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Discover how to keep `matplotlib` images in memory to optimize plotting speed and reduce save times when generating multiple plots in Python.
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Efficiently Store matplotlib Images in Memory for Faster Plotting

When working with data visualization in Python using matplotlib, you might encounter a situation where generating multiple plots can significantly slow down your program—especially if you’re saving images for each plot. This can become problematic when you're performing calculations in a loop, plotting results, scoring them, and then later aiming to generate a single HTML file with the saved images. But fear not! In this post, we'll explore how to keep matplotlib images in memory until you're ready to plot them, speeding up your overall process.

The Problem: Slow Plotting and Saving

Imagine you're running a loop where each iteration involves:

Performing a calculation.

Plotting the results.

Assigning a score to each plot based on the results.

At the end of the loop, you sort the plots by their scores and decide which images to keep. The challenge emerges from having to repeatedly save each image to disk immediately after plotting, which slows down the entire operation.

Example Scenario

Let's consider a simplified version of your task:

You generate 50 plots with random scores.

Each plot is immediately saved to the filesystem as a PNG file.

Here’s a snippet of code that illustrates this:

[[See Video to Reveal this Text or Code Snippet]]

While functional, this approach has substantial downsides due to the performance hit from frequent disk I/O operations.

The Solution: In-Memory Storage for Images

Instead of saving each plot immediately, you can store the figures in memory, retrieve them later when you need to save the best-performing plots. This can be accomplished by modifying the figure assignment within the loop. Let’s walk through an improved solution.

Step-by-Step Implementation

Create Figures with Specific Identifiers: Assign each plot a unique identifier.

Store Plots in Memory: Keep track of all the images in memory instead of saving them right away.

Sort and Save Only the Top Scoring Plots: After scoring, only save the top plots.

Here is how you can implement it:

[[See Video to Reveal this Text or Code Snippet]]

Key Benefits of This Approach

Improved Performance: By keeping figures in memory, you reduce the repeated disk operations, allowing your plots to be created and accessed quickly.

Flexibility: Since you're scoring and sorting in-memory, you can easily adjust how many plots you want to save without the penalty of unnecessary I/O.

Simplicity: You use existing matplotlib functionality and avoid custom caching mechanisms.

Conclusion

Storing matplotlib figures in memory until you're ready to save them can significantly optimize your plotting workflow. By following the outlined method above, you'll have a more efficient code structure that allows for faster processing and plotting with minimal delays. Next time you're generating plots in Python, consider using this in-memory approach to elevate your data visualization experience!