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.

Practice

1. What is the main purpose of blitting in matplotlib?

easy

A. To redraw only the changed parts of a plot for faster updates

B. To create 3D plots from 2D data

C. To save plots as image files

D. To change the color scheme of a plot

Solution

1. Step 1: Understand what blitting does

   Blitting redraws only the parts of the plot that change, instead of the whole plot.

2. Step 2: Compare options

   Options B, C, and D describe unrelated tasks like 3D plotting, saving files, or color changes.

3. Final Answer:

   To redraw only the changed parts of a plot for faster updates -> Option A

4. Quick Check:

   Blitting = redraw changed parts only [OK]

Hint: Blitting means updating only what changes fast [OK]

Common Mistakes:

• Thinking blitting saves plots as files
• Confusing blitting with changing colors
• Assuming blitting creates 3D plots

2. Which of the following is the correct way to save the background region for blitting in matplotlib?

easy

A. background = ax.copy_from_bbox(fig.bbox)

B. background = fig.canvas.copy_from_bbox(ax.bbox)

C. background = ax.copy_from_bbox(ax.bbox)

D. background = fig.copy_from_bbox(ax.bbox)

Solution

1. Step 1: Identify correct method usage

   The copy_from_bbox method is called on the figure canvas (fig.canvas) with the axes bounding box (ax.bbox).

2. Step 2: Check options carefully

   background = fig.canvas.copy_from_bbox(ax.bbox) is correct. Options B, C call it on ax (which lacks the method), D calls it on fig (missing .canvas), B also uses wrong bbox.

3. Final Answer:

   background = fig.canvas.copy_from_bbox(ax.bbox) -> Option B

4. Quick Check:

   copy_from_bbox called on fig.canvas with ax.bbox [OK]

Hint: copy_from_bbox called on fig.canvas with ax.bbox [OK]

Common Mistakes:

• Calling copy_from_bbox on ax instead of fig.canvas
• Using fig.bbox instead of ax.bbox
• Mixing up ax and fig in method calls

3. What will the following code print?

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
line, = ax.plot([0, 1], [0, 1])
background = fig.canvas.copy_from_bbox(ax.bbox)
line.set_ydata([1, 0])
fig.canvas.restore_region(background)
ax.draw_artist(line)
print(line.get_ydata())

medium

A. [1 0]

B. [0 1]

C. [0 0]

D. Error: restore_region not found

Solution

1. Step 1: Trace the code changes

   The line's y-data is changed to [1, 0] using set_ydata.

2. Step 2: Understand blitting steps

   The background is restored, then the updated line is drawn. The line's data remains [1, 0].

3. Final Answer:

   [1 0] -> Option A

4. Quick Check:

   set_ydata changes line data to [1 0] [OK]

Hint: set_ydata changes data; restore_region redraws background [OK]

Common Mistakes:

• Assuming restore_region resets line data
• Confusing line data with original plot data
• Expecting an error from restore_region

4. You try to use blitting but your plot does not update visually after calling draw_artist. What is the most likely mistake?

medium

A. You called copy_from_bbox after draw_artist

B. You did not call plt.show() at the end

C. You used restore_region before copy_from_bbox

D. You forgot to call fig.canvas.blit(ax.bbox) after draw_artist

Solution

1. Step 1: Understand blitting update steps

   After drawing the updated artist, you must call fig.canvas.blit(ax.bbox) to update the screen.

2. Step 2: Analyze options

   You forgot to call fig.canvas.blit(ax.bbox) after draw_artist correctly identifies the missing blit call. Options A and C describe incorrect method orders. You did not call plt.show() at the end is unrelated if running in interactive mode.

3. Final Answer:

   You forgot to call fig.canvas.blit(ax.bbox) after draw_artist -> Option D

4. Quick Check:

   Missing canvas.blit call stops visual update [OK]

Hint: Always call canvas.blit after draw_artist to update [OK]

Common Mistakes:

• Not calling canvas.blit after draw_artist
• Calling copy_from_bbox too late
• Confusing restore_region order
• Assuming plt.show fixes blitting updates

5. You want to animate a scatter plot with 1000 points updating their positions in real-time. Which approach using blitting will give the best performance?

hard

A. Only update the figure title text each frame using blitting

B. Redraw the entire scatter plot from scratch each frame without blitting

C. Save background with copy_from_bbox, update scatter offsets, restore background, draw scatter, then call canvas.blit

D. Use plt.pause() inside a loop without blitting

Solution

1. Step 1: Identify efficient blitting steps for animation

   Best practice is to save the background once, then restore it each frame, update only the scatter points, draw them, and call canvas.blit.

2. Step 2: Compare other options

   Redraw the entire scatter plot from scratch each frame without blitting redraws everything, which is slow. Only update the figure title text each frame using blitting updates only title text, not points. Use plt.pause() inside a loop without blitting uses pause without blitting, which is less efficient.

3. Final Answer:

   Save background with copy_from_bbox, update scatter offsets, restore background, draw scatter, then call canvas.blit -> Option C

4. Quick Check:

   Blitting updates only changed scatter points fast [OK]

Hint: Save background once, restore, update points, then blit [OK]

Common Mistakes:

• Redrawing entire plot each frame
• Updating only title text instead of points
• Using plt.pause without blitting for speed