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:
importsysimportnumpyasnpimportmatplotlib.pyplotaspltfrommatplotlib.animationimportFuncAnimationfig,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)defupdate(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)returnline,axif__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)iflen(sys.argv)>1andsys.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:
importseabornThen 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.