shunollo 0.3.9

Biomimetic Physics Engine for Cognitive Architectures

Shunollo - The Biomimetic Physics Engine

A Universal Physics Engine for Cognitive Architectures

Shunollo provides a pure, agnostic physics layer for translating any data stream into sensory qualia - enabling AI systems to "feel" their environment through entropy, roughness, viscosity, and other universal metrics.

[!IMPORTANT] Status: Production Ready (v0.3.9)

• Physics: Modular (Thermodynamics, Quantum, Optics, Mechanics)
• Integrity: 228 Tests (Unit, Integration, Functional, Performance)
• Performance: Sub-millisecond latency (<0.1ms)
• Safety: Verified (Refractory Period, Thermal Limits)

100% Open Source

Shunollo is fully open source under the Apache 2.0 license. There is no "Enterprise Edition" or paid tier of the library itself. You get everything.

Build whatever you want. That's why we made this.

Key Features

Physics Engine (Multivariate Sensory Qualia)

Transform any data stream into a normalized physics fingerprint:

• Mechanics: Energy, Entropy, Roughness, Viscosity
• Thermodynamics: Temperature, Landauer Cost, Arrhenius Rates
• Quantum: Radical Pair coherence, Tunneling
• Time Series: Volatility, Lyapunov Exponents, Poisson Detection
• High-Order: Dissonance, Hamiltonian, Lagrangian Action

Physics-RAG (Integrated v0.3.9) 🆕

Retrieval-Augmented Generation for Sensation - your AI remembers what it has "felt" before:

from shunollo_core.memory.hippocampus import Hippocampus
from shunollo_core.models import ShunolloSignal

# Store an experience
hippo = Hippocampus()
signal = ShunolloSignal(energy=5.0, roughness=0.8, entropy=6.5)
hippo.remember(signal)

# Later: Déjà Vu - "Have I felt this before?"
query_vector = new_signal.to_vector(normalize=True)
similar_episodes = hippo.recall_similar(query_vector, k=3)

if similar_episodes:
    past_signal, distance = similar_episodes[0]
    print(f"Déjà Vu! This feels like {past_signal.timestamp} (distance: {distance})")

Why it matters: Enables One-Shot Learning. Detect an anomaly once, store the sensory signature, recognize it instantly on reoccurrence.

Architecture

shunollo/
├── shunollo_core/       # Pure Physics (Math only)
│   ├── physics/         # Mechanics, Thermo, Quantum, etc.
│   ├── brain/           # Autoencoder (Imagination)
│   ├── cognition/       # Active Inference + DDM
│   └── memory/          # Holographic + Hippocampus
└── shunollo_runtime/    # Nervous System (Event Bus)

graph LR
    subgraph Shunollo Core
    A[Physics Engine] --> B[18-dim Vector]
    B --> M[Hippocampus]
    M -->|Déjà Vu| B
    end
    
    subgraph Shunollo Runtime
    B --> C((Thalamus Bus))
    C --> D[Neural Cortex]
    C --> E[Reflex Agent]
    end
    
    D & E --> F[Decision]

Installation

pip install shunollo

Quick Start

# Pure physics calculation
from shunollo_core.physics.mechanics import calculate_entropy, calculate_roughness
entropy = calculate_entropy(data)
roughness = calculate_roughness(entropy, jitter=0.1)

# Create a sensory signal
signal = ShunolloSignal(
    energy=1.5,
    entropy=entropy,
    roughness=roughness,
)

# Get 13-dimensional sensory vector
vector = vectorize_sensation(signal.to_dict())

# Store in episodic memory
hippo = Hippocampus()
hippo.remember(signal)

# Check novelty: "How new is this sensation?"
novelty = hippo.get_novelty_score(signal.to_vector())
if novelty > 1.0:
    print("Novel pattern detected!")

License

Apache 2.0 - See LICENSE

Documentation

• THE_SHUNOLLO_CODEX.md - Philosophy & Vision
• docs/EXAMPLES.md - 4 Real-World Examples (Finance, Health, IoT, DevOps)
• docs/whitepapers/ - Physics Theory
• docs/technical/SENSORY_LEXICON.md - Sensory Vocabulary
• docs/technical/BRAIN_MAP.md - Neural Architecture
• docs/TESTING.md - Testing Strategy & Validation

Community

• 📖 Roadmap - See what's coming
• 🐛 Issue Tracker - Report bugs
• 💬 Discussions - Ask questions

Contributing

We welcome research contributions. Please see CONTRIBUTING.md for architectural rules and setup instructions.

Note: By contributing, you agree to our Contributor License Agreement.