This repository contains minimal, self-contained code to explore the coherence-gradient chaos framework introduced in:

Valamontes, A., Chaos as a Coherence–Gradient Phenomenon:
A DLSFH–SGCV–MC Analysis (manuscript).

The goal is to provide a concrete numerical playground for the model-dependent results discussed in the paper, including:

• The linearized ∞–tensor propagator $\mathcal{T}_{\infty}$.
• The spectral-radius / operator-norm instability criterion.
• The three-node VID chain example.
• Numerical estimation of the coherence-gradient Lyapunov exponent $\lambda_{\mathrm{CG}}$.
• Simple experiments linking VID-like circulation parameters to divergence rates.

The code is intentionally lightweight and readable, intended as a starting point for more elaborate DLSFH/VID simulations.

Create and activate a fresh environment (conda or venv recommended), then install dependencies from requirements.txt:

pip install -r requirements.txt

Core dependencies:

• numpy — core linear algebra
• scipy — optional (spectral radius, stability checks)
• matplotlib — plotting
• networkx — optional lattice/graph tooling

chaos_cg_model/
  README.md               # This file
  requirements.txt        # Python dependencies

  src/
    cg_model.py           # Core coherence-gradient model + utilities
    example_three_node.py # Reproduces the 3-node VID chain from the paper
    vid_lattice_demo.py   # Skeleton for DLSFH-like lattice experiments

  tests/
    test_divergence.py
    test_operator_norm.py
    test_lyapunov_coarse.py

  notebooks/
    chaos_three_vertex.ipynb
    vid_circulation_instability.ipynb

All scripts correspond directly to sections of the manuscript (see Appendix A).

To reproduce the three-node VID chain analyzed in the paper:

cd chaos_cg_model
python -m src.example_three_node

This script:

1. Constructs the 3×3 propagator $\mathcal{T}_{\infty}^{(3)}$
   using parameters (eta, alpha, beta).
2. Evolves two nearby coherence configurations for N steps.
3. Computes the distance trajectory
   $D_n = | \Psi_n - \Psi'_n |$.
4. Fits $\lambda_{\mathrm{CG}}$ from a linear regression of log(D_n) vs n.
5. Compares against the model expectation
   $\log(1+\eta)$.

If matplotlib is installed, diagnostic plots are also generated.

The numerical model implements these ideas:

• Evolution of a discrete coherence field represented by
  $\Psi_t \in \mathbb{R}^N$.
• A linearized update rule
  $\Psi_{t+1} = \mathcal{T}_{\infty} \Psi_t$.
• A coherence-gradient Lyapunov exponent $\lambda_{\mathrm{CG}}$ defined by
  exponential growth of distances between nearby states.
• Instability driven by anisotropies in a coherence-gradient field.
• Theoretical linkage to VID loop circulation.

The code does not attempt full DLSFH or SGCV physics — it reproduces only the mathematical instability structure used in the manuscript.

Several natural directions for extending this package:

• Replace the 3×3 matrix with larger propagators modeling full VID subgraphs.
• Examine how “circulation parameters’’ in vid_lattice_demo.py influence
  $\lambda_{\mathrm{CG}}$.
• Introduce explicit dependence of $\mathcal{T}_{\infty}$
  • on a discrete coherence-gradient field $G_{ab}(v)$.
• Perform parameter scans over $(\eta,\alpha,\beta)$ to map regions where
  $\rho(\mathcal{T}_{\infty}) > 1$.
• Compare operator-norm growth with numerically extracted Lyapunov exponents.

These experiments mirror the interpretive structure of the manuscript.

• All randomness uses fixed seeds.
• No external data files required.
• Every experiment runs in < 1.5 seconds on a modern laptop.
• Each numerical claim in the paper corresponds to a test or notebook:

See TESTS.md for details.

If you use this package, please cite:

Valamontes, A., Chaos as a Coherence–Gradient Phenomenon:
A DLSFH–SGCV–MC Analysis (manuscript).