zacheryasc
c1feddcab4
Stand up a runnable distribution stack on top of the core node logic. - distribution: add NodeDriver bridging DistributedNode to real TCP I/O (TcpTransport/TcpAcceptor), translating NodeActions to/from wire messages; refine swim probe timing and transport wiring. - node: new swactor-node binary (crates/node) hosting a real node over TCP. - tests/docker: multi-host LAN cluster harness (compose, run-lan-cluster.sh, cluster + lan_cluster integration tests) exercising the full SWIM path. - simulation: cluster_scenarios integration + distribution property coverage. - docs: reorganize into distribution/, runtime/, diagrams/, connectome/; add DOCKER_REALIZATION + SIMULATION_TESTING realization notes. Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
76 lines
2.8 KiB
Markdown
76 lines
2.8 KiB
Markdown
# Connectome Analysis
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The connectome analysis applies spectral graph theory to the codebase's internal dependency DAG, producing quantitative coupling metrics and visual dashboards.
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## What it measures
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The tool parses `deps.dot` (a GraphViz DOT file describing struct/trait dependencies between modules) and computes:
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- **Laplacian eigenvalue spectrum** -- encodes the graph's overall connectivity structure
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- **Fiedler vector** -- the optimal spectral bisection of the dependency graph, revealing natural module clusters
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- **Module coupling matrix** -- directed edge counts between every pair of modules
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- **Connectome Complexity Index (CCI)** -- a single 0-1 score combining five sub-metrics:
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| Sub-metric | Weight | What it captures |
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|---|---|---|
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| Algebraic connectivity (lambda_2/n) | 25% | How tightly connected the graph is |
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| Spectral entropy (H/log2(k)) | 25% | How uniformly distributed coupling is across eigenvalues |
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| Edge density (\|E\|/n(n-1)) | 15% | Raw ratio of edges to possible edges |
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| Cross-module coupling ratio | 20% | Fraction of edges that cross module boundaries |
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| Spectral radius (rho/(n-1)) | 15% | Maximum hub concentration |
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### Interpreting CCI
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| CCI range | Label | Meaning |
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|---|---|---|
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| < 0.30 | LOW | Well-decomposed architecture |
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| 0.30 - 0.60 | MODERATE | Typical well-structured codebase |
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| > 0.60 | HIGH | Consider reviewing module boundaries |
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## Running
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From the project root:
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```sh
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# Default: outputs to docs/connectome/
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python tools/spectral/spectral_analysis.py deps.dot
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# Custom output directory
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python tools/spectral/spectral_analysis.py deps.dot -o path/to/output
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# Also emit JSON metrics
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python tools/spectral/spectral_analysis.py deps.dot --json
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# Text report only (skip matplotlib PNG)
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python tools/spectral/spectral_analysis.py deps.dot --no-plots
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```
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### Prerequisites
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The script requires numpy, scipy, and matplotlib (for the PNG dashboard). These are available in the project's `.venv`:
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```sh
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source .venv/bin/activate
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python tools/spectral/spectral_analysis.py deps.dot
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```
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## Output files
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All output goes to `docs/connectome/` by default:
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| File | Description |
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|---|---|
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| `connectome_report.txt` | Full text report with eigenvalues, Fiedler bisection, coupling matrix, and CCI breakdown |
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| `connectome_dashboard.html` | Interactive HTML dashboard with zoomable DAG, eigenvalue plot, Fiedler bar chart, and coupling heatmap |
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| `connectome_dashboard.png` | Static PNG snapshot of the spectral dashboard (dark theme, 16x12 @ 150 DPI) |
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| `connectome_metrics.json` | Machine-readable metrics (only with `--json` flag) |
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## Regenerating deps.dot
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The DOT file is the input to the spectral analysis. To regenerate it from source:
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```sh
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cargo run --manifest-path tools/depgraph/Cargo.toml -- --src-dir src/ --output deps
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```
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Then re-run the spectral analysis to update the connectome report.
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