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TNFR-Python-Engine: Resonant Fractal Nature Theory Implementation

Martinez Gamo, F. F.
Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

TNFR-Python-Engine is a Python framework for coherent-pattern analysis on graph-coupled networks. The nodal relation dEPI/dt = nu_f DeltaNFR organizes a declared evolution model; pressure, phase, capacity, support and any additional forcing require their own specifications. The engine provides 13 registered structural operators, grammar U1-U6, shared integration and the structural-field tetrad: potential, phase-gradient magnitude, circular phase curvature and coherence length. These fields are diagnostics; they do not reconstruct every nodal state or independently select its evolution. Operator contracts distinguish primary effects, auxiliary writes, grammar admission and execution provenance. Version 0.0.3.6 integrates scoped diffusion, memory, phase/form and quotient research with corrected documentation. Exact identities, conditional theorems, represented finite observations, auxiliary wave/Hamiltonian/arithmetic models and open hypotheses are identified separately. Passing tests is not proof of unrestricted stability, autonomous physical emergence or a Millennium conjecture. The research execution plan remains the single source of current priorities. The shared SDK study API and CLI construct and execute declared operator-word studies through the existing engine. JSON recipes and reports retain supplied initial state, software versions, diagnostic availability and coherence-length provenance. Recipe cycles are operator-word counts, not physical seconds, and recipes are not complete checkpoints or cross-version replay guarantees. The release consolidates technical guides and test helpers, removes the retired educational manuscript and obsolete standalone demonstrations, reuses existing numerical evaluations, and validates documentation against implemented contracts. Scoped regression checks and historical research evidence remain distinct from retired toy tests and heuristic campaigns. See the 0.0.3.6 release, versioned source, Python package and current documentation.

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