CARP DSP - Data Science Platform

A Kotlin Multiplatform framework for CARP (Copenhagen Research Platform) data science and analytics processing.

🚀 Features

• Type-Safe Tabular Data: Modern tabular data structures with full CARP semantics preservation
• Multiplatform Support: Kotlin Multiplatform targeting JVM (with future JS/Native support)
• CARP Integration: Seamless integration with existing CARP data structures and workflows
• Execution Framework: Pluggable execution strategies for data processing workflows
• Python Integration: First-class support for Python scripts and conda environments
• Data Retrieval: Built-in HTTP-based data retrieval with retry logic
• Auto Environment Setup: Automatic conda environment creation and management

📚 Documentation

📖 Quick Start Guide - Get started in minutes!

📘 Complete Documentation - Comprehensive guide covering:

• Architecture and design decisions
• Module structure and components
• Demos and examples
• API reference
• Troubleshooting

Quick Links:

• Getting Started
• Demos and Examples
• Architecture Overview
• Documentation Index

🔧 Development

Prerequisites

• JDK 17 or higher
• Kotlin 2.1.20+
• (Optional) Conda - for running Python-based demos

Quick Start

# Clone and build
git clone https://github.com/ngreve/carp-dsp.git
cd carp-dsp
./gradlew build

# Run a demo
./gradlew :carp.dsp.demo:jvmRun -PmainClass=carp.dsp.demo.DexcomRetrievalDemoMainKt

See Demos and Examples for more examples.

Building

./gradlew build

Running Tests

# All tests
./gradlew test

# Module-specific tests
./gradlew :carp.dsp.core:jvmTest
./gradlew :carp.dsp.demo:jvmTest

Code Quality

# Run Detekt analysis
./gradlew detektPasses

# Generate coverage reports
./gradlew koverHtmlReport
./gradlew koverXmlReport

Building Artifacts

# Build JAR files
./gradlew :carp.dsp.core:jvmJar
./gradlew :carp.dsp.demo:jvmJar

🎯 CI/CD Workflows

The project includes comprehensive GitHub Actions workflows:

Main CI Pipeline (.github/workflows/ci.yml)

• Multiplatform builds and testing
• Detekt code quality analysis
• Kover coverage reporting
• Codecov integration
• Artifact generation

Documentation & Artifacts (.github/workflows/docs-and-artifacts.yml)

• JAR artifact building
• Quality gates and verification
• Coverage report uploads

📊 Coverage Reports

Coverage reports are automatically generated and uploaded:

• HTML Report: Available as CI artifacts
• XML Report: Integrated with Codecov
• Badge: Shows current coverage status

🏗️ Architecture

carp-dsp/
├── carp.dsp.core/          # Core framework
│   ├── domain/             # Domain models and logic
│   │   ├── data/           # Tabular data structures
│   │   └── execution/      # Workflow execution
│   └── application/        # Data Converters
├── carp.dsp.demo/          # Demo and examples
└── .github/workflows/      # CI/CD automation

📈 Project Status

• ✅ Core Framework: Complete with comprehensive testing
• ✅ CI/CD Pipeline: Fully automated with quality gates
• ✅ Demo Implementation: Working CLI examples with DEXCOM CGM analysis
• ✅ Python Integration: Conda environment management and script execution
• ✅ Documentation: Comprehensive documentation available
• 🚧 Dokka Integration: API docs generation in progress
• 🚧 Additional Platforms: JS/Native support planned

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat: add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

All PRs are automatically tested with the CI pipeline and require:

• ✅ All tests passing
• ✅ Detekt quality checks
• ✅ Coverage requirements met

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.