yaflux

A declarative framework for managing complex analytical workflows in Python.

Overview

yaflux provides a structured approach to managing complex data analysis pipelines where tracking transformations, ensuring reproducibility, and maintaining clear provenance are essential. It offers a pure Python solution for declaring dependencies between analysis steps and managing results immutably.

Key Features

• Declarative Workflow Definition: Analysis steps are defined through decorators that explicitly state their inputs and outputs

• Immutable Results Management: Results are tracked and protected from inadvertent mutation

• Dependency Tracking: Automatic tracking of dependencies between analysis steps

• Progress Monitoring: Built-in tracking of completed analysis steps

• Serialization: Simple persistence of complete analysis states

• Portable Results: Analysis results can be shared and loaded without original class definitions

Contents

• Installation
• Quick Start
  • Defining an Analysis
  • Executing an Analysis
  • Saving and Loading Analysis States
  • Visualizing Analysis Steps
• Design Philosophy
  • Core Principles
  • Implementation Decisions
  • Design Tradeoffs
  • Future Considerations
• Advanced Usage
  • Analysis Inheritance

API

• API Reference
  • Base Class
  • Step Decorator
  • Portable Results
  • Metadata
  • Results