Spanda Engine™ & PranaSphere™ – by PUTMAN

Symbolic cognition infrastructure for emotional AI, narrative engines, and tonal NPCs.
This Phase 1 release includes the core pipeline from tone signal detection to field resonance and contextual modulation.

Spanda Engine Pre-Demo

🚀 Live Demo: https://spanda-engine-predemo.vercel.app

This is a simplified public preview of the Spanda Engine™ — a system for real-time emotional resonance modeling, symbolic tone parsing, and recursive narrative logic.

The full PUTMAN Suite of Emotive Software will be released in stages.

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📋 Prerequisites

• Node.js v16+ (recommended: v18+)
• npm v8+

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📥 Installation

git clone https://github.com/putmanmodel/spanda-pranasphere.git
cd spanda-pranasphere
npm install

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🔁 Quickstart – “Hello, Tone”

npm run liveNuanceTest
# or
npm run test:nuance

This simulates the full flow:

• Injects mock tone signals
• Detects arcs from deviation
• Tracks resonance in the PranaSphere
• Runs contextual inference

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🧩 Core Modules

Module	Description	Sample Output
stackedDeviation.ts	Detects arcs from emotional deviation	arcType: 'swing'
toneArcMemory.ts	Tracks tone memory across poles	avgDeviation: 0.35
contextualToneInference.ts	Infers alignment between tone + mood	resonance: 'aligned'
memoryBuffer.ts	Temporary buffer for tone snapshots	n/a
liveNuanceTest.ts	Runs the full Spanda + PranaSphere stack	Full inference output

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🧠 Mermaid Flow – Tone to Modulation

flowchart TD
    A["Tone Input (Vector)"] --> B["ToneSig Generation"]
    B --> C["Deviation Analysis"]
    C --> D["Arc Classification"]
    D --> E["PranaSphere Update"]
    E --> F["Contextual Tone Inference"]
    F --> G["Modulation Strategy"]

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🛠️ Developer Notes

npm run lint
npm run typecheck

• Cover edge cases: sarcasm flips, zero-deviation, conflicting poles
• Write unit tests for each module under src/

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🔬 Edge Case Tests

To run symbolic tone engine tests against curated edge case scenarios:

npm run edge-case -- <case-id>

Available Test Cases

ID	Description
sarcasm-flip	High-tone input meant ironically — initial positivity flips to sharp negative.
flatline-no-deviation	Repeated neutral inputs show no emotional swing — stability with zero arc.
clashing-signals	Two poles receive opposing inputs — system must choose dominant or flag conflict.
overamplification-drift	Rapid tone escalation leads to runaway loop — tests intensity vs decay balance.

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📊 Sample Output Results

Case ID	Arc Type	Peak Pole	Avg Deviation	Inference Summary
sarcasm-flip	surge	1	-0.20	Clashing → Redirect; then Neutral → Dampen
flatline-no-deviation	flat	2	0.00	Both aligned → Amplify
clashing-signals	swing	4	-0.03	Both clashing → Redirect
overamplification-drift	swing	5	+1.23	Dampen → Dampen → Amplify

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🤝 Contributing

1. Fork the repo
2. Create a feature branch
3. Add your code + tests
4. Submit a PR with a clear explanation

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💬 Troubleshooting

• TS errors after file changes? → Restart the TypeScript server or clear your IDE cache
• No tone data? → Run liveNuanceTest once to seed memory before inference

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📄 License

Licensed under CC-BY-NC 4.0
Free for research, education, and non-commercial use.
Commercial licensing available by inquiry.

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📡 Contact

• Email: putmanmodel@pm.me
• X (Twitter): @putmanmodel
• LinkedIn: Stephen A. Putman
• BlueSky: @putmanmodel.bsky.social

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🧭 Coming Soon – Visual Layers + Symbolic Mapping

The included tonePairs.json and keywordMap.json form the symbolic base for future emotional modeling tools.
These files support upcoming visual and semantic systems currently in active R&D.

🌱 Planned Experimental Modules

• 🎨 ToneGlowHUD™ — Real-time emotional field overlays (Unity-ready)
• 🧠 Nuance Engine — Symbolic tone memory with recursive deviation tracking
• 📊 ToneSig Scene Designer — Visualize NPC state arcs + resonance clusters

These features are exploratory and may evolve as the Spanda Engine develops.

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📦 Version

1.0.0