sigma-c-framework 1.2.1

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

Sigma-C Framework v1.2.0

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 the new v1.2.0 "Full Power" demo
python -m sigma_c.examples_v1_2.demo_universal_rigor

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 Optimization (v1.2.0) - Balanced optimizers for Fidelity vs. Resilience
• 🆕 Publication-Ready Reporting (v1.2.0) - Automated LaTeX reports and Nature-style plots
• Universal Diagnostics System - 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

Universal Optimization (v1.2.0) 🆕

from sigma_c.optimization.quantum import BalancedQuantumOptimizer
from sigma_c.adapters.quantum import QuantumAdapter

# Initialize adapter and optimizer
adapter = QuantumAdapter()
optimizer = BalancedQuantumOptimizer(adapter)

# Optimize circuit parameters (balancing fidelity vs. noise resilience)
result = optimizer.optimize_circuit(
    circuit_factory=my_circuit_func,
    param_space={'epsilon': [0.01, 0.05], 'idle_frac': [0.1, 0.2]}
)

print(f"Optimal Params: {result.optimal_params}")
print(f"Critical Stability (Sigma-C): {result.sigma_c_after:.4f}")

Automated Reporting (v1.2.0) 🆕

from sigma_c.reporting.latex import LatexGenerator

report = LatexGenerator()
report.generate_report(
    title="Criticality Analysis",
    sections=[{'title': 'Results', 'content': 'System is stable.'}],
    filename="analysis_report"
)
# Generates analysis_report.tex and compiles to PDF

📄 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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