Your Research Code Deserves Better (But Not Too Much Better)

It starts innocently enough. You write a 200-line Python script to analyze some experimental data. It works perfectly. Your advisor is happy. You move on to the next experiment.

Six months later, that script is 2,000 lines. You’ve added three different analysis methods, support for four instrument types, and a plotting system that “mostly works.” Every time you add a feature, something else breaks. The only person who understands the code is you—and even you’re not entirely sure anymore.

You know the code needs restructuring, but where do you start? You may have heard of “design patterns” and “software architecture,” but those resources seem written for people building web applications and enterprise systems, not analyzing spectroscopy data or controlling lab equipment.

This blog series is for you.

What This Series Covers

Over the next few weeks, I’ll be publishing a 5-part series on SOLID principles—a set of design principles that help make code more maintainable, testable, and extensible. But instead of the usual enterprise examples, every post uses real scientific scenarios.

Each principle gets its own detailed post with complete, runnable code examples:

  • Single Responsibility Principle - “One Class, One Job”
  • Open/Closed Principle - “Extending Without Breaking”
  • Liskov Substitution Principle - “Interchangeable Components”
  • Interface Segregation Principle - “Lean Interfaces”
  • Dependency Inversion Principle - “Depend on Abstractions”

What Makes This Series Different

Real scientific examples. No shopping carts, no user authentication, no enterprise business logic. Just real problems that scientists and engineers face: handling data from different instruments, swapping analysis methods, working with various file formats.

Acknowledges when NOT to apply these principles. Your exploratory Jupyter notebook is fine as-is! Over-engineering is real, and I’ll tell you when simple is better.

Complete code examples. Every post includes full, working Python code you can run and modify. Before/after comparisons show what changes and why.

Focus on evolution, not perfection. Most research code starts as a quick script. I’ll show you how to recognize when it needs more structure, and how to refactor incrementally without rewriting everything.

Who This Series Is For

  • PhD students maintaining research code that’s outgrown its original scope
  • Research scientists whose “temporary” script is now supporting three other lab members
  • Engineers who dread opening their own six-month-old code
  • Lab managers maintaining code written by departed students

You don’t need a computer science degree. You don’t need to know what a “factory pattern” is. You just need to write code that’s becoming harder to maintain.

The Key Insight

Here’s what I’ve learned after years of writing and maintaining code:

Start simple. Refactor when pain appears.

Your exploratory script doesn’t need elegant architecture. But when that script becomes a production pipeline that runs every day for two years, the lack of structure becomes painful. The trick is recognizing when you’ve crossed that threshold and knowing what to do about it.

That’s what this series teaches.

A Preview: The Spectrum of Scientific Code

Not all code needs the same level of design rigour. Your quick data exploration? Keep it simple. That analysis pipeline your entire research group depends on? Time for structure.

Exploratory code → One-off Script → Production Pipeline → Reusable Library
     │                  │                │                     │
No SOLID needed    Light SOLID      Moderate SOLID        High SOLID

What to Expect

Each post is approximately 15-20 minutes of reading time and includes:

  • A relatable problem that scientists actually face
  • Complete “before” code showing the issue
  • Complete “after” code showing the solution
  • Explanation of why the refactoring helps
  • Guidance on when to apply (and when to skip) the principle

Posts will be published weekly starting next Monday. Bookmark this page and check back regularly.

Why I’m Writing This

I’ve spent years writing software, from quick analysis scripts to production pipelines to open-source libraries. I’ve made every mistake in this series (multiple times). I’ve written 5,000-line files that even I didn’t want to touch. I’ve broken validated code by adding “just one small feature.” I’ve forced implementations to inherit methods they couldn’t possibly support.

I learned SOLID principles the hard way: by feeling the pain of their absence.

This series is the guide I wish I’d had when I started. It’s specifically designed for scientists and engineers who need to write maintainable code but weren’t trained in software design.

Ready to Dive In?

The first post—Single Responsibility Principle for Scientists and Engineers—goes live next Monday. It covers the most common issue I see in scientific code: classes and functions that try to do too much.

We’ll take a realistic spectroscopy analysis script and refactor it step by step, showing exactly how to break apart monolithic code into focused, maintainable pieces.

Until then, take a look at your current code and ask yourself:

  • When I need to change how I load data, do I have to edit the same file that does the analysis?
  • When I add a new feature, do unrelated things break?
  • Have I ever swapped one component for another and gotten strange results?
  • Do I have classes implementing methods that just raise NotImplementedError?
  • Can I test my code without connecting to real hardware or reading real files?

If you answered “yes” to any of these, the following posts will show you exactly what to do about it.

Questions or topics you’d like covered? Leave a comment below!