states_layer_dimension — CONNGRAPH dimension op

• Data kinds: matrix × labels → table

• Call: import fullseye as fs; fs.ledger.states_layer_dimension(X: 'Any', labels: 'Any') -> 'dict[str, np.ndarray]' (to call the implementation directly, import conngraph; conngraph.states_layer_dimension(X: 'Any', labels: 'Any') -> 'dict[str, np.ndarray]'; from the registry, opsconngraph.get("states_layer_dimension"))

Usage

Table of the effective dimension per layer: columns layer / n / participation_ratio / ratio (= PR / n).

X = the (N, n) returned by graph_layer_propagate, labels = layer ids of the nodes. Reads, with 1 formula, where along "brain → neck → ventral nerve cord → muscle" the dimension drops (the quantization of movement).

Detailed usage guide

• conngraph family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_connectome_motor_bottleneck — py -3.11 examples/poc_connectome_motor_bottleneck.py

Ops the type connects to (they accept table as input)

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Same category (dimension)

graph_block_shuffle · graph_layer_propagate · states_participation_ratio

*Provenance: conngraph.py — CONNGRAPH operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.