sigma-c-framework 1.2.0

Critical Susceptibility Framework for Quantum, GPU, Financial, Climate, Seismic, and Magnetic analysis

Sigma-C Framework v1.1.0

Copyright (c) 2025 ForgottenForge.xyz

Critical Susceptibility Framework for Quantum, GPU, Financial, Climate, Seismic, and Magnetic analysis.

🚀 Quick Start

# Install the package
pip install sigma-c-framework

# Run examples
python -m sigma_c.examples.demo_quantum

Or clone and install from source:

git clone https://github.com/forgottenforge/sigmacore.git
cd sigmacore/sigma_c_framework
pip install .

🎯 What is Sigma-C?

Sigma-C detects critical phase transitions in complex systems using Critical Susceptibility (χ) theory. Unlike traditional metrics, it identifies the precise scale where systems undergo fundamental structural changes.

Use Cases:

• 🔬 Quantum Computing: Find noise thresholds that break quantum algorithms
• 🎮 GPU Optimization: Auto-tune kernels to avoid cache thrashing
• 💰 Finance: Predict market crashes before they happen
• 🌍 Climate Science: Identify characteristic scales of weather systems
• 🌋 Seismology: Detect critical stress states in earthquake catalogs
• 🧲 Magnetism: Analyze phase transitions (Curie temperature)

📦 Features

• 6 Domain Adapters ready for production use
• 🆕 Universal Diagnostics System (v1.1.0) - Auto-search, validation, and recommendations
• High-Performance C++ Core with Python bindings
• Statistical Robustness via bootstrap and permutation tests
• Comprehensive Documentation in English and German
• Dual License: AGPL-3.0 or Commercial

📚 Documentation

• Quick Start: See QUICKSTART.md (5 minutes)
• Full Documentation: See DOCUMENTATION.md (English + German)
• Changelog: See CHANGELOG.md

💡 Example

Basic Usage (v1.0.0)

from sigma_c import Universe

# Detect GPU performance critical point
gpu = Universe.gpu()
result = gpu.auto_tune(alpha_levels=[0.1, 0.5, 0.9])

print(f"Critical threshold: {result['sigma_c']:.3f}\"")
print(f"Stability score: {result['statistics']['kappa']:.2f}")

With Diagnostics (v1.1.0) 🆕

from sigma_c import Universe

# Step 1: Diagnose your setup
gpu = Universe.gpu()
diag = gpu.diagnose()

if diag['status'] == 'ok':
    # Step 2: Auto-search optimal parameters
    search = gpu.auto_search()
    print(f"Optimal alpha: {search['best_params']['alpha']:.2f}")
    
    # Step 3: Get human-readable explanation
    result = gpu.auto_tune(alpha_levels=[search['best_params']['alpha']])
    print(gpu.explain(result))

📄 License

Dual-licensed under AGPL-3.0 or Commercial License.

• Open Source: See license_AGPL.txt
• Commercial: Contact [e-mail]

For commercial licensing without AGPL-3.0 obligations, contact: [e-mail]

🤝 Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests.

🙏 Acknowledgments

The Sigma-C Framework builds upon the excellent work of the open-source community. We gratefully acknowledge the following projects:

Core Dependencies

• NumPy - Fundamental package for scientific computing
• SciPy - Scientific computing library for optimization and statistics
• pandas - Data analysis and manipulation library
• scikit-learn - Machine learning library for statistical analysis
• pybind11 - C++/Python interoperability

Domain-Specific Libraries

• CuPy - GPU-accelerated computing (optional for GPU adapter)
• yfinance - Financial market data (for Financial adapter)
• tqdm - Progress bars for long-running computations

Visualization & Analysis

• matplotlib - Plotting and visualization
• seaborn - Statistical data visualization

Build & Development Tools

• CMake - Cross-platform build system
• setuptools - Python package building
• wheel - Python package distribution format

We are deeply grateful to the maintainers and contributors of these projects for making the Sigma-C Framework possible.

📧 Contact

• Email: [e-mail]
• GitHub: github.com/forgottenforge/sigmacore
• Issues: github.com/forgottenforge/sigmacore/issues

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