Running Two Python Files at Once: A Guide to Parallel Processing with Pygame and Matplotlib

Learn how to effectively run `two Python files simultaneously` using multithreading and explore alternatives for seamless graphical data representation alongside your simulation.
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Running Two Python Files at Once: A Guide to Parallel Processing with Pygame and Matplotlib

If you have ever found yourself wanting to run two Python scripts concurrently, you're not alone. Many developers encounter situations where they need to execute multiple scripts simultaneously for various purposes, such as running a simulation while graphing data. This is especially prevalent in gaming, data analysis, and scientific simulations. In this guide, we will tackle the problem of running two Python files at once, specifically when one file runs a Pygame simulation and the other processes and graphs data.

The Challenge

The challenge here lies in the need to have a simulation running along with a data visualization tool in real-time. Instead of running in a single window, you want both tools to operate simultaneously while allowing data from the simulation to be logged and displayed graphically. However, multithreading can pose complications, especially when both scripts use while True loops to continuously update their respective outputs.

In your case, your initial implementation used threading to launch both scripts but faced difficulties in achieving the desired outcome.

Here’s a brief look at your code implementation:

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

In addition, your graphData() function was designed to plot data continuously, but the reliance on threads made the task trickier than expected.

Proposed Solution

After experimenting with multithreading and hitting a wall, you discovered a workaround that may address your concerns adequately. Here’s a breakdown of that solution:

Option 1: Simplifying with a Single Window

Instead of enforcing multithreading, consider rendering the graph within the same window as the Pygame simulation. This approach allows you to maintain control over both outputs without worrying about thread conflicts. Here’s how this can be done:

Integrate Graphing within the Simulation Loop:

You can call the graph rendering function directly after updating the Pygame window.

By doing this, you ensure that your graph updates in sync with the simulation, avoiding complications that may arise from unable to share resources across threads.

Use Matplotlib’s Animation:

Instead of constantly opening new figures or windows, use Matplotlib’s animation capabilities to update your plots dynamically. This minimizes the graphical overhead and optimizes the display.

Example of Combined Code

Here’s a simplified approach to render the data while keeping the simulation running smoothly:

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

Benefits of This Method

Simplicity: By avoiding multithreading, you reduce complexity and potential errors in your code.

Real-time Visualization: Keeping both simulations and data visualization in a synchronized manner ensures that your data is always up-to-date.

Ease of Debugging: With a single flow of control, debugging becomes more straightforward.

Conclusion

Running two Python files at once can be challenging, especially when both require continuous updates in parallel. However, by simplifying your approach and integrating visualization directly within your main simulation loop, you can achieve a seamless execution. Remember that while multithreading can be powerful, simplicity often leads to more maintainable and understandable code.

If you encounter similar situations in the future, consider combining functionalities instead of separating them into conflicting threads. Happy coding!