Note
Go to the end to download the full example code
Animations using Matplotlib#
Based on its plotting functionality, Matplotlib also provides an interface to
generate animations using the animation
module. An
animation is a sequence of frames where each frame corresponds to a plot on a
Figure
. This tutorial covers a general guideline on
how to create such animations and the different options available.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.animation as animation
Animation Classes#
The animation process in Matplotlib can be thought of in 2 different ways:
FuncAnimation
: Generate data for first frame and then modify this data for each frame to create an animated plot.ArtistAnimation
: Generate a list (iterable) of artists that will draw in each frame in the animation.
FuncAnimation
is more efficient in terms of
speed and memory as it draws an artist once and then modifies it. On the
other hand ArtistAnimation
is flexible as it
allows any iterable of artists to be animated in a sequence.
FuncAnimation
#
The FuncAnimation
class allows us to create an
animation by passing a function that iteratively modifies the data of a plot.
This is achieved by using the setter methods on various
Artist
(examples: Line2D
,
PathCollection
, etc.). A usual
FuncAnimation
object takes a
Figure
that we want to animate and a function
func that modifies the data plotted on the figure. It uses the frames
parameter to determine the length of the animation. The interval parameter
is used to determine time in milliseconds between drawing of two frames.
Animating using FuncAnimation
would usually follow the following
structure:
Plot the initial figure, including all the required artists. Save all the artists in variables so that they can be updated later on during the animation.
Create an animation function that updates the data in each artist to generate the new frame at each function call.
Create a
FuncAnimation
object with theFigure
and the animation function, along with the keyword arguments that determine the animation properties.Use
animation.Animation.save
orpyplot.show
to save or show the animation.
The update function uses the set_*
function for different artists to
modify the data. The following table shows a few plotting methods, the artist
types they return and some methods that can be used to update them.
Plotting method |
Artist |
Set method |
---|---|---|
|
||
|
||
Covering the set methods for all types of artists is beyond the scope of this
tutorial but can be found in their respective documentations. An example of
such update methods in use for Axes.scatter
and Axes.plot
is as follows.
fig, ax = plt.subplots()
t = np.linspace(0, 3, 40)
g = -9.81
v0 = 12
z = g * t**2 / 2 + v0 * t
v02 = 5
z2 = g * t**2 / 2 + v02 * t
scat = ax.scatter(t[0], z[0], c="b", s=5, label=f'v0 = {v0} m/s')
line2 = ax.plot(t[0], z2[0], label=f'v0 = {v02} m/s')[0]
ax.set(xlim=[0, 3], ylim=[-4, 10], xlabel='Time [s]', ylabel='Z [m]')
ax.legend()
def update(frame):
# for each frame, update the data stored on each artist.
x = t[:frame]
y = z[:frame]
# update the scatter plot:
data = np.stack([x, y]).T
scat.set_offsets(data)
# update the line plot:
line2.set_xdata(t[:frame])
line2.set_ydata(z2[:frame])
return (scat, line2)
ani = animation.FuncAnimation(fig=fig, func=update, frames=40, interval=30)
plt.show()
ArtistAnimation
#
ArtistAnimation
can be used
to generate animations if there is data stored on various different artists.
This list of artists is then converted frame by frame into an animation. For
example, when we use Axes.barh
to plot a bar-chart, it creates a number of
artists for each of the bar and error bars. To update the plot, one would
need to update each of the bars from the container individually and redraw
them. Instead, animation.ArtistAnimation
can be used to plot each frame
individually and then stitched together to form an animation. A barchart race
is a simple example for this.
fig, ax = plt.subplots()
rng = np.random.default_rng(19680801)
data = np.array([20, 20, 20, 20])
x = np.array([1, 2, 3, 4])
artists = []
colors = ['tab:blue', 'tab:red', 'tab:green', 'tab:purple']
for i in range(20):
data += rng.integers(low=0, high=10, size=data.shape)
container = ax.barh(x, data, color=colors)
artists.append(container)
ani = animation.ArtistAnimation(fig=fig, artists=artists, interval=400)
plt.show()
Animation Writers#
Animation objects can be saved to disk using various multimedia writers (ex: Pillow, ffpmeg, imagemagick). Not all video formats are supported by all writers. There are 4 major types of writers:
PillowWriter
- Uses the Pillow library to create the animation.HTMLWriter
- Used to create JavaScript-based animations.Pipe-based writers -
FFMpegWriter
andImageMagickWriter
are pipe based writers. These writers pipe each frame to the utility (ffmpeg / imagemagick) which then stitches all of them together to create the animation.File-based writers -
FFMpegFileWriter
andImageMagickFileWriter
are examples of file-based writers. These writers are slower than their pipe-based alternatives but are more useful for debugging as they save each frame in a file before stitching them together into an animation.
Saving Animations#
Writer |
Supported Formats |
---|---|
.gif, .apng, .webp |
|
.htm, .html, .png |
|
All formats supported by ffmpeg: |
|
All formats supported by imagemagick: |
To save animations using any of the writers, we can use the
animation.Animation.save
method. It takes the filename that we want to
save the animation as and the writer, which is either a string or a writer
object. It also takes an fps argument. This argument is different than the
interval argument that FuncAnimation
or
ArtistAnimation
uses. fps determines the frame rate that the
saved animation uses, whereas interval determines the frame rate that
the displayed animation uses.
Below are a few examples that show how to save an animation with different writers.
Pillow writers:
HTML writers:
FFMpegWriter:
Imagemagick writers:
(the extra_args
for apng are needed to reduce filesize by ~10x)
Total running time of the script: (0 minutes 3.210 seconds)