Performance Considerations for Matplotlib Plotting

While evaluating different Python plotting libraries, you may encounter performance issues when using Matplotlib. This article explores why Matplotlib plotting can be slow and provides solutions to improve its speed.

Slowness Causes

Matplotlib's sluggish performance primarily stems from two factors:

• Frequent Redraws: Each time fig.canvas.draw() is called, it refreshes the entire figure, including elements like axes boundaries and tick labels. This process is computationally intensive.
• Numerous Subplots: Plots with multiple subplots featuring many tick labels can significantly slow down rendering.

Improving Performance

To enhance performance, consider the following strategies:

1. Use Blitting:

Blitting involves only updating a specific portion of the canvas instead of redrawing the entire figure. This dramatically reduces the computational overhead. Matplotlib provides backend-specific blitting methods that vary depending on the GUI framework used.

2. Restrict Redrawing:

Utilize the animated=True option when plotting. Combined with the Matplotlib animations module, this technique permits specific object updates without triggering a full canvas redraw.

3. Customize Subplots:

Minimize the number of subplots and tick labels. Remove unnecessary elements to reduce rendering time.

4. Enhance Code Efficiency:

Refactor your code to improve its structure and reduce the number of operations performed. Utilize vectorized operations where possible.

Example:

Here's an optimized version of the code provided in the question, using blitting with copy_from_bbox and restore_region:

import matplotlib.pyplot as plt
import numpy as np
import time

x = np.arange(0, 2*np.pi, 0.01)
y = np.sin(x)

fig, axes = plt.subplots(nrows=6)
fig.show()  # Draw the canvas initially

styles = ['r-', 'g-', 'y-', 'm-', 'k-', 'p-']
lines = [ax.plot(x, y, style)[0] for ax, style in zip(axes, styles)]

# Store background images of the axes
backgrounds = [fig.canvas.copy_from_bbox(ax.bbox) for ax in axes]

tstart = time.time()               
for i in range(1, 200):
    for j, line in enumerate(lines, start=1):
        # Restore the background
        fig.canvas.restore_region(backgrounds[j-1])
        
        # Update the data
        line.set_ydata(sin(j*x i/10.0))  

        # Draw the artist and blit
        ax.draw_artist(line)
        fig.canvas.blit(ax.bbox)

print('FPS:', 200/(time.time()-tstart))

Alternative Libraries

If Matplotlib's performance remains unsatisfactory, consider alternative plotting libraries such as Bokeh, Plotly, or Altair. These libraries prioritize real-time interactivity and performance optimization.