Why Blitting for performance in Matplotlib? - Purpose & Use Cases

The Big Idea

What if you could make your live graphs update instantly without slowing down your computer?

The Scenario

Imagine you are trying to update a live graph on your computer screen every second to show the latest temperature readings. You redraw the entire graph from scratch each time, even though only the temperature line changes.

The Problem

Redrawing the whole graph every second is slow and makes your computer lag. It wastes time and power because it refreshes parts of the image that never change, causing delays and a choppy experience.

The Solution

Blitting lets you update only the parts of the graph that change, like the temperature line, without redrawing the whole image. This makes updates much faster and smoother, saving time and computer resources.

Before vs After

✗ Before

ax.clear()
ax.plot(new_data)
plt.draw()

✓ After

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

What It Enables

Blitting enables smooth, real-time updates of complex plots without slowing down your program.

Real Life Example

In a weather app, blitting helps show live temperature changes on a graph instantly, making the display smooth and responsive.

Key Takeaways

Manual full redraws are slow and inefficient.

Blitting updates only changed parts for speed.

This improves real-time graph performance greatly.