goliat 1.4.0

A Python framework for dosimetric assessments using Sim4Life.

Comprehensive automated near- and far-field SAR assessments using Sim4Life

🚀 Get Started • 📖 View Tutorials

GOLIAT GUI showing real-time progress monitoring. Scroll down to see the online monitoring website and Bash shell!

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What is GOLIAT?

GOLIAT is a Python framework that automates electromagnetic field (EMF) dosimetry simulations from start to finish. Calculate Specific Absorption Rate (SAR) in digital human phantoms with minimal manual intervention, whether you're simulating a phone call (near-field) or environmental exposure (far-field).

Perfect for: Researchers, compliance engineers, and anyone studying EMF exposure in biological tissues.

Why GOLIAT?

• ⚡ Zero manual scene building – Phantoms, antennas, and materials load automatically
• 🔄 Reproducible – Configuration-driven workflow ensures consistent results
• ☁️ Cloud-ready – Scale from local testing to 100s of parallel cloud simulations
• 📊 Analysis built-in – Get plots and detailed SAR reports
• 🎨 GUI included – Monitor progress in real-time with ETA tracking
• 🤖 AI Assistant – Query codebase and documentation with natural language

Project funding and Acknowledgements

This software is written for subtask 1.5 of 5G expOsure, causaL effects, and rIsk perception through citizen engAgemenT (GOLIAT) funded by the European Union as part of the CLUE-H consortium to study health effects of radio-frequency electromagnetic fields. Subtask 1.5 is central to the project to relate incident fields to absorption values inside of humans.

Key features

🎯 Near-Field Simulations Simulate devices near the body (phones, wearables). Automatic antenna placement with customizable positions and orientations. 🏗️ Modular Architecture Plug-and-play components for phantoms, materials, gridding, and sources. Extend easily for custom scenarios. 📊 Built-in Analysis Extract whole-body SAR, localized peaks (10g), and tissue-specific metrics. Auto-generate heatmaps and statistical reports. 📝 Automated Reporting Turn results into a draft paper instantly. Generates a fully compiled LaTeX PDF with all plots, tables, and statistics organized for peer review.

🌐 Far-Field Simulations Environmental exposure from plane waves. Supports 6 incident directions and multiple polarizations per frequency. 🚀 Dual Execution Modes Local: iSolve for quick testing and debugging Cloud: oSPARC for large-scale batch processing 🖥️ Real-time Monitoring Interactive GUI with progress bars, ETA estimation, and live logs. Optional web dashboard for monitoring distributed studies. 🤖 AI Assistant Integrated RAG-based assistant. Ask questions like "running a simulation", "adding a new phantom", or "how are logs handled" to get instant, context-aware answers.

Quick start

Prerequisites: Sim4Life 8.2.0 with a valid license. Note: GOLIAT has only been tested on Sim4Life 8.2.0; compatibility with newer versions is untested.

GOLIAT supports two installation methods. Most users should install from PyPI:

# 1. Create a virtual environment with Sim4Life Python
"C:\Program Files\Sim4Life_8.2.0.16876\Python\python.exe" -m venv venv --system-site-packages
source venv/Scripts/activate  # On Windows Git Bash

# 2. Install GOLIAT
python -m pip install goliat

# 3. Navigate to your project directory and initialize
cd /path/to/your/project
goliat init

# 4. Run your first simulation
goliat study near_field_config

The --system-site-packages flag allows the venv to access Sim4Life's packages (like s4l_v1). This installs the latest released version from PyPI. For unreleased features, use editable installation.

For developers: If you need to modify code, run tests, or access repository tools, you should install in editable mode instead. See installation guide for details.

Note: The goliat init command checks your setup and downloads required data files. If you skip this step, goliat study will automatically prompt you to install when first run.

That's it! The GUI will launch, download required phantoms/antennas, and run the simulation. Results appear in results/ with JSON metrics and plots.

New to GOLIAT? Follow the Quick Start Tutorial for a step-by-step walkthrough.

