population op• データ種: table → table
• 呼び出し: import fullseye as fs; fs.ledger.graph_edge_consensus(adjs, ordered=False, n_null=0, swaps_per_edge=5, seed=0) (実装を直接呼ぶなら import graphinv; graphinv.graph_edge_consensus(adjs, ordered=False, n_null=0, swaps_per_edge=5, seed=0)、台帳から引くなら opsgraph.get("graph_edge_consensus"))
How many individuals share each connection — K wiring matrices on one node order.
`adjs is either a list of K square matrices or a table {name: matrix}` (its
insertion order is the individual order), all on the same node order (align
the cell names first; a node absent from an individual is a zero row and column).
Weights (synapse counts) are kept for the synapse shares; presence is `> 0`.
Self-loops are dropped.
Returns a dict with:
• `k (K), n, names, edges_per_individual and union` (edges
seen in at least one individual)
• `occupancy_hist — h[c] = number of edges present in exactly c`
individuals, `c = 0..K (h[0]` is always 0: only the union is counted)
• `jaccard — the K x K pairwise overlap |Ei ∩ Ej| / |Ei ∪ Ej|`
• `synapse_share — for each c`, the fraction of all synapses (summed over
individuals) on edges of occupancy `c` (with a 0/1 input: the edge share)
• with `ordered=True` (individuals listed in developmental order): the union is
split by its presence pattern along the order into `stable` (present in all),
`added (absent, then present to the end: 0..01..1), lost`
(`1..10..0) and flicker (anything else). The four sum to union`.
• with `n_null >= 2`: every individual is rewired independently by
degree-preserving swaps (each node's in/out degree kept exactly, checked per
sample, fail-closed) and the occupancy histogram is re-counted. Reports
`occupancy_null_mean / occupancy_null_sd per c` and
`shared_all_ratio` (edges present in all K, observed over the null mean; inf
when the null never shares one, so `shared_all_null_max` is reported too):
the chance level of "the same connection in every animal" given only each
animal's degrees.
Identities (the tests are built on these, not on fitted numbers):
`sum_c c * h[c] == sum_i |E_i|; sum_c h[c] == union`; K identical inputs give
`h[K] == union` and every Jaccard == 1; the Jaccard matrix is symmetric with a unit
diagonal; `stable + added + lost + flicker == union; synapse_share` sums to 1.
Raises `ValueError: adjs` not a list / table; fewer than 2 individuals;
matrices of different sizes; any refusal of :func:graph_degree_summary
(non-square, negative, non-finite, string / bool / complex / masked input); an
individual with no edges; `n_null` equal to 1 (no spread) or negative;
`swaps_per_edge < 1`.
• サンプルデータ カタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。
• poc_connectome_across_decades — py -3.11 examples/poc_connectome_across_decades.py
• poc_connectome_across_worms — py -3.11 examples/poc_connectome_across_worms.py
table を入力に取れる)graph_core_persistence · tree_morphometry · tree_sholl · tree_run_length
population)graph_strength_growth · graph_core_persistence
*Provenance: graphinv.py — GRAPH operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.