Resolving the tkinter.TclError When Using matplotlib in Real-Time Data Processing

Learn how to fix the common error of `tkinter.TclError: can't invoke "update" command: application has been destroyed` when using `matplotlib` during real-time data visualization with Python.
---
This video is based on the question https://stackoverflow.com/q/66635406/ asked by the user 'baihakhi' ( https://stackoverflow.com/u/6230048/ ) and on the answer https://stackoverflow.com/a/67168087/ provided by the user 'baihakhi' ( https://stackoverflow.com/u/6230048/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: _tkinter.TclError: can't invoke “update” command: application has been destroyed error on pyplot.pause()

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Resolving the tkinter.TclError When Using matplotlib in Real-Time Data Processing

If you're working with real-time data visualization in Python using the matplotlib library for displaying data from sources like Arduino and the opencv library for video processing, you may have encountered a frustrating error. Many users have faced the tkinter.TclError: can't invoke "update" command: application has been destroyed when closing figure windows. This guide will introduce the problem and provide a step-by-step guide to solve it.

Understanding the Problem

The error often arises when you run a loop with data visualization that includes an application interface like tkinter, even if you haven't explicitly imported tkinter. Here's what typically happens:

You create an application that visualizes data in real-time using plots generated by matplotlib.

When you close the figure window, an attempt to call an update command from tkinter fails, resulting in an error.

This can occur even without directly using tkinter, as matplotlib may implicitly create a tkinter application when you call its plotting functions.

Breakdown of the Solution

Adjusting the Loop for Real-Time Visualization

To prevent the tkinter error, one effective approach is to rearrange the order of operations within your main data-processing loop. Here are the steps you can follow:

Move the Quit Check Above the Pause Call: Instead of putting your key-checking condition after the plt.pause() function, place it before. This allows you to break the loop and cleanly exit the application before the pause function is invoked.

Code Example: Here’s the adjusted version of your code that should help eliminate the error:

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

Key takeaways:

Rearranging Logic: By checking for the exit condition (if key == ord('q'): break) before pausing, you ensure that the loop can terminate correctly without invoking tkinter update commands after closure.

Experiment and Adjust: If you continue facing problems, you can tweak other related parts of your code, or consider debugging to check if any other part of your setup indirectly relies on tkinter.

Should You Use Tkinter?

In this scenario, it's not necessary to import or use the tkinter library directly. The changes above should be sufficient to resolve your issue without additional libraries.

Conclusion

Real-time data visualization with Python can be complex, especially when integrating multiple libraries. However, with a clear understanding of the problem and steps to adjust your code, you can effectively eliminate common errors like the tkinter.TclError.

By following the adjusted loop method outlined in this guide, you should have a smoother experience while working with your data visualization project.

Feel free to ask questions or share your experiences in the comments below! Happy coding!