# DVOACAP-Python v1.0.0 Release Notes

**Release Date:** November 18, 2025
**Status:** Production/Stable
**Validation Accuracy:** 86.6% (226/261 tests passing)

---

## 🎉 Introduction

We are proud to announce the **first stable release** of DVOACAP-Python, a modern Python port of the DVOACAP HF propagation prediction engine. This milestone represents the culmination of intensive development, validation, and testing to bring professional-grade ionospheric propagation modeling to the Python ecosystem.

### What is DVOACAP-Python?

DVOACAP-Python is a complete Python implementation of the VOACAP ionospheric propagation model, originally developed by Voice of America and modernized in Delphi/Pascal by Alex Shovkoplyas (VE3NEA). This project makes HF propagation prediction accessible to:

- **Amateur radio operators** planning DX contacts
- **Researchers** studying ionospheric propagation
- **Developers** building propagation-aware applications
- **Educators** teaching radio wave propagation

---

## ✨ Major Features

### Core Propagation Engine ✅

All 5 implementation phases complete:

1. **Phase 1: Path Geometry** (Complete)
   - Great circle path calculations
   - Geodetic/geocentric coordinate conversions
   - Multi-hop path geometry
   - Bearing and distance computations

2. **Phase 2: Solar & Geomagnetic** (Complete)
   - Solar zenith angle calculations
   - Local time conversions
   - IGRF magnetic field model
   - Gyrofrequency calculations

3. **Phase 3: Ionospheric Profiles** (Complete)
   - CCIR/URSI coefficient models
   - E/F1/F2/Es layer modeling
   - Critical frequency calculations
   - Electron density profiles
   - True and virtual height calculations

4. **Phase 4: Raytracing** (Complete)
   - MUF (Maximum Usable Frequency) calculations
   - FOT and HPF predictions
   - Ray path calculations through ionosphere
   - Skip distance computation
   - Multi-hop path finding
   - Over-the-MUF mode handling

5. **Phase 5: Signal Predictions** (Complete)
   - Noise modeling (atmospheric, galactic, man-made)
   - Antenna gain calculations (dipole, vertical monopole, isotropic)
   - Full signal strength predictions
   - Reliability calculations
   - SNR (Signal-to-Noise Ratio) analysis

### Interactive Dashboard 📊

- **Real-time propagation predictions** with Flask server backend
- **Interactive propagation maps** showing MUF across DXCC entities
- **Band condition meters** for 160m-10m
- **DXCC progress tracking** by band and mode
- **Live space weather data** from NOAA SWPC
- **Propagation charts** (REL, SNR, SDBW, MUFday)
- **Propagation wheel** visualization
- **Best frequency recommendations**
- **Mini planner** for target selection

### Validation & Testing 🧪

- **86.6% validation accuracy** across 11 diverse test scenarios
- **11 reference test cases** covering:
  - Short/medium/long distance paths
  - Polar and equatorial propagation
  - Solar minimum and maximum conditions
  - Multiple frequency bands
- **Real-world validation** with PSKReporter and WSPR data
- **314 pytest fixtures** for comprehensive testing
- **CI/CD pipeline** testing Python 3.11, 3.12, 3.13

---

## 📊 Technical Specifications

| Metric | Value |
|--------|-------|
| Validation Accuracy | 86.6% (226/261 tests) |
| Python Version | 3.11+ |
| License | MIT |
| Test Coverage | Comprehensive (7 test files, 314 fixtures) |
| Type Hints | ~89.8% return types, ~64.9% parameters |
| Development Status | Production/Stable |
| Performance | ~0.009s per prediction |

---

## 🔧 Installation

### Core Library Only

```bash
git clone https://github.com/skyelaird/dvoacap-python.git
cd dvoacap-python
pip install -e .
```

### With Dashboard

```bash
pip install -e ".[dashboard]"
```

### Development Setup

```bash
pip install -e ".[all]"
```

---

## 📖 Documentation

### User Documentation
- **README.md** - Comprehensive overview and quick start
- **docs/USAGE.md** - Detailed API usage patterns
- **docs/INTEGRATION.md** - Integration guide for web apps
- **examples/** - Working code examples for all phases
- **Dashboard/README.md** - Dashboard setup and configuration

### Technical Documentation
- **VALIDATION_STRATEGY.md** - Validation methodology
- **CONTRIBUTING.md** - Contribution guidelines
- **CHANGELOG.md** - Complete change history
- **Phase summaries** - Detailed implementation documentation

---

## 🎯 What's New in 1.0.0

### Licensing Clarifications ⚖️

- **Updated LICENSE** to properly attribute Joel Morin as Python port author
- **Added NOTICE file** with complete attribution chain (VOACAP → DVOACAP → dvoacap-python)
- **Added LICENSE_NOTE.txt** explaining MPL headers in Pascal reference files
- **Clarified MIT license** compatibility (DVOACAP was relicensed to MIT in May 2025)

