Python Codebase Review: Best Practices & Cleanup

Hey guys! Let's dive into a thorough review of our Python codebase. We've got a lot of scripts kicking around, and it's time to make sure everything is up to snuff. This review focuses on improving code quality, organization, and adherence to best practices as outlined in CLAUDE.md. Our goal is to create a more maintainable and efficient codebase. This will make our lives easier down the road and ensure our scripts are reliable and effective.

Overview of the Python Codebase Review

As the project has grown, the Python codebase has accumulated a variety of scripts. These scripts have different purposes and levels of quality. It's crucial to ensure that all code meets established best practices and provides clear value. This review aims to address these issues and streamline our development process. We'll be looking at everything from hardcoded paths to missing documentation, with the end goal of a cleaner, more organized, and production-ready codebase. The review is divided into several phases to ensure a systematic approach. Each phase will address specific aspects of the codebase, leading to a comprehensive understanding of the current state and a clear roadmap for improvement. This structured approach will help us prioritize tasks, track progress, and ultimately, achieve our goals of a high-quality, maintainable codebase.

Identifying Key Issues

We've identified several key areas that need immediate attention. These issues range from hardcoded paths and code quality problems to script organization and compliance with best practices. Addressing these issues will significantly improve the overall quality and efficiency of the codebase. Let's break down the major problems.

Hardcoded Temporary Paths: A Common Headache

One of the first issues we'll tackle is the presence of hardcoded temporary paths, specifically /tmp/, within our scripts. This approach is not ideal for production environments. Specifically, the following files have been flagged:

• docs/metpo/scripts/definition_work/fetch_source_metadata.py
• docs/metpo/scripts/definition_work/merge_sparql_with_metadata.py

These hardcoded paths suggest that these scripts were originally designed for exploratory analysis rather than production use. We need to replace these with more flexible solutions, such as using configuration files or environment variables. This change will make the scripts more adaptable and easier to deploy in different environments. This flexibility is crucial for long-term maintainability and scalability.

Code Quality: Addressing Linting and Complexity

We've already made some progress in addressing code quality issues, but there's still work to be done. We initially fixed 6854 quote style errors and 1224 other auto-fixable linting issues. Despite these fixes, a significant 616 linting errors remain. These errors mostly consist of complexity and style warnings. The errors are: functions with too many branches/statements, and inconsistent use of modern Python patterns (e.g., Path vs open()). These issues can make the code harder to read, understand, and maintain. Addressing these warnings is crucial for improving code quality and ensuring consistency. We'll be focusing on refactoring complex functions, enforcing consistent style guidelines, and adopting modern Python patterns to create a more readable and maintainable codebase. This will involve using tools such as linters, code formatters, and refactoring techniques to streamline the code and reduce complexity.

Script Organization: Clarity and Structure

The organization of our scripts also needs improvement. Specifically, scripts in the docs/metpo/scripts/ directory raise questions about their purpose and status. Additionally, we need to clarify which scripts are intended for production use versus those used for one-off analysis. Many scripts are missing Click CLI interfaces, which are essential for command-line execution and integration. Many scripts also lack entries in pyproject.toml, which is necessary for managing dependencies and configurations. Without these interfaces and configurations, it becomes harder to run, maintain, and share these scripts. We need a clear separation between production tools and analysis artifacts. We will ensure that all production scripts are properly organized within the designated metpo/scripts/ directories, have Click CLI interfaces, and are correctly integrated with pyproject.toml and the Makefile. This organized approach will improve clarity and ensure that each script has a clear purpose and role.

Best Practices Compliance: Adhering to CLAUDE.md

We need to ensure all production scripts comply with the guidelines in CLAUDE.md. The existing scripts need to be adapted to meet the production standards. Let's look at the specific requirements:

• ✅ Environment Management: Scripts should use uv or poetry for environment management. We need to verify that all scripts are using a consistent and correct approach to dependency management.
• ❌ Click CLI Interfaces: Many scripts are missing Click CLI interfaces. We'll need to add these to ensure command-line execution. This is crucial for automation and integration.
• ❌ Directory Structure: Scripts should reside in the metpo/scripts/ directories. This ensures a consistent structure. We need to verify that all production scripts are located in the appropriate directories.
• ❌ CLI Aliases: Many scripts are missing CLI aliases in pyproject.toml. This is crucial for command-line execution and integration. We'll make sure that all production scripts have the appropriate aliases defined.
• ❌ Makefile Integration: Many scripts are not integrated into the Makefile. This makes it difficult to manage and execute scripts. We'll need to update the Makefile to include all production scripts.
• ❌ Production Standards: Many scripts are still one-off analysis notebooks instead of being production-ready. We'll need to ensure that the scripts meet all production standards. This includes proper error handling, logging, and documentation.

