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

CLI tool for brain topology analysis, simulation, and visualization.

Overview

ruv-neural-cli is the command-line binary (ruv-neural) that ties together the entire rUv Neural crate ecosystem. It provides subcommands for simulating neural sensor data, analyzing brain connectivity graphs, computing minimum cuts, running the full processing pipeline with an optional ASCII dashboard, and exporting to multiple visualization formats.

Installation

# Build from source
cargo install --path .

# Or run directly
cargo run -p ruv-neural-cli -- <command>

Commands

simulate -- Generate synthetic neural data

ruv-neural simulate --channels 64 --duration 10 --sample-rate 1000 --output data.json
Flag Default Description
-c, --channels 64 Number of sensor channels
-d, --duration 10.0 Duration in seconds
-s, --sample-rate 1000.0 Sample rate in Hz
-o, --output (none) Output file path (JSON)

analyze -- Analyze a brain connectivity graph

ruv-neural analyze --input graph.json --ascii --csv metrics.csv
Flag Default Description
-i, --input (required) Input graph file (JSON)
--ascii false Show ASCII visualization
--csv (none) Export metrics to CSV file

mincut -- Compute minimum cut

ruv-neural mincut --input graph.json --k 4
Flag Default Description
-i, --input (required) Input graph file (JSON)
-k (none) Multi-way cut with k partitions

pipeline -- Full end-to-end pipeline

ruv-neural pipeline --channels 32 --duration 5 --dashboard

Runs: simulate -> preprocess -> build graph -> mincut -> embed -> decode.

Flag Default Description
-c, --channels 32 Number of sensor channels
-d, --duration 5.0 Duration in seconds
--dashboard false Show real-time ASCII dashboard

export -- Export to visualization format

ruv-neural export --input graph.json --format dot --output graph.dot
Flag Default Description
-i, --input (required) Input graph file (JSON)
-f, --format d3 Output format: d3, dot, gexf, csv, rvf
-o, --output (required) Output file path

info -- Show system information

ruv-neural info

Displays crate versions, available features, and system capabilities.

Global Options

Flag Description
-v Increase verbosity (up to -vvv)
--version Print version
--help Print help

Integration

Depends on all workspace crates: ruv-neural-core, ruv-neural-sensor, ruv-neural-signal, ruv-neural-graph, ruv-neural-mincut, ruv-neural-embed, ruv-neural-memory, ruv-neural-decoder, and ruv-neural-viz. Uses clap for argument parsing and tokio for async runtime.

License

MIT OR Apache-2.0