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rUv f49c722764
chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427) 
The Rust port lived two directories deep (rust-port/wifi-densepose-rs/)
without any sibling under rust-port/ that warranted the extra level.
Move the whole workspace up to v2/ to match v1/ (Python) at the same
depth and shorten every cd / build command across the repo.

git mv preserves history for all tracked files. 60 files updated for
path references (CI workflows, ADRs, docs, scripts, READMEs, internal
.claude-flow state). Two manual fixes for relative-cd paths in
CLAUDE.md and ADR-043 that became wrong after the depth change
(cd ../.. → cd ..).

Validated:
- cargo check --workspace --no-default-features → clean (after target/
  nuke; the gitignored target/ was carried by the OS rename and had
  hard-coded old paths in build scripts)
- cargo test --workspace --no-default-features → 1,539 passed, 0 failed,
  8 ignored (same totals as pre-rename)
- ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm)

After-merge follow-up: contributors should `rm -rf v2/target` once and
let cargo regenerate from the new path.
 		2026-04-25 21:28:13 -04:00
..
src chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427) 2026-04-25 21:28:13 -04:00
Cargo.toml chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427) 2026-04-25 21:28:13 -04:00
README.md chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427) 2026-04-25 21:28:13 -04:00

README.md

ruv-neural-graph

Brain connectivity graph construction from neural signals with graph-theoretic analysis and spectral properties.

Overview

ruv-neural-graph builds brain connectivity graphs from multi-channel neural time series data and connectivity matrices. It provides graph-theoretic metrics (efficiency, clustering, centrality), spectral graph properties (Laplacian, Fiedler value), brain atlas definitions, petgraph interoperability, and temporal dynamics tracking for brain topology research.

Features

  • Graph construction (constructor): Build BrainGraph instances from connectivity matrices and multi-channel time series data via BrainGraphConstructor
  • Brain atlases (atlas): Built-in Desikan-Killiany 68-region atlas with support for loading custom atlas definitions
  • Graph metrics (metrics): Global efficiency, local efficiency, clustering coefficient, betweenness centrality, degree distribution, modularity, graph density, small-world index
  • Spectral analysis (spectral): Graph Laplacian, normalized Laplacian, Fiedler value (algebraic connectivity), spectral gap
  • Petgraph bridge (petgraph_bridge): Bidirectional conversion between BrainGraph and petgraph Graph types
  • Temporal dynamics (dynamics): TopologyTracker for monitoring graph property evolution over time

Usage

use ruv_neural_graph::{
    BrainGraphConstructor, load_atlas, AtlasType,
    global_efficiency, clustering_coefficient, modularity,
    fiedler_value, graph_laplacian,
    to_petgraph, from_petgraph,
    TopologyTracker,
};

// Construct a brain graph from a connectivity matrix
let constructor = BrainGraphConstructor::new();
let graph = constructor.from_matrix(&connectivity_matrix, 0.3, atlas)?;

// Compute graph-theoretic metrics
let efficiency = global_efficiency(&graph);
let clustering = clustering_coefficient(&graph);
let mod_score = modularity(&graph);

// Spectral properties
let laplacian = graph_laplacian(&graph);
let fiedler = fiedler_value(&graph);

// Convert to petgraph for additional algorithms
let pg = to_petgraph(&graph);
let brain_graph = from_petgraph(&pg);

// Track topology over time
let mut tracker = TopologyTracker::new();
tracker.update(&graph);

API Reference

Module Key Types / Functions
constructor BrainGraphConstructor
atlas load_atlas, AtlasType
metrics global_efficiency, local_efficiency, clustering_coefficient, betweenness_centrality, modularity, small_world_index
spectral graph_laplacian, normalized_laplacian, fiedler_value, spectral_gap
petgraph_bridge to_petgraph, from_petgraph
dynamics TopologyTracker

Integration

Depends on ruv-neural-core for BrainGraph and atlas types, and on ruv-neural-signal for connectivity computation. Feeds graphs into ruv-neural-mincut for topology partitioning and into ruv-neural-viz for visualization. Uses petgraph for underlying graph data structures.

License

MIT OR Apache-2.0