset()

def update(val):
    time = int(stime.val/100.* 256)
    im.set_array(XZslice[time,:,:].ravel())
    fig.canvas.draw()

#Bind sliding bar and reset button  
stime.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
button.on_clicked(reset)

plt.show()