From f6102650ac912a630d8e81964478b92be59c734d Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 9 Mar 2026 02:14:49 +0000 Subject: [PATCH] Add ruv-neural-cli README https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv --- .../ruv-neural/ruv-neural-cli/README.md | 181 ++++++++++++++++++ 1 file changed, 181 insertions(+) create mode 100644 rust-port/wifi-densepose-rs/crates/ruv-neural/ruv-neural-cli/README.md diff --git a/rust-port/wifi-densepose-rs/crates/ruv-neural/ruv-neural-cli/README.md b/rust-port/wifi-densepose-rs/crates/ruv-neural/ruv-neural-cli/README.md new file mode 100644 index 00000000..28069f35 --- /dev/null +++ b/rust-port/wifi-densepose-rs/crates/ruv-neural/ruv-neural-cli/README.md @@ -0,0 +1,181 @@ +# rUv Neural CLI + +CLI tool for the rUv Neural brain topology analysis system. Provides commands for +simulating neural sensor data, analyzing brain connectivity graphs, computing +minimum cuts, running full analysis pipelines, and exporting results to multiple +visualization formats. + +## Installation + +```bash +cargo install --path . +``` + +Or build from the workspace root: + +```bash +cargo build -p ruv-neural-cli --release +``` + +The binary is named `ruv-neural`. + +## Command Reference + +| Command | Description | +|------------|-------------------------------------------------------| +| `simulate` | Generate simulated multi-channel neural sensor data | +| `analyze` | Load and analyze a brain connectivity graph (JSON) | +| `mincut` | Compute minimum cut (Stoer-Wagner or multi-way) | +| `pipeline` | Full end-to-end: simulate -> filter -> graph -> decode| +| `export` | Export brain graph to D3, DOT, GEXF, CSV, or RVF | +| `info` | Show system info, crate versions, and capabilities | + +## Usage Examples + +### Simulate Neural Data + +Generate 64-channel simulated neural data at 1 kHz for 10 seconds: + +```bash +ruv-neural simulate -c 64 -d 10.0 -s 1000.0 -o output.json +``` + +Default parameters (no arguments required): + +```bash +ruv-neural simulate +``` + +### Analyze a Brain Graph + +Load a graph from JSON and display topology metrics: + +```bash +ruv-neural analyze -i brain_graph.json +``` + +With ASCII adjacency matrix visualization: + +```bash +ruv-neural analyze -i brain_graph.json --ascii +``` + +Export per-node metrics to CSV: + +```bash +ruv-neural analyze -i brain_graph.json --csv metrics.csv +``` + +### Compute Minimum Cut + +Standard two-way Stoer-Wagner minimum cut: + +```bash +ruv-neural mincut -i brain_graph.json +``` + +Multi-way cut with 4 partitions: + +```bash +ruv-neural mincut -i brain_graph.json -k 4 +``` + +### Run Full Pipeline + +The pipeline command runs all stages end-to-end: + +1. Generate simulated sensor data +2. Preprocess (bandpass filter 1-100 Hz) +3. Construct brain connectivity graph (PLV) +4. Compute minimum cut and topology metrics +5. Generate topology and spectral embeddings +6. Decode cognitive state +7. Display results summary + +```bash +ruv-neural pipeline -c 32 -d 5.0 +``` + +With ASCII dashboard visualization: + +```bash +ruv-neural pipeline -c 16 -d 3.0 --dashboard +``` + +### Export Graph + +Export to D3.js-compatible JSON: + +```bash +ruv-neural export -i brain_graph.json -f d3 -o graph.d3.json +``` + +Export to Graphviz DOT: + +```bash +ruv-neural export -i brain_graph.json -f dot -o graph.dot +``` + +All supported formats: + +```bash +ruv-neural export -i graph.json -f d3 -o out.json # D3.js JSON +ruv-neural export -i graph.json -f dot -o out.dot # Graphviz DOT +ruv-neural export -i graph.json -f gexf -o out.gexf # GEXF XML +ruv-neural export -i graph.json -f csv -o out.csv # CSV edge list +ruv-neural export -i graph.json -f rvf -o out.rvf # RuVector File +``` + +### System Info + +```bash +ruv-neural info +``` + +### Verbosity + +Use `-v` flags for increased logging detail: + +```bash +ruv-neural -v pipeline -c 8 -d 2.0 # INFO level +ruv-neural -vv pipeline -c 8 -d 2.0 # DEBUG level +ruv-neural -vvv pipeline -c 8 -d 2.0 # TRACE level +``` + +## Output Formats + +| Format | Extension | Description | +|--------|-----------|------------------------------------------------| +| D3 | `.json` | D3.js force-directed graph with nodes and links| +| DOT | `.dot` | Graphviz DOT for rendering with `dot` or `neato`| +| GEXF | `.gexf` | Graph Exchange XML Format for Gephi | +| CSV | `.csv` | Edge list with source, target, weight, metric | +| RVF | `.json` | RuVector File format with adjacency matrix | + +## Pipeline Walkthrough + +The `pipeline` command demonstrates the full rUv Neural analysis flow: + +``` +simulate -> filter -> PLV graph -> mincut -> embed -> decode +``` + +**Step 1 - Simulate**: Generates multi-channel neural data with alpha (10 Hz), +beta (20 Hz), and gamma (40 Hz) oscillations plus white noise. + +**Step 2 - Filter**: Applies a 4th-order Butterworth bandpass filter (1-100 Hz) +using zero-phase SOS filtering. + +**Step 3 - Graph**: Computes Phase Locking Value (PLV) between all channel pairs +and constructs a brain connectivity graph with edges above a PLV threshold. + +**Step 4 - Mincut**: Runs the Stoer-Wagner algorithm for the global minimum cut, +revealing the natural partition boundary in the brain network. + +**Step 5 - Embed**: Generates both topology-based and spectral (Laplacian +eigenvector) embeddings of the brain graph state. + +**Step 6 - Decode**: Classifies the cognitive state (Rest, Focused, MotorPlanning) +using a threshold decoder on topology metrics. + +**Step 7 - Results**: Displays a formatted summary table with all computed metrics.