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PCE Setup Guide

This guide will help you set up and run the Pan-Omics Consciousness Engine (PCE) on your system.

Prerequisites

  • Python: 3.8 or higher
  • Operating System: Windows, macOS, or Linux
  • Memory: At least 4GB RAM (8GB recommended)
  • Storage: 2GB free space

Installation Steps

1. Clone the Repository

git clone https://github.com/pce-project/pce.git
cd pce

2. Set Up Python Environment

conda create -n pce python=3.8
conda activate pce

Using venv:

python -m venv pce_env
# On Windows:
pce_env\Scripts\activate
# On macOS/Linux:
source pce_env/bin/activate

3. Install Dependencies

pip install -e .

4. Verify Installation

python -c "import pce; print(f'PCE v{pce.__version__} ready!')"

Expected output: 

PCE v1.0.0-alpha ready!

Quick Test

Run the complete system test to verify everything is working:

import pce

# Create test dataset
data = pce.create_toy_dataset('toy_genomics', 10, 5)
print(f'Dataset: {data.name} with {len(data.get_all_entities())} entities')

# Run consciousness analysis
metrics = pce.quick_consciousness_analysis(
    data, 
    integration_cycles=3, 
    save_results=False
)

# Display results
print(f'Consciousness φ (Phi): {metrics.phi:.6f}')
print(f'Consciousness Level: {metrics.consciousness_level:.6f}')
print(f'Category: {metrics.consciousness_category.name}')

Expected output: 

Dataset: Toy Genomics Dataset with 5 entities
Consciousness φ (Phi): 0.000639
Consciousness Level: 0.107696
Category: SUBCONSCIOUS

Optional Dependencies

For enhanced performance and features, install optional dependencies:

# Scientific computing (recommended)
pip install scipy scikit-learn

# Visualization (optional)
pip install matplotlib seaborn plotly

# Development tools (if contributing)
pip install pytest black flake8 mypy

System Configuration

PCE can be configured through environment variables:

# Set log level
export PCE_LOG_LEVEL=INFO

# Set number of parallel processes
export PCE_NUM_PROCESSES=4

# Enable GPU acceleration (if available)
export PCE_USE_GPU=true

Troubleshooting

Common Issues

  1. Import Error: No module named 'pce' 

    # Make sure you installed with -e flag
pip install -e .

  2. Numerical Warnings

  3. These are normal for small datasets and don't affect functionality

  4. Can be suppressed with: export PCE_SUPPRESS_WARNINGS=true

  5. Slow Performance

  6. Install optional dependencies: pip install scipy scikit-learn
  7. Reduce integration cycles for testing: integration_cycles=2

  8. Use smaller datasets for initial testing

  9. Memory Issues

  10. Reduce population size in E³DE: modify config files
  11. Use fewer simulation steps in HDTS
  12. Process smaller datasets initially

Getting Help

If you encounter issues:

  1. Check the FAQ
  2. Search existing issues
  3. Create a new issue with:
  4. Your operating system
  5. Python version
  6. Complete error message
  7. Steps to reproduce

Next Steps

Once PCE is installed and verified:

  1. Explore Examples: Check examples/ directory for usage patterns
  2. Read Documentation: See detailed API documentation
  3. Run Benchmarks: Test with different dataset sizes
  4. Contribute: See contributing guidelines for development setup

Performance Expectations

For the toy genomics dataset (5 entities):

  • Hypergraph Construction: ~0.003s
  • Neural Network Encoding: ~0.15s
  • Quantum Optimization: ~4.4s
  • Evolutionary Simulation: ~0.3s
  • Multi-scale Simulation: ~90s
  • Consciousness Integration: ~0.15s
  • Total Runtime: ~95s

Performance scales with dataset size and system complexity.

System Requirements by Use Case

Research/Development

  • CPU: 4+ cores recommended
  • RAM: 8GB minimum, 16GB recommended
  • Storage: 5GB for development environment

Production/Large Datasets

  • CPU: 8+ cores, preferably with AVX support
  • RAM: 32GB+ recommended
  • GPU: CUDA-compatible GPU for acceleration
  • Storage: SSD recommended, 50GB+ for large datasets

Congratulations! You now have a working PCE installation. 🎉