population op• 데이터 종류: table → table
• 호출: import fullseye as fs; fs.ledger.graph_core_persistence(adjs, mode='in', weighted=False)(구현을 직접 호출하려면 import graphinv; graphinv.graph_core_persistence(adjs, mode='in', weighted=False), 원장에서 가져오려면 opsgraph.get("graph_core_persistence"))
> 이 연산자의 설명은 아직 번역이 없습니다. 원문을 그대로 싣습니다.
Which nodes sit in the innermost core of every individual — K wirings on one node order.
adjs is a list of K square matrices or a table {name: matrix} on the same node order (a node absent from an individual is a zero row and column). For each individual the innermost core of graph_kcore (same mode / weighted) is taken; appearances[v] counts the individuals whose innermost core contains v. An individual whose innermost index is 0 (e.g. no edges) has no core and contributes no member. Following Yadav & Singh 2026, a node is persistent if it is in the core of all K, recurrent if in 2 .. K−1, transient if in exactly 1, and never otherwise.
Returns a dict: k (K), n, names, membership (K, n) bool, appearances (n,), persistent / recurrent / transient / never (n,) bool masks, n_persistent .. n_never, kmax (K,) the innermost index per individual, n_inner (K,), mode, weighted.
Identities: sum(n_inner) == sum(appearances); the four classes partition the nodes; K identical inputs make every core node persistent and nothing recurrent or transient; every persistent node is in every individual's innermost core.
Raises ValueError: adjs not a list / table; fewer than 2 individuals; matrices of different sizes; any refusal of graph_kcore.
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• poc_worm_core_persists — py -3.11 examples/poc_worm_core_persists.py
table 를 입력으로 받는 것)graph_edge_consensus · tree_morphometry · tree_sholl · tree_run_length
population)graph_edge_consensus · graph_strength_growth
*Provenance: graphinv.py — GRAPH 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.