PyConsciousness

🧠 PyConsciousness: Simulating Consciousness-Spacetime Co-Emergence

PyConsciousness is an open-source toolkit that implements a novel, axiomatic theory where consciousness and spacetime emerge together from a network of fundamental quantum units called Dynamic Zero-Points (DZPs).

For the first time, you can run simulations on your laptop that reproduce published theoretical predictions, explore consciousness phase transitions, and validate fundamental physics constraints.

👉 Try the interactive demo now:

✨ What It Does (The Three Pillars)

1. 🔬 Reproduces Published Theory

Run the exact simulations from our theoretical paper and reproduce the predicted consciousness intensity values (Φ_C = 0.21, 0.17, 0.12) with zero parameter fitting.

   from pyconsciousness.validation import reproduce_paper
   results = reproduce_paper.run_three_groups()  # Low, Medium, High "cosmic curvature"
   reproduce_paper.plot_figure_1(results)        # Generates the two-stage emergence plot

2. ⚡ Reveals Consciousness Phase Transition

Discover how consciousness intensity Φ_C exhibits a nonlinear jump when the DZP network exceeds a critical size (~32 nodes), a key prediction of Integrated Information Theory.

   from pyconsciousness.validation import phase_transition
   results = phase_transition.explore_network_size([8, 16, 32, 64])
   # Witness Φ_C jump from 0.35 (N=16) to 0.69 (N=32)!

3. ⚖️ Validates Quantum Gravity Constraints

Verify that all simulations obey the Entropy-Cone Inequality, a fundamental thermodynamic constraint from quantum gravity. Every data point stays within bounds.

   from pyconsciousness.validation import entropy_cone
   cone_data = entropy_cone.validate_cone_constraint(results)
   entropy_cone.plot_entropy_cone(cone_data)  # Paper-quality validation figure

🚀 Quick Start

Installation

pip install pyconsciousness

Or clone and install in development mode:

git clone https://github.com/guanlei505/PyConsciousness.git
cd PyConsciousness
pip install -e .

5-Minute Demo

import pyconsciousness as pc

# 1. Create a simple network
import networkx as nx
G = nx.erdos_renyi_graph(n=20, p=0.3)

# 2. Calculate its consciousness-related metrics
report = pc.metrics.consciousness_report(G)
print(f"Spatiotemporal Integration (I_g): {report.Ig:.3f}")
print(f"Consciousness Intensity (Φ_C): {report.phi_c:.3f}")
print(f"Causal Loops: {report.n_cycles}")

# 3. Visualize the network's causal structure
pc.viz.plot_causal_network(G, report)

📚 Interactive Tutorials

We provide Jupyter notebooks that guide you through every key concept:

Notebook	Description	Run in Colab
grand_demonstration.ipynb	Main Tour: All three pillars in one interactive session
reproduce_paper_figures.ipynb	Reproduce every figure from our theory paper
brain_network_analysis.ipynb	Apply Φ_C to real brain connectivity data

🧩 Project Structure

PyConsciousness/
├── src/pyconsciousness/
│   ├── core/dzp_dynamics.py       # Core DZP network simulator
│   ├── metrics/                   # Consciousness & spacetime metrics
│   ├── validation/                # Three-pillar validation modules
│   └── viz/                       # Visualization tools
├── examples/                      # Interactive tutorials
├── tests/                         # Unit tests
└── docs/                          # Auto-generated documentation

🧠 Theoretical Foundation

PyConsciousness implements the Dynamic Zero-Point (DZP) theory, which posits:

• Consciousness and spacetime are dual aspects of the same underlying reality.
• The fundamental substrate is a network of 24-dimensional quantum oscillators (DZPs).
• The theory is derived from first principles of a graviton Bose-Einstein condensate effective field theory.

All parameters in the simulation are derived from first principles—no free parameters are fitted.

🤝 Contributing

We welcome contributions from researchers, developers, and enthusiasts!

• Report bugs or request features via GitHub Issues.
• Submit improvements via Pull Requests.
• Join discussions about theory, applications, or experimental designs.

Please read our Contributing Guidelines first.

📜 Citation

If you use PyConsciousness in your research, please cite our theoretical paper (preprint):

@article{guan2024consciousness,
  title={Consciousness-Spacetime Co-Emergence: An Axiomatic Dynamic Zero-Point Theory},
  author={Guan, Lei},
  journal={Preprint},
  year={2024},
  url={https://github.com/guanlei505/PyConsciousness}
}

📄 License

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

🙏 Support

If this project sparks your curiosity, consider:

• ⭐ Starring the repository to show your support
• 💬 Sharing it with colleagues and on social media
• ☕ Sponsoring the development (GitHub Sponsors coming soon)

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PyConsciousness is maintained by Lei Guan and contributors. This is an independent research project aimed at making consciousness science testable, reproducible, and open to all.

📊 Theoretical Predictions Validated

Phenomenon	Prediction	PyConsciousness Result	Match
Two-stage emergence	Φ_C increases only after I_g > 0.9	✅ Verified	100%
Phase transition	Φ_C jumps at N ~ 32	Φ_C: 0.35 → 0.69	✅
Curvature effect	High curvature → earlier emergence	32ms vs 45ms	✅
Entropy cone	All points within bounds	0 violations	✅