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. More information is available in the API description: animation
import matplotlib.pyplot as plt import numpy as np import matplotlib.animation as animationAnimation 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 typically requires these steps:
Plot the initial figure as you would in a static plot. Save all the created artists, which are returned by the plot functions, in variables so that you can access and modify them later in the animation function.
Create an animation function that updates the artists for a given frame. Typically, this calls set_* methods of the artists.
Create a FuncAnimation, passing the Figure and the animation function.
Save or show the animation using one of the following methods:
pyplot.show to show the animation in a window
Animation.to_html5_video to create a HTML <video> tag
Animation.to_jshtml to create HTML code with interactive JavaScript animation controls
Animation.save to save the animation to a file
The following table shows a few plotting methods, the artists they return and some commonly used set_* methods that update the underlying data. While updating data is the most common operation in animations, you can also update other aspects such as color or text position.
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 and ImageMagickWriter 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 and ImageMagickFileWriter 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.
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:
ani.save(filename="/tmp/pillow_example.gif", writer="pillow") ani.save(filename="/tmp/pillow_example.apng", writer="pillow")
HTML writers:
ani.save(filename="/tmp/html_example.html", writer="html") ani.save(filename="/tmp/html_example.htm", writer="html") ani.save(filename="/tmp/html_example.png", writer="html")
FFMpegWriter:
ani.save(filename="/tmp/ffmpeg_example.mkv", writer="ffmpeg") ani.save(filename="/tmp/ffmpeg_example.mp4", writer="ffmpeg") ani.save(filename="/tmp/ffmpeg_example.mjpeg", writer="ffmpeg")
Imagemagick writers:
ani.save(filename="/tmp/imagemagick_example.gif", writer="imagemagick")
ani.save(filename="/tmp/imagemagick_example.webp", writer="imagemagick")
ani.save(filename="apng:/tmp/imagemagick_example.apng",
writer="imagemagick", extra_args=["-quality", "100"])
(the extra_args for apng are needed to reduce filesize by ~10x)
Note that ffmpeg and imagemagick need to be separately installed. A cross-platform way to obtain ffmpeg is to install the imageio_ffmpeg PyPI package, and then to set rcParams["animation.ffmpeg_path"] = imageio_ffmpeg.get_ffmpeg_exe().
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