Proposed Review Process: A Step-by-Step Guide

To ensure a systematic and thorough review, we'll follow a structured process. This phased approach will allow us to address each issue efficiently.

Phase 1: Inventory - Listing and Categorizing Files

• Goal: Create a complete inventory of all Python files in the repository. We'll start by listing all the Python files. We'll need to categorize each file as a production tool, analysis script, test, or legacy code. This categorization will help us prioritize the review and focus on the most important files. We'll also identify scripts with hardcoded paths or other anti-patterns.

Phase 2: Evaluation - Assessing Each Script

• Goal: Evaluate each script individually to determine its purpose, quality, and compliance with best practices. For each script, we'll determine if it's still useful, a production tool, or analysis artifact. This will help us determine which scripts need immediate attention. We'll also check if the script meets CLAUDE.md requirements. This includes checking for CLI interfaces, directory structure, and Makefile integration. We'll also determine if each script has tests and if it's properly documented.

Phase 3: Action - Implementing Necessary Changes

• Goal: Take appropriate action based on the evaluation findings. We'll start by removing obsolete or superseded scripts. We'll also upgrade scripts by adding Click CLI interfaces, pyproject.toml entries, and Makefile integration. We'll relocate scripts to the appropriate directories. This is important for code organization and clarity. We'll fix scripts by removing hardcoded paths and adding proper argument parsing. We'll add docstrings and usage examples to improve documentation.

Phase 4: Standardization - Maintaining Consistent Standards

• Goal: Establish and enforce consistent standards across all Python code. We'll apply consistent linting rules across all Python code. This will help maintain code quality and consistency. We'll refactor complex functions to reduce branches and statements. This will improve code readability and maintainability. We'll ensure that all production scripts use modern Python patterns. This includes using Path objects, f-strings, and other modern Python features. We'll add pre-commit hooks to maintain coding standards. This will automatically check the code before committing changes.

Files Needing Immediate Attention

Certain files require our immediate focus due to their impact on the codebase. Here are the files that need immediate attention:

• Scripts with hardcoded /tmp/ paths: We need to fix the hardcoded paths in these scripts. These are docs/metpo/scripts/definition_work/fetch_source_metadata.py and docs/metpo/scripts/definition_work/merge_sparql_with_metadata.py.
• Recent additions needing review: We also need to review these scripts to ensure they meet our standards: metpo/scripts/definition_work/regenerate_curator_attributions.py and metpo/scripts/visualize_distances.py.

These scripts require our immediate attention because they either contain critical issues, such as hardcoded paths, or are recent additions that need to be reviewed to ensure they meet our standards.

Success Criteria: What We Aim to Achieve

To consider this review a success, we must meet these criteria:

• ✅ All production scripts meet CLAUDE.md requirements. This includes using uv or poetry for environment management, having Click CLI interfaces, residing in the correct directories, having CLI aliases, being integrated into the Makefile, and meeting production standards.
• ✅ No hardcoded file paths in production code. This improves the adaptability of our scripts. This ensures that the code can be deployed and used in different environments.
• ✅ All scripts have a clear purpose and documentation. This makes it easier to understand, maintain, and contribute to the code. This improves collaboration and reduces the time needed to onboard new team members.
• ✅ Linting errors are reduced to acceptable levels. This enhances code quality and readability. This improves the overall consistency of the code and reduces the chances of errors.
• ✅ Clear separation between production tools and analysis artifacts. This improves organization and maintainability. This helps us to understand which scripts are essential for our core functionality and which are for exploratory analysis.
• ✅ All scripts are integrated into the workflow (Makefile/pyproject.toml). This improves ease of use and maintainability. This ensures that our scripts can be easily managed and executed.

Related Context: Building on Previous Efforts

This review builds upon previous efforts to improve the codebase. The following efforts are linked to the review:

• Quote Style Errors and Linting: The codebase previously addressed 6854 quote style errors and 1224 other auto-fixable linting issues.
• Remaining Linting Issues: Despite fixes, 616 linting errors remain, mainly related to code complexity and style warnings. Addressing these will be part of the review.
• Repository Cleanup: The repository cleanup identified numerous files requiring organization, which will be addressed as part of this review. This comprehensive review aims to build on these successes and address outstanding issues.

This review is a medium-priority project and is estimated to take 2-3 days for a comprehensive review. The assigned person for this project is TBD. This review is a critical step in ensuring our codebase remains maintainable, efficient, and aligned with best practices. By following the proposed process and addressing the identified issues, we can significantly improve the quality and reliability of our scripts.