matplotlib.animation — Matplotlib 3.10.3 documentation (original) (raw)
Table of Contents
Animation#
The easiest way to make a live animation in Matplotlib is to use one of theAnimation classes.
In both cases it is critical to keep a reference to the instance object. The animation is advanced by a timer (typically from the host GUI framework) which the Animation object holds the only reference to. If you do not hold a reference to the Animation object, it (and hence the timers) will be garbage collected which will stop the animation.
To save an animation use Animation.save, Animation.to_html5_video, or Animation.to_jshtml.
See Helper Classes below for details about what movie formats are supported.
FuncAnimation#
The inner workings of FuncAnimation is more-or-less:
for d in frames: artists = func(d, *fargs) fig.canvas.draw_idle() fig.canvas.start_event_loop(interval)
with details to handle 'blitting' (to dramatically improve the live performance), to be non-blocking, not repeatedly start/stop the GUI event loop, handle repeats, multiple animated axes, and easily save the animation to a movie file.
'Blitting' is a standard technique in computer graphics. The general gist is to take an existing bit map (in our case a mostly rasterized figure) and then 'blit' one more artist on top. Thus, by managing a saved 'clean' bitmap, we can only re-draw the few artists that are changing at each frame and possibly save significant amounts of time. When we use blitting (by passing blit=True), the core loop ofFuncAnimation gets a bit more complicated:
ax = fig.gca()
def update_blit(artists): fig.canvas.restore_region(bg_cache) for a in artists: a.axes.draw_artist(a)
ax.figure.canvas.blit(ax.bbox)artists = init_func()
for a in artists: a.set_animated(True)
fig.canvas.draw() bg_cache = fig.canvas.copy_from_bbox(ax.bbox)
for f in frames: artists = func(f, *fargs) update_blit(artists) fig.canvas.start_event_loop(interval)
This is of course leaving out many details (such as updating the background when the figure is resized or fully re-drawn). However, this hopefully minimalist example gives a sense of how init_funcand func are used inside of FuncAnimation and the theory of how 'blitting' works.
Note
The zorder of artists is not taken into account when 'blitting' because the 'blitted' artists are always drawn on top.
The expected signature on func and init_func is very simple to keep FuncAnimation out of your book keeping and plotting logic, but this means that the callable objects you pass in must know what artists they should be working on. There are several approaches to handling this, of varying complexity and encapsulation. The simplest approach, which works quite well in the case of a script, is to define the artist at a global scope and let Python sort things out. For example:
import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation
fig, ax = plt.subplots() xdata, ydata = [], [] ln, = ax.plot([], [], 'ro')
def init(): ax.set_xlim(0, 2*np.pi) ax.set_ylim(-1, 1) return ln,
def update(frame): xdata.append(frame) ydata.append(np.sin(frame)) ln.set_data(xdata, ydata) return ln,
ani = FuncAnimation(fig, update, frames=np.linspace(0, 2*np.pi, 128), init_func=init, blit=True) plt.show()
The second method is to use functools.partial to pass arguments to the function:
import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation from functools import partial
fig, ax = plt.subplots() line1, = ax.plot([], [], 'ro')
def init(): ax.set_xlim(0, 2*np.pi) ax.set_ylim(-1, 1) return line1,
def update(frame, ln, x, y): x.append(frame) y.append(np.sin(frame)) ln.set_data(x, y) return ln,
ani = FuncAnimation( fig, partial(update, ln=line1, x=[], y=[]), frames=np.linspace(0, 2*np.pi, 128), init_func=init, blit=True)
plt.show()
A third method is to use closures to build up the required artists and functions. A fourth method is to create a class.
Examples#
- Decay
- The Bayes update
- The double pendulum problem
- Animated histogram
- Rain simulation
- Animated 3D random walk
- Animated line plot
- Oscilloscope
- Matplotlib unchained
ArtistAnimation#
Examples#
Writer Classes#
The provided writers fall into a few broad categories.
The Pillow writer relies on the Pillow library to write the animation, keeping all data in memory.
The HTML writer generates JavaScript-based animations.
The pipe-based writers stream the captured frames over a pipe to an external process. The pipe-based variants tend to be more performant, but may not work on all systems.
The file-based writers save temporary files for each frame which are stitched into a single file at the end. Although slower, these writers can be easier to debug.
The writer classes provide a way to grab sequential frames from the same underlying Figure. They all provide three methods that must be called in sequence:
- setup prepares the writer (e.g. opening a pipe). Pipe-based and file-based writers take different arguments to
setup(). - grab_frame can then be called as often as needed to capture a single frame at a time
- finish finalizes the movie and writes the output file to disk.
Example:
moviewriter = MovieWriter(...) moviewriter.setup(fig, 'my_movie.ext', dpi=100) for j in range(n): update_figure(j) moviewriter.grab_frame() moviewriter.finish()
If using the writer classes directly (not through Animation.save), it is strongly encouraged to use the saving context manager:
with moviewriter.saving(fig, 'myfile.mp4', dpi=100): for j in range(n): update_figure(j) moviewriter.grab_frame()
to ensure that setup and cleanup are performed as necessary.
Examples#
Helper Classes#
Animation Base Classes#
Writer Registry#
A module-level registry is provided to map between the name of the writer and the class to allow a string to be passed toAnimation.save instead of a writer instance.
Writer Base Classes#
To reduce code duplication base classes
and mixins
are provided.
See the source code for how to easily implement new MovieWriter classes.