Blitting for performance in Matplotlib - Step-by-Step Execution

Concept Flow - Blitting for performance

Create Figure & Axes

↓

Draw Static Background

↓

Save Background Image

↓

Update Dynamic Elements

↓

Restore Background

↓

Draw Updated Elements

↓

Blit to Screen

↓

Repeat Updates Efficiently

Blitting redraws only changing parts of a plot to speed up updates by saving and restoring the static background.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
line, = ax.plot([0], [0], 'ro')
fig.canvas.draw()  # draw the canvas once to initialize
background = fig.canvas.copy_from_bbox(ax.bbox)
line.set_data([1], [1])
fig.canvas.restore_region(background)
ax.draw_artist(line)
fig.canvas.blit(ax.bbox)

This code sets up a plot, saves the background, updates a point, restores background, redraws the point, and blits to update only the changed part.

Execution Table

Step	Action	Variable/Method	Effect	Output/State
1	Create figure and axes	plt.subplots()	Figure and axes objects created	fig, ax ready
2	Plot initial point	ax.plot([0], [0], 'ro')	Red dot at (0,0) drawn	line object created
3	Save background	fig.canvas.copy_from_bbox(ax.bbox)	Static background saved	background image stored
4	Update point data	line.set_data([1], [1])	Point data changed to (1,1)	line updated
5	Restore background	fig.canvas.restore_region(background)	Background restored, erasing old point	canvas reset to background
6	Draw updated point	ax.draw_artist(line)	New point drawn on restored background	line drawn at (1,1)
7	Blit to screen	fig.canvas.blit(ax.bbox)	Only updated area refreshed on screen	fast visual update
8	Repeat updates	Loop or event triggers steps 4-7	Efficient redraw of dynamic elements	smooth animation
9	Exit	No more updates	Stop blitting	final frame displayed

💡 Updates stop when no more changes are needed or animation ends

Variable Tracker

Variable	Start	After Step 3	After Step 4	After Step 5	After Step 6	After Step 7
fig	None	Figure created	Figure created	Figure created	Figure created	Figure created
ax	None	Axes created	Axes created	Axes created	Axes created	Axes created
line	None	Line at (0,0)	Line at (1,1)	Line at (1,1)	Line at (1,1)	Line at (1,1)
background	None	Saved background image	Saved background image	Saved background image	Saved background image	Saved background image

Key Moments - 3 Insights

Why do we save the background before updating the point?

Saving the background (step 3) lets us restore it later (step 5) to erase old drawings without redrawing the whole plot, making updates faster.

What happens if we skip restoring the background before drawing the updated point?

If we skip restoring (step 5), the old point remains visible, causing visual artifacts because the new point draws on top without clearing the old.

Why does blitting improve performance compared to redrawing the entire figure?

Blitting (step 7) updates only the changed area on screen, avoiding full redraws which are slower, so animations or updates run smoothly.

Visual Quiz - 3 Questions

Look at the execution table, what is the state of 'line' after step 4?

A Line at (0,0)

B Line removed

C Line at (1,1)

D Line not created yet

💡 Hint

Check the 'line' variable in variable_tracker after step 4

At which step is the background restored to erase old drawings?

A Step 4

B Step 5

C Step 3

D Step 7

💡 Hint

Look at the 'Action' column in execution_table for restoring background

If we do not call fig.canvas.blit(ax.bbox), what happens?

A The updated point is drawn but screen does not refresh efficiently

B The background is not saved

C The figure is closed

D The point data is not updated

💡 Hint

Refer to step 7 in execution_table about blitting effect

Concept Snapshot

Blitting redraws only changing parts of a plot.
Save static background once.
Restore background before drawing updates.
Draw updated elements.
Use canvas.blit() to refresh efficiently.
Speeds up animations and interactive plots.

Full Transcript

Blitting is a technique in matplotlib to speed up plot updates. First, we create a figure and axes, then draw static parts and save this background. When we want to update dynamic parts like a moving point, we restore the saved background to erase old drawings. Then we draw the updated elements and use blitting to refresh only the changed area on screen. This avoids redrawing the entire figure, making animations smooth and fast. The execution table shows each step from creating the plot, saving background, updating data, restoring background, drawing updated elements, and blitting to screen. Variable tracking shows how the line data changes from (0,0) to (1,1) while the background remains saved. Key moments clarify why saving and restoring background is important and how blitting improves performance. The visual quiz tests understanding of these steps and their effects.

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