QSignature

Data Physics Framework.

📖 Overview

QSignature is a Data Physics framework that extracts physical signatures from causal response data (R(t)). It transforms raw response data into diagnostic fingerprints that reveal the underlying physical regime, memory structure, dynamical properties and more.

Key Capabilities:

• Assumption free — No assumptions about system order, linearity, or excitation
• Data-Driven — Extracts Physics directly from response data R(t)
• Domain agnostic — Works for any causal response signal
• Interpretable — Every output has a physical meaning

🚀 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']}")

📦 Installation

pip install QSignature

For development installation:

git clone https://github.com/1030ahmad1030/QSignature.git
cd QSignature
pip install -e .

🧩 Modules

📊 Core Estimators

Timescales (8)

Higher Moments (3)

Diagnostic Ratios (18)

📈 Full Signature (64 Features)

# Get everything — QSignature + QPDF + Time Signature
results = compute_all(t, R, return_pdf=True)

# Access all features
print(f"τ_s = {results['tau_s']:.4f}")
print(f"Δ_su = {results['Delta_su']:.4f}")
print(f"PDF Shape: {results['pdf_shape']}")
print(f"Entropy: {results['entropy']:.4f}")
print(f"Peaks: {results['n_peaks']}")
print(f"Time Signature: {results['time_signature']['signature']}")

📚 Documentation

• Official Docs: https://qsignature.readthedocs.io/
• GitHub: https://github.com/1030ahmad1030/QSignature
• PyPI: https://pypi.org/project/QSignature/

Papers

• QSignature 1.0 Framework: IEEE ISDFS 2026
• Theorems for Environmental Signature: Research Square

🛠️ Requirements

• Python >= 3.9
• numpy >= 1.21.0
• scipy >= 1.7.0
• pandas >= 1.3.0
• matplotlib >= 3.4.0
• scikit-learn >= 1.0.0

👥 Contributors

The QSignature framework was developed by:

📄 License

MIT License — see LICENSE.txt for details.

📝 Citation

If you use QSignature in your research, please cite:

@inproceedings{qsignature2026_isdfs,
  title={QSignature 1.0: A Dynamical Regime Classification Framework for Causal Time Series Data},
  author={Muhammad, Ahmad and Danbatta, Salim Jibrin and Isah, Muhammad Abubakar and others},
  booktitle={IEEE ISDFS 2026},
  year={2026}
}