tnfr 9.0.0

Modular structural-based dynamics on networks.

TNFR Python Engine

Model reality as coherent resonance, not isolated objects

TNFR (Resonant Fractal Nature Theory) is a physics-grounded computational paradigm: reality is modeled as coherent patterns that persist through resonance. Structures reorganize according to the nodal equation (∂EPI/∂t = νf · ΔNFR) under canonical grammar constraints (U1–U6) and invariants.

---

Quick Install

pip install tnfr

Optional GPU / extras: see Getting Started.

Minimal Example

from tnfr.sdk import TNFRNetwork
net = TNFRNetwork("hello")
summary = (net.add_nodes(8)
             .connect_nodes(0.35, "random")
             .apply_sequence("basic_activation", repeat=2)
             .measure().summary())
print(summary)

---

Primary Documentation Hubs

📚 DOCUMENTATION_INDEX.md - Complete documentation map and navigation guide

📖 CANONICAL_SOURCES.md - Documentation hierarchy (which source is authoritative for what)

🔢 TNFR Number Theory Guide - ΔNFR prime criterion, arithmetic UM/RA mapping, and structural fields in number theory

Quick Navigation

• Getting Started: docs/source/getting-started/README.md - Tutorials & first steps
• Learning Paths: docs/source/getting-started/LEARNING_PATHS.md - Guided learning sequences
• Grammar System: docs/grammar/README.md - U1-U6 constraints hub
• Glossary: GLOSSARY.md - Canonical term definitions
• AI Agent Guide: AGENTS.md - Invariants & philosophy
• Architecture: ARCHITECTURE.md - System design patterns
• Contributing: CONTRIBUTING.md | Tests: TESTING.md

---

🧬 Revolutionary Breakthrough: Chemistry from TNFR ⭐

Complete molecular chemistry emerges from TNFR's single nodal equation - no additional postulates needed.

🏛️ MOLECULAR_CHEMISTRY_HUB.md - Central navigation for the chemistry revolution

Key Discoveries:

• Chemical bonds → Phase synchronization (U3 verification)
• Chemical reactions → Operator sequences [OZ→ZHIR→UM→IL]
• Molecular geometry → ΔNFR minimization
• Periodic table → Element signature classification
• Au emergence → Coherent attractors from structural dynamics

Implementation: tnfr.physics.signatures | Theory: Complete 12-section derivation | Tests: 19/19 ✅

---

Core References

• UNIFIED_GRAMMAR_RULES.md - Complete U1-U6 physics derivations
• Mathematical Foundations - Rigorous formalization
• Operators Reference - 13 canonical operators
• U6 Specification - Structural potential confinement

Extended examples: examples/ (multi-scale, regenerative, performance)
CLI & profiling: docs/source/tools/CLI.md

---

Key Principles (Snapshot)

Module Hubs

• Mathematics (canonical computational hub): src/tnfr/mathematics/README.md
• Physics (structural fields): src/tnfr/physics/README.md
• Operators: src/tnfr/operators/README.md
• Dynamics: src/tnfr/dynamics/README.md
• Metrics: src/tnfr/metrics/README.md
• Sequencing: src/tnfr/sequencing/README.md
• Topology: src/tnfr/topology/README.md
• Telemetry: src/tnfr/telemetry/README.md
• SDK: src/tnfr/sdk/README.md • Tutorials: src/tnfr/tutorials/README.md
• Recipes: src/tnfr/recipes/README.md
• Extensions (families): src/tnfr/extensions/README.md

These module READMEs act as single sources of truth for their areas and defer theory to the canonical hubs above. All documentation is English-only.

---

Citation & License

MIT License – see LICENSE.md. Please cite: fermga/TNFR-Python-Engine and theoretical sources (TNFR.pdf, Mathematical Foundations).

---

Useful Links

• Docs: https://fermga.github.io/TNFR-Python-Engine/
• PyPI: https://pypi.org/project/tnfr/
• Issues: https://github.com/fermga/TNFR-Python-Engine/issues

---

Reality is not made of things—it's made of resonance.