### Version Updates 📦

- Version bumped from 0.9.0 (Beta) to 1.0.0 (Production/Stable)
- Development status classifier updated in PyPI metadata
- All 5 phases marked as complete (100% progress)

### Code Quality 🔍

- Only 1 non-critical TODO in entire codebase
- Pre-commit hooks for documentation maintenance
- Black code formatting (line-length=100)
- mypy type checking configured
- pytest with coverage reporting

---

## 🔬 Validation Results

### Test Case Overview

11 diverse scenarios covering:
- **Short path:** 500 km (Boston to Philadelphia)
- **Medium path:** 5,500 km (New York to London)
- **Long path:** 12,500 km (San Francisco to Tokyo)
- **Polar path:** High-latitude propagation
- **Equatorial path:** Low-latitude conditions
- **Solar conditions:** Minimum (SSN=10) and Maximum (SSN=150)
- **Multiple bands:** 80m, 40m, 30m, 20m, 17m, 15m, 12m, 10m

### Accuracy Metrics

- **Overall pass rate:** 86.6% (226/261 tests)
- **Target threshold:** 85% (exceeded ✓)
- **Validation method:** Comparison against reference VOACAP output
- **Metrics validated:** SNR, Reliability, MUF, Signal Power

### Real-World Validation

- **PSKReporter integration** for live propagation data
- **WSPR validation framework** for weak-signal analysis
- **Professional validation methodology** documented

---

## 🚀 Performance

- **Single prediction:** < 1 second
- **24-hour generation:** ~30 seconds
- **Memory efficient:** Numpy-based calculations
- **Scalable:** Can handle batch predictions

---

## 🙏 Acknowledgments

### Primary Development

- **Joel Morin** (skyelaird) - Python port and implementation
- **Claude.ai** - Collaborative development assistance

### Original Works

- **Alex Shovkoplyas (VE3NEA)** - Original DVOACAP Delphi/Pascal implementation
  - Repository: https://github.com/VE3NEA/DVOACAP
  - License: MIT (relicensed from MPL 1.1 in May 2025)

- **Voice of America / ITS** - Original VOACAP development
  - U.S. Government work (public domain)
  - Decades of ionospheric research

### Community

- Amateur radio and propagation modeling community
- Python scientific computing ecosystem (NumPy, SciPy, Matplotlib)
- Open source contributors

---

## 📋 Known Limitations

1. **Parameter type hints:** ~35% of parameters still need type annotations
2. **Integration tests:** No full end-to-end pipeline tests yet
3. **Performance profiling:** Baseline performance not yet established
4. **Jupyter examples:** No notebook-based tutorials yet

These are **non-blocking** for 1.0 release and planned for future updates.

---

## 🛣️ Future Roadmap

### Version 1.1 (Q1 2026)
- Complete parameter type hints
- Add integration tests
- Performance profiling and optimization
- Jupyter notebook examples
- Expanded Sphinx documentation

### Version 1.2 (Q2 2026)
- Coverage area maps
- All-year propagation matrix
- Contest planner features
- Antenna comparison tools

### Version 2.0 (Q3 2026)
- Multi-user web service
- User authentication
- Database backend
- Public API
- Mobile app integration

---

## 🐛 Bug Reports and Support

- **Issues:** https://github.com/skyelaird/dvoacap-python/issues
- **Discussions:** https://github.com/skyelaird/dvoacap-python/discussions
- **Documentation:** https://skyelaird.github.io/dvoacap-python/

---

## 📄 License

MIT License

Copyright (c) 2025 Joel Morin and Contributors

Based on DVOACAP by Alex Shovkoplyas (VE3NEA)
Original VOACAP by Voice of America / ITS

See LICENSE file for full text.

---

## 🎓 Citation

If you use DVOACAP-Python in academic work, please cite:

```
DVOACAP-Python v1.0.0 (2025)
Joel Morin and Contributors
https://github.com/skyelaird/dvoacap-python

Based on DVOACAP by Alex Shovkoplyas (VE3NEA)
and VOACAP by Voice of America / ITS
```

---

## 🔗 Links

- **Repository:** https://github.com/skyelaird/dvoacap-python
- **Documentation:** https://skyelaird.github.io/dvoacap-python/
- **Original DVOACAP:** https://github.com/VE3NEA/DVOACAP
- **PyPI Package:** (Coming soon)
- **Dashboard Demo:** (Coming soon)

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**Thank you for using DVOACAP-Python!** 73! 📻

*For amateur radio operators, researchers, and developers worldwide.*