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Usage examples

Example 1: Near-field phone simulation

# Run a 700 MHz phone-to-cheek simulation
goliat study near_field_config

What happens:

• Loads "thelonious" (6-year-old) phantom
• Places and rotates PIFA antenna either by the face, belly or cheek
• Runs FDTD solver (5-10 min on GPU)
• Extracts head SAR, brain peak SAR (psSAR10g), power balance
• Generates heatmaps in results/near_field/thelonious/700MHz/by_cheek/

Example 2: Far-field environmental exposure

# Plane wave exposure from 6 directions at 900 MHz
goliat study far_field_config

What happens:

• Simulates waves from x/y/z axes (± directions)
• Also theta and phi polarizations (so 12 sims total)
• Calculates whole-body average SAR
• Outputs statistical summaries and boxplots

Example 3: Cloud batch processing

"execution_control": {
  "batch_run": true
}

# Submit 100+ sims to cloud in parallel
goliat study large_study

Also want to run setup and extract phases in parallel? See the Cloud Setup Guide for deploying GPU instances. For monitoring distributed studies across multiple workers, see the monitoring dashboard documentation or the goliat-monitoring repository.

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Screenshots & videos

GUI, Bash shell & online monitoring website

Detailed real-time progress tracking with ETA estimation, along with the verbose logging and the online monitoring website.

Simulation Setup

Automatic scene building in Sim4Life with phantom and antenna placement.

Far-field simulation with plane wave sources from multiple directions.

SAR distribution visualization in Sim4Life.

Analysis Results Gallery

GOLIAT auto-generates 16+ plot types. Click each to expand.

Heatmap - SAR overview across all conditions

Bar Chart - Average SAR by frequency

Boxplot - SAR distribution across scenarios

Bubble Chart - Tissue mass vs SAR relationship

CDF - Cumulative distribution function

Correlation Matrix - Tissue group correlations

Line Plot - SAR vs frequency trends

Outliers - Outlier detection summary

Penetration - Depth ratio analysis

Power Balance - Power distribution overview

Ranking - Top 20 tissues by SAR

Spatial - Peak SAR 2D location

Scatter - Max local vs peak spatial SAR

Tissue Response - Frequency response curve

Distribution - Tissue mass/volume distribution

Comparison - UGent vs CNR validation

Cloud Monitoring Dashboard

Web dashboard for monitoring distributed studies across multiple workers.

Track individual worker status and progress.

Monitor super studies spanning multiple phantoms and frequencies.

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How it works

GOLIAT follows a simple 5-stage pipeline:

graph LR
    A[📝 Config] --> B[🎬 Setup]
    B --> C[⚡ Simulate]
    C --> D[📊 Extract]
    D --> E[📈 Analyze]

    style A fill:#4CAF50
    style E fill:#4CAF50
    style B fill:#2196F3
    style C fill:#FF9800
    style D fill:#9C27B0

1. Config: Load JSON with study parameters (phantoms, frequencies, placements)
2. Setup: Auto-build Sim4Life scene (load models, assign materials, set grid)
3. Simulate: Run FDTD solver (local or cloud)
4. Extract: Pull SAR, power balance, point sensors from results
5. Analyze: Generate CSVs, plots, statistical summaries

Each stage is modular, swap in custom phantoms, antennas, or analysis strategies.

🔗 Detailed Architecture Guide

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Documentation

Resource	Description
Quick Start	Get running in 5 minutes
User Guide	Workflows and concepts explained
Tutorials	Step-by-step examples (basic → advanced)
Configuration	All config options with examples
API Reference	Class and function details
Troubleshooting	Common issues and solutions
Cloud & Monitoring	Web dashboard for distributed execution
AI Assistant	Natural language query interface
Developer Guide	Extend GOLIAT or contribute

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Contributing

Contributions are welcome! We follow a standard fork-and-PR workflow:

1. Fork the repo and create a feature branch
2. Make changes following our code style (Ruff, type hints)
3. Add tests for new features
4. Submit a PR with a clear description

See CONTRIBUTING.md for detailed guidelines, code style rules, and how to run tests locally.

Quick links: Code of Conduct • Developer Guide

Note: GOLIAT has just come out of beta and is still in early development. You may still encounter occasional errors. Please report any issues you find.

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Citing

If you use this software, please cite it as:

@software{Wydaeghe_GOLIAT,
  title   = {{GOLIAT: A Comprehensive Automated Near- and Far-Field SAR Assessment Toolbox using Sim4Life}},
  author  = {Wydaeghe, Robin},
  url     = {https://github.com/rwydaeghe/goliat},
  license = {Apache-2.0},
  version = {1.4.0}
}

For other citation formats, please refer to the "Cite this repository" button on the main page of our GitHub repository.

License

This project is licensed under the Apache 2.0 License – see LICENSE for details.

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Results

Check out the auto-generated first draft paper (only results):

• Results PDF - Download the compiled PDF
• Results LaTeX Source - Download the LaTeX source

🔗 Links

• 📚 Full Documentation
• 🐛 Report an Issue
• 💬 Discussions
• 📦 Releases

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