QSignature 1.0.3

Data Physics Framework for Physical Systems

QSignature

Model-free dynamical regime classification for time series data.

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🙏 Acknowledgments

This work was developed with the assistance of DeepSeek AI, which provided technical guidance, code review, and documentation support throughout the development of the QSignature framework.

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🤝 Contributing

Contributions are welcome! Please see our Contributing Guidelines.

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📄 License

MIT License — see LICENSE.txt for details.

📖 Overview

QSignature is a Data Physics framework that extracts physical signatures from causal response signals — without assuming any model. It transforms raw time-series data into diagnostic fingerprints that reveal the underlying physical regime, memory structure, and dynamical properties.

Key Capabilities:

• Model-Free — No assumptions about system order, linearity, or excitation
• Data-Driven — Extracts everything directly from the signal
• Universal — Works for any causal response data
• Interpretable — Each diagnostic has clear physical meaning

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🚀 Quick Start

import numpy as np
from QSignature import compute_all, QPDF, QSpace, QSynthetic

# Generate synthetic data
t = np.linspace(0, 10, 1000)
R = QSynthetic.physical.exponential_step(t, tau=2.0, R_inf=1.0)

# Core analysis — timescales and ratios
results = compute_all(t, R)
print(f"τ_s = {results['tau_s']:.4f}")
print(f"τ_u = {results['tau_u']:.4f}")
print(f"Δ_su = {results['Delta_su']:.4f}")
print(f"ρ₂₃ = {results['rho_23']:.4f}")

# Full signature with PDF analysis
results = compute_all(t, R, return_pdf=True)
print(f"PDF Shape: {results['pdf_shape']}")
print(f"Entropy: {results['entropy']:.4f}")
print(f"Time Signature: {results['time_signature']['signature']}")