Animations

Purpose

Animations bring data to life by showing changes over time or through parameter space. They're useful for:

• Illustrating temporal evolution.
• Demonstrating algorithm convergence.
• Creating engaging presentations or educational content.

Example in the Notebook

The notebook uses Matplotlib's FuncAnimation to create a simple animated sine wave:

1. The wave oscillates horizontally.
2. The animation runs for 200 frames at 30 ms per frame.
3. It's displayed inline as HTML/JavaScript in the notebook.
4. It's also exported to MP4 (with ffmpeg) or GIF (with Pillow).

Key Code Snippet

from matplotlib import animation

fig, ax = plt.subplots()
x = np.linspace(0, 2*np.pi, 400)
line, = ax.plot(x, np.sin(x))
ax.set_ylim(-1.5, 1.5)

def init():
    line.set_ydata(np.sin(x))
    return (line,)

def animate(i):
    line.set_ydata(np.sin(x + i/10.0))
    return (line,)

anim = animation.FuncAnimation(fig, animate, init_func=init, frames=200, interval=30, blit=True)

Export Options

• Inline (Jupyter): Display directly with HTML(anim.to_jshtml()) (no external files).
• MP4: Requires ffmpeg. Use anim.save('out.mp4', writer='ffmpeg', fps=30).
• GIF: Requires Pillow. Use anim.save('out.gif', writer='pillow', fps=30).

Customization Tips

• Frame count: Increase frames=200 for longer animations.
• Speed: Adjust interval=30 (milliseconds between frames; smaller = faster).
• Blit mode: Set blit=True for faster rendering (only redraws changed elements).
• Complex animations: Use update_lines_and_patches() or custom update functions.
• Interactivity: Combine with Jupyter widgets (ipywidgets) for interactive parameter control.

When to Use

• Always: for time-series visualizations and pedagogical content.
• Consider: GIF for social media sharing (MP4 may have compatibility issues on some platforms).
• Avoid: animations for static reports (use stills or summaries instead).

See Also

• Matplotlib: FuncAnimation
• Examples: matplotlib.org/gallery/animation/