How to Optimize Matplotlib Plotting Speed for Improved Performance?

Slow Plotting with Matplotlib

Evaluating various Python plotting libraries, you may encounter performance limitations with Matplotlib. Let's examine why and explore solutions to improve its speed.

Causes of Slowness

1. Complete Canvas Redraws: By default, calling fig.canvas.draw() redraws the entire canvas, including axes, labels, and other elements irrespective of what has changed. This is a time-consuming process.
2. Abundant Subplots and Tick Labels: Multiple subplots and numerous tick labels lead to a significant rendering time. Drawing these elements repeatedly can slow down the animation.

Blitting for Speed Enhancement

To mitigate these performance issues, consider implementing blitting. Blitting involves only redrawing the regions that have changed, leaving the rest unchanged. This dramatically improves performance without compromising visual quality.

GUI-Specific Blitting

If using a GUI toolkit, you can utilize GUI-specific blitting methods to achieve optimal speed. The preferred approach depends on the specific GUI being used.

GUI-Neutral Blitting

For scenarios without a specific GUI, you can implement GUI-neutral blitting using Matplotlib's restore_region() and blit() methods. This approach restores the previous canvas background, updates only the relevant data, and blits the changes to improve rendering speed.

Matplotlib Animations Module

Recent versions of Matplotlib provide a more convenient way of animating plots through the matplotlib.animation module. This module simplifies the blitting process, enabling you to create smooth and efficient animations.

By leveraging blitting techniques and employing the Animations module, you can significantly enhance the performance of Matplotlib plotting and achieve a more user-friendly interactive experience. However, it's important to consider that Matplotlib may not be the best choice for situations requiring real-time display due to its focus on publication-quality figures.

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