activity op• Data kinds: matrix × points × labels → table
• Call: import fullseye as fs; fs.ledger.graph_activity_spread(X: 'Any', P: 'Any', source: 'Any', thresh: 'float' = 0.1) -> 'dict[str, np.ndarray]' (to call the implementation directly, import conngraph; conngraph.graph_activity_spread(X: 'Any', P: 'Any', source: 'Any', thresh: 'float' = 0.1) -> 'dict[str, np.ndarray]'; from the registry, opsconngraph.get("graph_activity_spread"))
Time-series table of how far the activity spread: columns step / mean_distance / active_fraction / source_fraction.
X = (T, n) state sequence, P = (n, 3) coordinates, source = indicator of the stimulated nodes (integers of length n, non-zero = stimulated; at least 1). mean_distance[t] = Σ|x_i(t)| ‖P_i − c‖ / Σ|x_i(t)| (c = centroid of the stimulated nodes; the mean distance of activity weighted by |x|, in the same unit as P; 0 for steps where activity is all zero). active_fraction[t] = fraction of nodes with |x_i(t)| ≥ global maximum × thresh. source_fraction[t] = share of activity located at the stimulated nodes, Σ_source |x| / Σ|x| (0 if all zero).
• 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.
• poc_malecns_activity_wave — py -3.11 examples/poc_malecns_activity_wave.py
• poc_microns_brain_wave — py -3.11 examples/poc_microns_brain_wave.py
table as input)—
activity)graph_activation_latency · points_activity_video
*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.