Discrete colorbars in Matplotlib - Time & Space Complexity
We want to understand how the time to create a discrete colorbar changes as we increase the number of color segments.
How does adding more color steps affect the work matplotlib does?
Analyze the time complexity of the following code snippet.
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import BoundaryNorm
levels = np.linspace(0, 100, 11) # 10 intervals
cmap = plt.get_cmap('viridis', len(levels) - 1)
norm = BoundaryNorm(levels, ncolors=cmap.N)
fig, ax = plt.subplots()
cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax)
plt.show()
This code creates a discrete colorbar with 10 color segments using matplotlib.
Identify the loops, recursion, array traversals that repeat.
- Primary operation: Generating and drawing each color segment in the colorbar.
- How many times: Once for each color segment, which equals the number of intervals (here 10).
As the number of color segments increases, matplotlib draws more rectangles and labels for the colorbar.
| Input Size (n) | Approx. Operations |
|---|---|
| 10 | 10 drawing steps |
| 100 | 100 drawing steps |
| 1000 | 1000 drawing steps |
Pattern observation: The work grows directly with the number of color segments.
Time Complexity: O(n)
This means the time to create the discrete colorbar grows linearly with the number of color segments.
[X] Wrong: "Adding more color segments does not affect the drawing time much because it's just one colorbar."
[OK] Correct: Each color segment requires separate drawing and labeling, so more segments mean more work.
Understanding how visual elements scale with input size helps you reason about performance in data visualization tasks.
What if we changed the colorbar to use a continuous colormap instead of discrete segments? How would the time complexity change?