population op• Data kinds: table → table
• Call: import fullseye as fs; fs.ledger.graph_edge_consensus(adjs, ordered=False, n_null=0, swaps_per_edge=5, seed=0) (implementation को सीधे call करना हो तो import graphinv; graphinv.graph_edge_consensus(adjs, ordered=False, n_null=0, swaps_per_edge=5, seed=0), registry से लेना हो तो opsgraph.get("graph_edge_consensus"))
हर connection को कितने individuals साझा करते हैं — एक node order पर K wiring matrices।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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 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.
• 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_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 को input के रूप में लेते हैं)graph_core_persistence · tree_morphometry · tree_sholl · tree_run_length
population)graph_strength_growth · graph_core_persistence
*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.