pybiorythm
PyBiorythm Documentation
Welcome to PyBiorythm - a comprehensive Python library for generating biorhythm charts and timeseries data.
!!! warning “Scientific Disclaimer” This software implements biorhythm theory, which is considered PSEUDOSCIENCE. Extensive scientific research has found NO VALIDITY to biorhythm theory beyond coincidence. Multiple controlled studies have consistently failed to find any correlation between the proposed 23, 28, and 33-day cycles and human performance or life events.
**This implementation is provided FOR ENTERTAINMENT PURPOSES ONLY** and should NOT be used for making any important life decisions.
What is PyBiorythm?
PyBiorythm is a modern Python library that implements the classical biorhythm theory with multiple output formats, comprehensive testing, and enterprise-grade CI/CD pipelines. It’s designed for educational purposes, data analysis experiments, and entertainment applications.
Key Features
🎯 Multiple Output Formats
- ASCII Charts: Traditional vertical and horizontal biorhythm charts
- JSON Data: Structured timeseries data for analysis and visualization
- Configurable Parameters: Adjustable chart width, time periods, and orientations
👨💻 Developer Friendly
- 90.33% Test Coverage with comprehensive test suite
- Modern Python: Type hints, dataclasses, and clean architecture
- Docker Support: Multi-stage builds for production deployment
- CI/CD Ready: GitHub Actions workflows for quality assurance
📊 Data Analysis Ready
- JSON Timeseries: Perfect for pandas, matplotlib, and data science workflows
- Critical Day Detection: Identifies when cycles cross zero
- Statistical Properties: Comprehensive metadata for analysis
- Feature Engineering: Ready for machine learning experiments
Quick Start
Installation
With pip (recommended):
# Install from PyPI (when published)
pip install biorythm
# Or install from source
git clone https://github.com/dkdndes/pybiorythm.git
cd pybiorythm
pip install .
With uv (fastest):
# Using uv package manager
uv add biorythm
# Or from source with uv
git clone https://github.com/dkdndes/pybiorythm.git
cd pybiorythm
uv pip install -e .
With Docker (easiest):
# Using Docker
docker run -it biorythm:latest
# Or build locally
git clone https://github.com/dkdndes/pybiorythm.git
cd pybiorythm
docker build -t biorythm:latest .
docker run -it biorythm:latest
See the Quick Start Guide for detailed usage instructions and examples.
Documentation Structure
🚀 User Guide
Get started quickly with installation, usage examples, and CLI reference.
- Quick Start Guide - Get up and running in 5 minutes
- Installation Guide - Multiple installation methods
- CLI Reference - Command line interface documentation
- Usage Examples - Practical examples and use cases
- Output Formats - Chart types and JSON formats
💻 API Reference
Comprehensive API documentation for the BiorhythmCalculator class and core functions.
- Calculator API - Main BiorhythmCalculator class
- Core Functions - Low-level calculation functions
- JSON Schema - Complete JSON output specification
- Error Handling - Exception types and error handling
🛠 Developer Guide
Development setup, testing, code quality, and contribution guidelines.
- Development Setup - Environment configuration
- Architecture - Project structure and design
- Testing Guide - Testing framework and coverage
- Code Quality - Linting, formatting, and standards
- Contributing - How to contribute to the project
🚢 Deployment
Docker deployment, Kubernetes manifests, and production deployment strategies.
- Deployment Guide - Production deployment strategies
- Docker Setup - Container deployment
- Kubernetes - Cluster deployment with manifests
- Security & Compliance - Security best practices
- Local GitHub Actions - Testing workflows locally
Chart Examples
For chart examples and detailed output format documentation, see:
- Output Formats Guide - All chart types and formats
- Usage Examples - Practical use cases
- JSON Schema - Complete JSON structure reference
What’s Next?
- New Users: Start with the Quick Start Guide
- Developers: Check out the Development Setup and Architecture Overview
- Contributors: Review the Branching & Workflow Guide, Code Quality Standards and Contributing Guidelines
- Data Scientists: Explore the JSON Schema and Output Formats
- DevOps: Review the Deployment Guide and Security Best Practices
Version Information
Current Version: 1.2.1
Python Compatibility: 3.9+
License: MIT
Author: Peter Rosemann (dkdndes@gmail.com)
Repository: GitHub