population op• Data kinds: table → table
• Call: import fullseye as fs; fs.ledger.graph_core_persistence(adjs, mode='in', weighted=False) (implementation को सीधे call करना हो तो import graphinv; graphinv.graph_core_persistence(adjs, mode='in', weighted=False), registry से लेना हो तो opsgraph.get("graph_core_persistence"))
कौन से nodes हर individual के सबसे भीतरी core में हैं — एक node order पर K wirings।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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.
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• poc_worm_core_persists — py -3.11 examples/poc_worm_core_persists.py
table को input के रूप में लेते हैं)graph_edge_consensus · tree_morphometry · tree_sholl · tree_run_length
population)graph_edge_consensus · graph_strength_growth
*Provenance: graphinv.py — GRAPH operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.