wifi-densepose/v2/crates/ruv-neural/ruv-neural-wasm

ruv-neural-wasm

WebAssembly bindings for browser-based brain topology visualization.

Overview

ruv-neural-wasm provides JavaScript-callable functions for creating, analyzing, and visualizing brain connectivity graphs directly in the browser. It wraps ruv-neural-core types with wasm-bindgen and implements lightweight WASM-compatible versions of graph algorithms (Stoer-Wagner mincut, spectral embedding via power iteration, topology metrics, and cognitive state decoding) that run without heavy native dependencies.

Note: This crate is excluded from the default workspace build. Build it separately targeting wasm32-unknown-unknown.

Features

• Graph parsing: create_brain_graph -- parse BrainGraph from JSON
• Minimum cut: compute_mincut -- Stoer-Wagner on graphs up to 500 nodes
• Topology metrics: compute_topology_metrics -- density, efficiency, modularity, Fiedler value, entropy, module count
• Spectral embedding: embed_graph -- power iteration on normalized Laplacian (no LAPACK dependency)
• State decoding: decode_state -- threshold-based cognitive state classification from topology metrics
• RVF I/O: load_rvf / export_rvf -- read and write RuVector binary files
• Streaming (streaming): WebSocket-compatible streaming data processor
• Visualization data (viz_data): Data structures for D3.js and Three.js rendering

Build

# Requires wasm-pack or cargo with wasm32 target
cargo build -p ruv-neural-wasm --target wasm32-unknown-unknown --release

# Or with wasm-pack for npm-ready output
wasm-pack build ruv-neural-wasm --target web

Usage (JavaScript)

import init, {
  create_brain_graph,
  compute_mincut,
  compute_topology_metrics,
  embed_graph,
  decode_state,
  export_rvf,
  version,
} from './ruv_neural_wasm.js';

await init();

const graphJson = JSON.stringify({
  num_nodes: 3,
  edges: [
    { source: 0, target: 1, weight: 0.8, metric: "Coherence", frequency_band: "Alpha" },
    { source: 1, target: 2, weight: 0.5, metric: "Coherence", frequency_band: "Beta" },
  ],
  timestamp: 0.0,
  window_duration_s: 1.0,
  atlas: { Custom: 3 },
});

const graph = create_brain_graph(graphJson);
const mincut = compute_mincut(graphJson);
const metrics = compute_topology_metrics(graphJson);
const embedding = embed_graph(graphJson, 2);
const rvfBytes = export_rvf(graphJson);
console.log('Version:', version());

API Reference

Function	Description
create_brain_graph(json)	Parse JSON into a BrainGraph JS object
compute_mincut(json)	Stoer-Wagner minimum cut, returns MincutResult
compute_topology_metrics(json)	Compute TopologyMetrics for a graph
embed_graph(json, dim)	Spectral embedding via power iteration
decode_state(json)	Classify CognitiveState from TopologyMetrics
load_rvf(bytes)	Parse RVF binary data into JS object
export_rvf(json)	Serialize BrainGraph to RVF bytes
version()	Return crate version string

Module	Key Types
graph_wasm	wasm_mincut, wasm_embed, wasm_topology_metrics, wasm_decode
streaming	WebSocket streaming data processor
viz_data	D3.js / Three.js visualization structures

Integration

Depends on ruv-neural-core for BrainGraph, TopologyMetrics, RvfFile, and CognitiveState types. Uses wasm-bindgen and serde-wasm-bindgen for JS interop. Designed for browser-based dashboards and real-time visualization applications.

License

MIT OR Apache-2.0