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

Brain topology visualization, ASCII rendering, and export formats.

Overview

ruv-neural-viz provides layout algorithms, color mapping, terminal-friendly ASCII rendering, animation frame generation, and export to standard graph visualization formats for brain connectivity graphs. It turns BrainGraph and mincut analysis results into visual output suitable for terminal dashboards, web applications, and graph analysis tools.

Features

  • Layout algorithms (layout): ForceDirectedLayout for spring-based node positioning and AnatomicalLayout for MNI-coordinate-based brain region placement; circular layout variants
  • Color mapping (colormap): ColorMap with cool-warm, viridis, and module-color schemes for mapping scalar values (edge weights, node degrees) to colors
  • ASCII rendering (ascii): Terminal-friendly renderers for brain graphs, mincut partitions, sparkline time series, connectivity matrices, and real-time dashboard views
  • Export formats (export): D3.js JSON (force-directed graph format), Graphviz DOT, GEXF (Gephi), and CSV timeline export
  • Animation (animation): AnimationFrames generator from temporal BrainGraphSequence data with AnimatedNode, AnimatedEdge, and AnimationFrame types; configurable LayoutType per frame

Usage

use ruv_neural_viz::{
    ForceDirectedLayout, AnatomicalLayout, ColorMap,
    AnimationFrames, LayoutType,
};
use ruv_neural_viz::ascii;
use ruv_neural_viz::export;

// Force-directed layout for a brain graph
let layout = ForceDirectedLayout::new();
let positions = layout.compute(&graph);

// Anatomical layout using MNI coordinates
let anat_layout = AnatomicalLayout::new();
let positions = anat_layout.compute(&graph, &parcellation);

// Color mapping
let cmap = ColorMap::cool_warm();
let color = cmap.map(0.75); // returns (r, g, b)

// ASCII rendering to terminal
ascii::render_graph(&graph);
ascii::render_mincut(&mincut_result);

// Export to D3.js JSON
let d3_json = export::to_d3_json(&graph, &positions);

// Export to Graphviz DOT
let dot = export::to_dot(&graph);

// Generate animation frames from temporal sequence
let frames = AnimationFrames::from_sequence(
    &graph_sequence,
    LayoutType::ForceDirected,
);

API Reference

Module Key Types / Functions
layout ForceDirectedLayout, AnatomicalLayout
colormap ColorMap
ascii Graph, mincut, sparkline, matrix, and dashboard renderers
export to_d3_json, to_dot, to_gexf, to_csv_timeline
animation AnimationFrames, AnimationFrame, AnimatedNode, AnimatedEdge, LayoutType

Feature Flags

Feature Default Description
std Yes Standard library support
ascii No ASCII art rendering for terminal

Integration

Depends on ruv-neural-core for BrainGraph types, ruv-neural-graph for graph metrics used in layout computation, and ruv-neural-mincut for partition visualization. Used by ruv-neural-cli for terminal dashboard output and export commands.

License

MIT OR Apache-2.0