Replication Dataset and Code for the Universal Law of Coherence (Entropy Diffusion Tests)

This dataset provides the raw numerical values, Python scripts, and example outputs used to test the proposed Universal Law of Coherence under entropy growth conditions. Contents CSV file with sample numerical values for energy (E), surrogate entropy, and coherence gap (Δ). Python script that simulates 1D diffusion under entropy growth, computes the coherence gap Δ = I_P(x_t; x_{t+1}) – I_Q(x_t; x_{t+1}), and evaluates endurance using a simple entropy-growth proxy. Example plots and output logs illustrating the fitted relationship between log(E/E₀) and Δ. Purpose This package provides a minimal reproducible implementation of coherence gap testing. The endurance metric is implemented here in its simplest form, but the script is modular: alternative endurance definitions (e.g., Lyapunov rate, signal decay horizon) can be substituted for further testing. The goal is to enable independent replication and critique. If the relationship breaks under valid assumptions, this will serve as evidence against the law. If it holds consistently across definitions, it suggests a robust scaling relation worth deepe r examination.