Phi-Complexity

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Code quality metrics based on Golden Ratio (φ) mathematical invariants

phi-complexity is the first code quality library that measures the health of your Python code using universal mathematical invariants derived from the Golden Ratio (φ = 1.618...).

Unlike pylint (cultural rules) or radon (McCabe metrics), phi-complexity answers:

"Is this code in resonance with the natural laws of order, or is it collapsing under its own entropy?"

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⚡ Quick Start

pip install git+https://github.com/spockoo/Phi-Complexity.git

Note : le paquet est aussi sur PyPI (pip install phi-complexity, 0.12.0) — identique à cet arbre depuis le 2026-10-01.

# Audit a file
phi check my_script.py

# Audit a folder
phi check ./src/

# Generate a Markdown report
phi report my_script.py --output report.md

# CI/CD strict mode (exit 1 if radiance < 75)
phi check ./src/ --min-radiance 75

# Freeze the structural state (reference)
phi snapshot ./src/ --out ref.json

# Watch for silent degradations
phi veille ./src/ --ref ref.json

Python API

from phi_complexity import auditer, rapport_console, rapport_markdown

# Get metrics as a dict
metrics = auditer("my_script.py")
print(metrics["radiance"])          # → 82.4
print(metrics["statut_gnostique"])  # → "EN ÉVEIL ◈"
print(metrics["oudjat"])            # → {"nom": "process_data", "ligne": 42, ...}

# Print console report
print(rapport_console("my_script.py"))

# Save Markdown report
rapport_markdown("my_script.py", sortie="report.md")

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📊 Metrics

Metric	Description	Mathematical basis
Radiance Score	Global quality score (0–100)	100 - f(Lilith) - g(H) - h(Anomalies) - i(Fib)
Variance de Lilith	Structural instability	Population variance of function complexities
Shannon Entropy	Information density	H = -Σ p·log₂(p)
φ-Ratio	Dominant function ratio	max_complexity / mean → should tend toward φ
Fibonacci Distance	Natural size alignment	`Σ
Zeta-Score	Global resonance	ζ_meta(functions, φ) converging series

Gnostic Status Levels

Score	Status	Meaning
≥ 85	HERMÉTIQUE ✦	Stable, harmonious, production-ready
60–84	EN ÉVEIL ◈	Potential exists, some entropy zones
< 60	DORMANT ░	Deep restructuring recommended

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🧭 Command Stability Matrix

Command	Stability	Purpose
phi check	Stable	Audit radiance for files/folders (console, JSON, SARIF) + quality gates
phi report	Stable	Markdown report
phi index	Stable	Project map: symbols, collisions, φ-health
phi snapshot	Stable	Freeze structural state (dated, MD5-signed reference)
phi veille	Stable	Detect silent degradations vs reference (STABLE / DÉGRADATION DÉTECTÉE / INSTRUMENT DÉGRADÉ)
phi chemins	Stable	Belief map: symbols ranked by posterior — which hole to attack first
phi oracle	Stable	Exhibited reasoning chain (prior → evidence → posterior → action)
phi sonde	Stable	A/B probes: tracing the unconditional from the conditional (closure pole / obstruction pole)
phi piste-sorry	Nouveau	Track a Lean 4 sorry: rigorous inventory, import graph, registry work-items — the path from any complexity
phi chemins-verifiables	Nouveau	Guided wiring candidates, each formally identified as a proof — Lean decides (PROUVÉ / RÉFUTÉ / INDÉCIDÉ)
phi infer	Experimental	Bayesian inference and self-suture prediction
phi explorer	Experimental	Interactive explorer (self-contained HTML, zero network)
phi entropie	Experimental	Entropy lens: the ciphered ledger of narrowing (H and ΔH in bits, EFT bounds)
phi ou-aller	Experimental	Where to go next: attention list (not a decision)
phi edit	Experimental	Edit a file in the phi TUI editor

Tip: keep phi check/phi report/phi oracle in CI. Use phi snapshot + phi veille to catch silent regressions between releases.

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🔍 Sample Output

╔══════════════════════════════════════════════════╗
║      PHI-COMPLEXITY — AUDIT DE RADIANCE          ║
╚══════════════════════════════════════════════════╝

  📄 Fichier : my_script.py
  📅 Date    : 2026-04-08 17:11

  ☼  RADIANCE     : ██████████████░░░░░░  72.6 / 100
  ⚖  LILITH       : 11221.9  (Structural variance)
  🌊 ENTROPIE     : 2.48 bits  (Shannon)
  ◈  PHI-RATIO    : 3.43  (ideal: φ = 1.618, Δ=1.81)
  ζ  ZETA-SCORE   : 0.3656  (Global resonance)

  STATUT : EN ÉVEIL ◈

  🔎 OUDJAT : 'process_data' (Line 42, Complexity: 376)

  ⚠  SUTURES IDENTIFIED (2):
  🟡 Line 18 [LILITH] : Nested loop (depth 2). Consider a helper function.
     >> for j in range(b):
  🟢 Line 67 [LILITH] : 'load_data' receives 6 arguments. Encapsulate in an object.
     >> def load_data(path, sep, enc, cols, dtype, na):

---

🛡 La Veille — the instrument never lies silently

A bad instrument fails silently. phi-complexity fails loudly :

• phi veille never reports ✅ on top of a degraded analyzer — it reports INSTRUMENT DÉGRADÉ (exit 3) instead of a fake degradation;
• phi snapshot refuses a degraded baseline without --force;
• every development of the instrument carries its mission justification (phi_complexity/mission.py) : which goal it serves. A module added without justification fails the test suite — mechanically, not by discipline.

