Blitting for performance in Matplotlib - Deep Dive

Overview - Blitting for performance

What is it?

Blitting is a technique used in matplotlib to speed up the process of updating plots by only redrawing the parts of the plot that change. Instead of redrawing the entire figure every time, blitting copies a saved background and updates only the moving or changing elements. This makes animations and interactive plots much faster and smoother.

Why it matters

Without blitting, updating plots can be slow and laggy, especially with complex or large datasets. This can make interactive visualizations frustrating or unusable. Blitting solves this by minimizing the amount of drawing work, improving user experience and enabling real-time data visualization.

Where it fits

Before learning blitting, you should understand basic matplotlib plotting and how animations work. After mastering blitting, you can explore advanced interactive visualizations, real-time data streaming, and performance optimization techniques in plotting.

Mental Model

Core Idea

Blitting speeds up plot updates by reusing a saved background and only redrawing the parts that change.

Think of it like...

Imagine you have a photo with a sticky note on it. Instead of taking a new photo every time you move the sticky note, you keep the photo and just move the sticky note around on top. Blitting is like moving the sticky note without retaking the whole photo.

┌─────────────────────────────┐
│ Full plot drawn once        │
│                             │
│ ┌───────────────┐           │
│ │ Save background│          │
│ └───────────────┘           │
│                             │
│ ┌───────────────┐           │
│ │ Draw moving   │           │
│ │ elements only │           │
│ └───────────────┘           │
│                             │
│ ┌───────────────┐           │
│ │ Copy background│          │
│ │ + redraw moving│          │
│ │ elements      │           │
│ └───────────────┘           │
└─────────────────────────────┘

Build-Up - 6 Steps

1

Foundation

Basic matplotlib plot drawing

Concept: Learn how matplotlib draws static plots by rendering all elements every time.

When you create a plot in matplotlib, it draws all parts like axes, lines, labels, and markers from scratch. For example, plt.plot() draws the entire figure once and shows it.

Result

A static plot appears on the screen with all elements drawn.

Understanding that matplotlib redraws everything by default helps see why updating plots repeatedly can be slow.

2

Foundation

Introduction to matplotlib animation

Concept: Animations update plots by redrawing frames repeatedly, usually by clearing and redrawing everything.

Using FuncAnimation, matplotlib calls a function repeatedly to update the plot. Each frame redraws the entire figure or axes, which can be slow for complex plots.

Result

An animation plays but may be slow or flicker due to full redraws.

Knowing that full redraws cause slow animations sets the stage for optimization.

3

Intermediate

What is blitting in matplotlib

🤔Before reading on: do you think blitting redraws the whole plot or only parts? Commit to your answer.

Concept: Blitting copies a saved background and only redraws the parts that change, improving speed.

Blitting saves the static parts of the plot as a background image. When updating, it restores this background and draws only the moving elements on top. This avoids redrawing static parts every frame.

Result

Animations run faster and smoother with less flicker.

Understanding blitting reveals how selective redrawing can drastically improve performance.

4

Intermediate

How to implement blitting in matplotlib

🤔Before reading on: do you think blitting requires special functions or just a flag? Commit to your answer.

Concept: Blitting requires saving the background and updating only changed artists using specific matplotlib methods.

You use canvas.copy_from_bbox() to save the background. Then, in each frame, restore it with canvas.restore_region(), draw updated artists, and call canvas.blit() to update the display. This requires manual control in the animation function.

Result

A working animation that updates only changed parts efficiently.

Knowing the exact steps to save and restore background is key to using blitting correctly.

5

Advanced

Limitations and challenges of blitting

🤔Before reading on: do you think blitting works automatically with all plot types? Commit to your answer.

Concept: Blitting does not work with all plot elements and requires careful management of what to redraw.

Some plot elements like text, legends, or complex widgets may not support blitting well. Also, resizing the window or changing axes can invalidate the saved background. You must manage these cases manually.

Result

Knowing when blitting may fail or need extra handling.

Understanding blitting's limits prevents frustration and helps design robust animations.

6

Expert

Blitting internals and performance impact

🤔Before reading on: do you think blitting reduces CPU, GPU, or both? Commit to your answer.

Concept: Blitting reduces CPU load by minimizing redraw calls and leverages fast image copying, improving overall rendering speed.

Internally, blitting uses fast memory operations to copy pixel data instead of recomputing the entire plot. This reduces CPU cycles spent on drawing and can also reduce GPU load by limiting draw calls. It is especially effective for animations with small changing regions.

Result

Animations with blitting consume less CPU and run at higher frame rates.

Knowing how blitting optimizes low-level rendering clarifies why it is a powerful performance tool.

Under the Hood

Blitting works by saving a pixel snapshot of the static plot area (background) and restoring it quickly before drawing only the updated parts (artists). This avoids recomputing and redrawing the entire scene. The canvas.blit() method then efficiently updates the display with the new pixels. This process leverages fast memory copy operations and reduces CPU and GPU workload.

Why designed this way?

Blitting was designed to solve slow redraws in interactive and animated plots. Early matplotlib animations were slow because they redrew everything. Blitting uses a classic graphics optimization technique from video games and GUI systems, adapted to matplotlib's drawing model, to improve speed without changing the core rendering pipeline.

