Animation update function in Matplotlib - Time & Space Complexity
When creating animations with matplotlib, the update function runs many times to redraw frames.
We want to know how the time to update grows as the animation length or data size changes.
Analyze the time complexity of the following animation update function.
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
fig, ax = plt.subplots()
line, = ax.plot([], [])
xdata, ydata = [], []
def update(frame):
xdata.append(frame)
ydata.append(frame ** 2)
line.set_data(xdata, ydata)
return line,
ani = FuncAnimation(fig, update, frames=range(1000), blit=True)
plt.show()
This code updates a line plot by adding one point each frame and redraws the line.
Look at what repeats every time the update function runs.
- Primary operation: Appending one new point and updating the line data.
- How many times: Once per frame, for all frames (e.g., 1000 times).
Each frame adds one point and redraws the entire line with all points so far.
| Input Size (frames) | Approx. Operations |
|---|---|
| 10 | About 55 (1+2+...+10) |
| 100 | About 5050 |
| 1000 | About 500,500 |
Pattern observation: The total work grows roughly like the square of the number of frames.
Time Complexity: O(n^2)
This means the total time to run all updates grows roughly with the square of the number of frames.
[X] Wrong: "Each update takes the same small time, so total time is just O(n)."
[OK] Correct: Each update redraws all points so far, so the work per frame grows as more points are added.
Understanding how repeated updates affect performance helps you write smoother animations and shows you can analyze code beyond just loops.
What if the update function only updated the newest point instead of all points? How would the time complexity change?