Intrinsic-Resonance-Holography-

IRH Interactive Notebooks

This directory contains Jupyter notebooks that demonstrate and validate Intrinsic Resonance Holography (IRH) computations.

🚀 v18.0 Colab Notebooks (Recommended)

The following notebooks provide complete, ready-to-run experiences for IRH v18.0:

IRH_v18_Quickstart_Colab.ipynb ⭐ Start Here

Quick Introduction to IRH v18.0

• Runtime: ~2 minutes
• Standard Model derivation from topology
• Cosmic Fixed Point computation
• Key predictions preview

IRH_v18_Full_Install_Colab.ipynb 🔬 Comprehensive Testing

Complete Installation with Interactive Menu

• Complete v18 project installation
• Interactive test level selection (Quick/Standard/Comprehensive/Full pytest)
• 11 physics modules to validate
• Visualization of predictions vs experiments
• Runtime: 30 seconds to 10 minutes (user selectable)

IRH_v18_Development_Colab.ipynb 🛠️ For Developers

Development Environment for Contributors

• Full development setup
• API reference and documentation
• Testing utilities (pytest, coverage)
• Code quality tools (ruff, black, mypy)
• Contributing workflow examples

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📓 Legacy Notebooks (v16 and earlier)

Note: These notebooks use v16 or earlier implementations which are now deprecated. For the current implementation, use the v18 notebooks above.

For Local Execution

1. Install dependencies:

   pip install -r ../requirements.txt
   

2. Launch Jupyter:

   jupyter notebook
   

3. Open any notebook and run all cells

📓 Available Notebooks

01_ARO_Demo.ipynb (Legacy - v16)

Adaptive Resonance Optimization Demo

• Runtime: ~5 minutes
• Demonstrates how random networks evolve toward 4D spacetime
• Shows convergence of the harmony functional
• Interactive visualizations of network evolution

02_Dimensional_Bootstrap.ipynb

Dimensional Bootstrap Analysis

• Runtime: ~10 minutes
• Derives d=4 from self-consistency requirements
• Validates spectral dimension computation
• Tests stability across different topologies

03_Fine_Structure_Derivation.ipynb

Fine Structure Constant Derivation

• Runtime: ~15 minutes
• Computes α⁻¹ ≈ 137.036 from first principles
• No free parameters or adjustable constants
• Comparison with CODATA 2022 experimental value

04_Dark_Energy_w(a).ipynb

Dark Energy Equation of State

• Runtime: ~10 minutes
• Predicts dark energy evolution w(a)
• Comparison with DESI 2024 observations
• Falsifiable predictions for Euclid mission

05_Spinning_Wave_Patterns.ipynb

Emergent Wave Dynamics

• Runtime: ~8 minutes
• Visualizes wave patterns on hypergraph substrate
• Demonstrates emergence of oscillatory behavior
• Beautiful animations of resonance modes

06_Grand_Audit.ipynb ⭐ NEW!

Comprehensive Validation Framework

• Runtime: ~10 minutes (quick mode), ~30-60 minutes (comprehensive)
• Most comprehensive validation tool
• 22+ validation checks across 4 foundational pillars
• Convergence testing across multiple network sizes
• Detailed visualizations and export capabilities

What it validates:

• ✅ Ontological Clarity (6 checks)
• ✅ Mathematical Completeness (4 checks)
• ✅ Empirical Grounding (6 checks)
• ✅ Logical Coherence (6 checks)
• ✅ Convergence analysis
• ✅ Comparison with experimental data

🎯 Recommended Learning Path

1. Start here: 01_ARO_Demo.ipynb - Learn the basics
2. Theory: 02_Dimensional_Bootstrap.ipynb - Understand the framework
3. Predictions: 03_Fine_Structure_Derivation.ipynb - See it in action
4. Validation: 06_Grand_Audit.ipynb - Comprehensive testing
5. Explore: 04_Dark_Energy_w(a).ipynb and 05_Spinning_Wave_Patterns.ipynb

💻 Command Line Alternative

Prefer command line? Use the standalone scripts:

# Quick grand audit
python ../scripts/run_enhanced_grand_audit.py --quick

# Full audit with visualizations
python ../scripts/run_enhanced_grand_audit.py --full --output results/

📊 Output Formats

Notebooks can export results in multiple formats:

• JSON: Structured data for further analysis
• CSV: Tabular data for spreadsheets
• PNG: High-resolution visualizations
• TXT: Human-readable summaries

🔧 Customization

All notebooks support customization:

• Adjust network size N for speed vs. accuracy tradeoff
• Modify random seed for different realizations
• Change visualization parameters
• Export custom subsets of results

📚 Documentation

For detailed documentation on the theoretical framework and implementation:

• Main README
• Mathematical Proofs
• Implementation Status

🐛 Troubleshooting

“Module not found” errors

• In Colab: The setup cell installs dependencies automatically
• Locally: Run pip install -r ../requirements.txt

Slow execution

• Reduce network size: N = 32 instead of N = 256
• Use quick mode in Grand Audit
• Skip convergence analysis for faster results

Memory issues

• Reduce N to a smaller value (32, 64, or 128)
• Close other applications
• Restart kernel and clear all outputs

🤝 Contributing

Found a bug or want to add a notebook? See our contribution guidelines.

📝 Citation

If you use these notebooks in your research, please cite:

@software{mccrary2025irh,
  title={Intrinsic Resonance Holography v11.0: The Complete Axiomatic Derivation},
  author={McCrary, Brandon D.},
  year={2025},
  url={https://github.com/dragonspider1991/Intrinsic-Resonance-Holography-}
}

📧 Support

Questions? Issues? Contact us:

• GitHub Issues
• Email: brandon.mccrary@example.com

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Happy exploring! 🚀

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