analysis-refactor

Refactoring a messy analysis script involves transforming it into a five-layer modular architecture: constants, data loading, atomic functions, output functions, and an orchestrator. This isolates computation and I/O to improve maintainability and robustness.

The best way to structure a data analysis workflow for testing is separating concerns into pure, well-scoped functions with clear inputs and outputs. Extracting constants and isolating data loading creates deterministic, auditable workflows that are easy to validate.

Yes, this modular architecture approach applies across scripting languages. It provides a language-agnostic blueprint to isolate constants, data loading, and computation, enabling predictable and testable workflows regardless of the specific language used.

Handling errors when iterating over dimensional combinations requires a central orchestrator, like a run_one function. It manages per-pair iteration and consistent outputs while gracefully catching errors to prevent total workflow failure.

Adding tests to an existing sequential script after refactoring uses a retrofit-testing approach. This validates the extracted atomic components and isolated functions to ensure the newly structured modular workflow behaves correctly.