Hey everyone! I'm looking for some advice on how to improve my debugging process for scientific computing code. Here's my usual workflow:
1. I start by gathering data and determining the equations to use.
2. Next, I write my code in Jupyter Notebook, experimenting with graphs and data frames in Pandas or Polars until my algorithm feels ready for production.
3. After that, I encapsulate the functionality into a function.
4. Finally, I migrate it to the production system and create tests.
The trouble I face arises later when I need to validate my data or tweak the algorithm. It gets confusing to go back to the code since the equations aren't always clearly visible, and if I haven't commented well, debugging can take me ages.
I typically use the Python debugger, which helps, but I was wondering if there's a way to also visualize the code. For example, if I'm working on a code block like this one in a production system, I'd love to be able to navigate to the code, run it, see relevant documentation about the algorithm, and visualize the data to quickly spot-check things.
Does anyone have thoughts on this? Am I missing something in my approach?
3 Answers
Check your tests! If you’re feeling lost, your tests should help guide you. If they don’t provide clarity, consider improving them or creating more. Also, visualizations can be super beneficial. Alongside your tests, include examples that output some visuals so you can better evaluate what's happening. You might also want to look for IDE plugins that offer math typesetting—this way, you can include formulas directly in your documentation to keep things clear.
Are you using any AI tools? Most modern IDEs that support Python come with built-in debugging features. When you review your code, remember that a lot of math is abstracted through functions, which can make it easy to lose the context. Visualizing your data in scatter plots or bar charts is often much clearer than just looking at lists. If you want to visualize this, consider copying your code into a Jupyter Notebook for testing.
Right, using print statements or any IDE debug feature you have can help. Are you suggesting that I should nicely format portions of the code and have AI tools like Copilot help explain what’s going on?
I'd recommend trying out PyCharm and getting familiar with its features. When working with scientific code, it's crucial to know what inputs lead to specific outputs. For example, if you have a linear regression, test it with known data points that should yield expected results. If the output doesn’t match what you expect, that’s a good indicator of where to look for bugs. Monte Carlo simulations are also useful; if you know your output should always fall within a certain range, generate a bunch of random datasets to see how often the results stay consistent. I’ve found these techniques really helpful for tracking down subtle bugs!
Absolutely! If it’s easier to visualize, use graphs for debugging rather than combing through data lists. Once you have data visualization alongside your code, it becomes a lot simpler to spot issues.