Trust in the tool is verified trust, never faith : 386 tests, run on every change.

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🧮 Mathematical Foundations

The Radiance Formula is derived from:

• φ-Meta Framework (Tomy Verreault, 2026) — Axioms AX-A0 through AX-A58
• Law of Antifragility (EQ-AFR-BMAD): φ_{t+1} = P_φ(φ_t + k·Var(E_t)·E_t)
• Cybernetics — Feedback and variance as control metrics
• Shannon Information Theory — Code as an information channel

The Sovereign Coding Rules are derived from:

• The C Book (Banahan, Brady, Doran) — Scope hermeticity, resource lifecycle
• JaCaMo / Multi-Agent Programming — Agent independence and encapsulation

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🏗 Sovereign Architecture

Zero heavy dependencies.
Pure Python standard library (ast, math, json) + tree-sitter (optional, for non-Python languages).

phi_complexity/
├── core.py               ← Golden constants (PHI, TAXE_SUTURE, ETA_GOLDEN...) + VERSION
├── analyseur.py          ← AST fractal dissection
├── metriques.py          ← Radiance Index calculation
├── rapport.py            ← Console / Markdown / JSON rendering
├── cli.py                ← the 15 phi commands
├── veille.py             ← Silent-degradation detection (STABLE / DÉGRADATION DÉTECTÉE)
├── mission.py            ← Mission-justification registry (anti-divergence guard)
├── croyances.py          ← Bayesian belief layer over symbols
├── sondes.py             ← A/B probes (closure / obstruction poles)
├── piste_sorry.py        ← Rigorous sorry inventory + import graph (Lean 4)
├── chemins_verifiables.py← Guided wiring candidates Lean formally decides
├── parseur_lean.py       ← Robust Lean parser (regex-based, no size limit)
├── dependances.py        ← Lean dependency graph (exact deps, transitive closure)
├── entropie.py           ← Entropy lens (H, ΔH, EFT bounds)
├── oracle.py             ← Exhibited reasoning traces
├── explorateur.py        ← Interactive explorer
├── bayes.py / eft.py     ← Bayesian EFT machinery, error-free transformations
├── carte.py / ancrage.py ← Maps, anchoring
├── durcissement.py       ← Hardening battery
├── modeles.py            ← Models
├── editeur/              ← TUI editor (phi edit)
├── formules/             ← Formula language
└── langs/                ← Multi-language backends (Python native, tree-sitter generic)

---

🔗 Integration

GitHub Action

- name: Phi-Complexity Audit
  run: |
    pip install git+https://github.com/spockoo/Phi-Complexity.git
    phi check ./src/ --min-radiance 75

Watchdog (snapshot + veille)

phi snapshot ./src/ --out ref.json   # after each audited release
phi veille ./src/ --ref ref.json     # at the start of the next work session

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📜 License

MIT — Tomy Verreault, 2026

Anchored in the Bibliothèque Céleste — Morphic Phi Framework (φ-Meta)

📜 Historique des Versions (Changelog)

• v0.13.0 (Extraction robuste) :

  • Nouveau phi_complexity/dependances.py : graphe de dépendances Lean — extraction exacte (fermeture transitive, tri topologique). Élimine le danger d'oubli d'une dépendance.
  • Nouveau extraire_declarations_robuste() dans parseur_lean.py : extraction par regex, pas de limite de taille. Corrige l'échec silencieux de tree-sitter sur fichiers >5000 lignes (147 → 196 déclarations).
  • Corrigé : extraction du corps par délimitation exacte (entre deux déclarations) au lieu de l'heuristique 50 lignes.

• v0.12.0 (Chemins vérifiables) :

  • Nouveau phi piste-sorry : piste d'un sorry Lean 4 — inventaire rigoureux (commentaires/chaînes exclus), graphe d'imports, chantiers du registre.
  • Nouveau phi chemins-verifiables : propose des candidats de câblage guidés, chacun identifié formellement comme une preuve ; Lean tranche mécaniquement (PROUVÉ / RÉFUTÉ / INDÉCIDÉ). Jamais le sorry lui-même, jamais un sorry déguisé.
  • Vocabulaire : « terminer » remplace les termes violents pour les hypothèses closes.
  • Exclusion des artefacts .lake des scans (vitesse, pas de bruit Mathlib).

• v0.11.0 (La Veille) — release GitHub :

  • Garde anti-divergence : chaque module porte sa justification « but de mission » (mission.py) — un module sans justification fait échouer les tests.
  • Durcissement : l'instrument échoue bruyamment (INSTRUMENT DÉGRADÉ, exit 3) au lieu de mentir quand l'analyseur manque ; phi snapshot refuse une baseline dégradée sans --force.
  • 386 tests verts, multi-langages (Python natif + tree-sitter générique).

Note d'honnêteté : les numéros 0.1.1–0.2.2 publiés avant octobre 2026 couvraient du scaffolding généré par bots, supprimé lors du nettoyage du 2026-10-01 (seule la 0.1.0 a été conservée). PyPI sert désormais la 0.12.0, identique à cet arbre. L'historique fiable commence à v0.11.0.