┌───────────────┐
│ Initial plot  │
│ drawn fully   │
└──────┬────────┘
       │ Save background (pixels)
       ▼
┌───────────────┐
│ Background    │
│ image stored  │
└──────┬────────┘
       │ For each frame:
       ▼
┌───────────────┐       ┌───────────────┐
│ Restore       │◄──────│ Saved         │
│ background    │       │ background    │
└──────┬────────┘       └───────────────┘
       │ Draw updated │
       │ artists     │
       ▼             │
┌───────────────┐    │
│ canvas.blit() │────┘
│ updates screen│
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does blitting redraw the entire figure every frame? Commit yes or no.

Common Belief: Blitting redraws the whole plot faster by optimizing the drawing code.

Reality: Blitting only redraws the parts that change, reusing a saved background for static parts.

Why it matters: Believing it redraws everything leads to misunderstanding performance gains and misusing blitting.

Quick: Can blitting be used automatically without manual code? Commit yes or no.

Common Belief: Blitting is automatic in matplotlib animations and requires no extra code.

Reality: Blitting requires explicit code to save and restore backgrounds and update only changed artists.

Why it matters: Expecting automatic blitting causes confusion when animations remain slow or flicker.

Quick: Does blitting work perfectly with all plot elements like legends and text? Commit yes or no.

Common Belief: Blitting works seamlessly with all matplotlib plot elements.

Reality: Some elements like text and legends may not support blitting well and need special handling.

Why it matters: Ignoring this causes broken or flickering visuals in animations.

Quick: Does blitting reduce CPU usage only, or also GPU? Commit your answer.

Common Belief: Blitting only reduces CPU usage by skipping redraws.

Reality: Blitting reduces both CPU and GPU load by minimizing draw calls and pixel updates.

Why it matters: Knowing this helps optimize animations on different hardware.

Expert Zone

1

Blitting requires careful management of the drawing order and which artists are updated to avoid visual glitches.

2

The saved background must be updated if the plot layout changes, such as resizing or zooming, or blitting will fail.

3

Blitting is less effective for plots where large areas change every frame, as the overhead of saving/restoring background can outweigh benefits.

When NOT to use

Avoid blitting when your plot changes completely every frame or includes complex widgets that do not support partial redraws. Instead, use full redraws or specialized animation libraries like Plotly or Bokeh for interactive visuals.

Production Patterns

In production, blitting is used in real-time monitoring dashboards, scientific simulations, and interactive data exploration tools where smooth, fast updates are critical. Developers combine blitting with event-driven updates and hardware acceleration for best results.

Connections

Double buffering in computer graphics

Blitting is a form of double buffering where one buffer is saved and reused to avoid flicker.

Understanding double buffering in graphics helps grasp why blitting reduces flicker and improves animation smoothness.

Caching in web development

Both blitting and caching store previous results to avoid repeating expensive work.

Knowing caching principles clarifies why saving a background image speeds up repeated plot updates.

Incremental rendering in video games

Blitting is similar to incremental rendering where only changed screen parts are redrawn each frame.

Recognizing this connection shows how performance techniques cross domains from gaming to data visualization.

Common Pitfalls

#1 Not saving the background before animation starts.

Wrong approach:

def update(frame):
line.set_ydata(data[frame])
fig.canvas.blit(fig.bbox)

ani = FuncAnimation(fig, update, frames=range(len(data)))

Correct approach:

background = fig.canvas.copy_from_bbox(fig.bbox)
def update(frame):
fig.canvas.restore_region(background)
line.set_ydata(data[frame])
ax.draw_artist(line)
fig.canvas.blit(fig.bbox)

ani = FuncAnimation(fig, update, frames=range(len(data)))

Root cause:

Forgetting to save the background means restore_region has nothing to restore, causing flicker and slow redraws.

#2 Trying to blit plot elements that do not support it, like legends.

Wrong approach:

fig.canvas.restore_region(background)
legend.draw(fig.canvas.get_renderer())
fig.canvas.blit(fig.bbox)

Correct approach:

Avoid updating legends in blitting or redraw the entire figure when legend changes.

Root cause:

Some artists are not compatible with blitting and require full redraws.

#3 Not calling draw_artist() on updated artists before blitting.

Wrong approach:

fig.canvas.restore_region(background)
line.set_ydata(new_data)
fig.canvas.blit(fig.bbox)

Correct approach:

fig.canvas.restore_region(background)
line.set_ydata(new_data)
ax.draw_artist(line)
fig.canvas.blit(fig.bbox)

Root cause:

Without draw_artist(), the updated artist is not rendered onto the canvas before blitting.

Key Takeaways

Blitting is a powerful technique that speeds up matplotlib animations by redrawing only changed parts of the plot.

It works by saving a background image of static plot elements and restoring it before drawing updates.

Using blitting requires explicit code to save and restore backgrounds and to draw updated artists properly.

Blitting improves performance by reducing CPU and GPU load, enabling smooth real-time visualizations.

However, it has limitations and requires careful handling of plot elements and layout changes.