Eli Bendersky: Drawing animated GIFs with matplotlib

This is a brief post on how to draw animated GIFs with python using matplotlib . I tried the code shown here on a Ubuntu machine with ImageMagick installed. ImageMagick is required for matplotlib to render animated GIFs with the save method.

Here's a sample animated graph:

A couple of things to note:

The scatter part of the graph is unchanging; the line is changing. The X axis title is changing in each frame.

Here's the code that produces the above:

import sys import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation fig, ax = plt.subplots() fig.set_tight_layout(True) # Query the figure's on-screen size and DPI. Note that when saving the figure to # a file, we need to provide a DPI for that separately. print('fig size: {0} DPI, size in inches {1}'.format( fig.get_dpi(), fig.get_size_inches())) # Plot a scatter that persists (isn't redrawn) and the initial line. x = np.arange(0, 20, 0.1) ax.scatter(x, x + np.random.normal(0, 3.0, len(x))) line, = ax.plot(x, x - 5, 'r-', linewidth=2) def update(i): label = 'timestep {0}'.format(i) print(label) # Update the line and the axes (with a new xlabel). Return a tuple of # "artists" that have to be redrawn for this frame. line.set_ydata(x - 5 + i) ax.set_xlabel(label) return line, ax if __name__ == '__main__': # FuncAnimation will call the 'update' function for each frame; here # animating over 10 frames, with an interval of 200ms between frames. anim = FuncAnimation(fig, update, frames=np.arange(0, 10), interval=200) if len(sys.argv) > 1 and sys.argv[1] == 'save': anim.save('line.gif', dpi=80, writer='imagemagick') else: # plt.show() will just loop the animation forever. plt.show()

If you want a fancier theme, install the seaborn library and just add:

import seaborn

Then you'll get this image:

A word of warning on size: even though the GIFs I show here only have 10 frames and the graphics is very bare-bones, they weigh in at around 160K each. AFAIU, animated GIFs don't use cross-frame compression, which makes them very byte-hungry for longer frame sequences. Reducing the number of frames to the bare minimum and making the images smaller (by playing with the figure size and/or DPI in matplotlib ) can help alleviate the problem somewhat.