graph_activity_spread — CONNGRAPH `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"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

活動がどこまで広がったかの時系列の表: 列 step / mean_distance / active_fraction / source_fraction。

`X = (T, n) の状態列、P = (n, 3) の座標、source` = 刺激したノードの指示子(長さ n の

整数、非零 = 刺激。1 つ以上)。

mean_distance[t] = Σ|x_i(t)| ‖P_i − c‖ / Σ|x_i(t)|(c = 刺激ノードの重心、|x| で重みづけた

活動の平均距離、単位は P と同じ。活動が全零のステップは 0)。

active_fraction[t] = |x_i(t)| ≥ 全体最大 × thresh のノードの割合。

source_fraction[t] = 活動のうち刺激ノードにある分 Σ_source |x| / Σ|x|(全零なら 0)。

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_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

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

—

Same category (`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).*

graph_activity_spread — CONNGRAPH activity op