Overview¶
DVOACAP Python is a complete Python port of the VOACAP (Voice of America Coverage Analysis Program) ionospheric propagation model. It provides accurate HF radio propagation predictions based on ionospheric physics and empirical models.
What is VOACAP?¶
VOACAP is the industry-standard software for predicting HF radio propagation. It uses:
Ionospheric Models: CCIR/URSI coefficients for worldwide ionospheric conditions
Solar Activity: Integration of solar flux and sunspot number data
Geomagnetic Field: IGRF model for magnetic field effects
Ray Tracing: Sophisticated raytracing through the ionosphere
Statistical Analysis: Reliability and probability calculations
Why Python?¶
The original VOACAP is written in FORTRAN, making it difficult to integrate into modern applications. DVOACAP Python provides:
Modern API: Clean, Pythonic interface with type hints
Easy Integration: Use in web apps, data analysis, automation
Extensibility: Add custom antenna patterns, noise models, etc.
Cross-Platform: Works on Windows, macOS, Linux
Open Source: MIT licensed, community-driven development
Use Cases¶
Amateur Radio: Plan DX contacts and optimize antenna systems
Professional Communications: Design HF communication networks
Research: Study ionospheric propagation phenomena
Education: Learn about HF radio propagation
Automated Systems: Build propagation prediction services
Architecture¶
The codebase is organized into 5 phases:
Phase 1: Path Geometry¶
Great circle path calculations
Azimuth and distance computations
Hop geometry (elevation angles, skip distances)
Phase 2: Solar & Geomagnetic¶
Solar position calculations
Geomagnetic field modeling (IGRF)
Magnetic latitude and gyrofrequency
Phase 3: Ionospheric Profiles¶
Fourier coefficient maps (CCIR/URSI)
Layer parameter computations (E, F1, F2)
Electron density profiles
True and virtual height calculations
Phase 4: Raytracing¶
MUF (Maximum Usable Frequency) calculations
Reflectrix (frequency vs elevation angle)
Multi-hop mode finding
Penetration angle computations
Phase 5: Signal Predictions¶
Radio noise modeling (ITU-R P.372)
Antenna gain patterns
Path loss calculations
SNR and reliability predictions