wiring op• 데이터 종류: labels2d × table → table
• 호출: import fullseye as fs; fs.ledger.seg_wiring_exposure(labels, synapses, spacing=None, background=0, p=0.5)(구현을 직접 호출하려면 import segcompare; segcompare.seg_wiring_exposure(labels, synapses, spacing=None, background=0, p=0.5), 원장에서 가져오려면 opsemproof.get("seg_wiring_exposure"))
> 이 연산자의 설명은 아직 번역이 없습니다. 원문을 그대로 싣습니다.
How much wiring each object stakes on staying one piece — from synapse end counts alone.
Cutting an object whose s synapse ends (synapses as in seg_synapse_partners) fall s1 / s - s1 on the two sides costs exactly (s / 2n) * H2(s1 / s) bits of ends-level split (seg_wiring_variation), and a hidden merge of two neurons inside one object costs the same expression as merge. So what an object can cost is fixed by its end count before any geometry is known:
• bound = s / 2n — the supremum over all cuts (reached when the ends halve). The bounds sum to n_ends / 2n: exactly 1 bit when no end lies on background.
• worst = (s / 2n) * H2(floor(s / 2) / s) — the largest cost a cut can actually reach.
• expected = (s / 2n) * E[H2(K / s)], K ~ Binomial(s, p) — the cost of a cut that puts each end on the cut-off side independently with probability p (default 1/2, the cut through the object's median voxel with ends spread evenly over it). This is the count-only prediction; the gap to the observed cost is the spatial clustering of the ends.
• density = (s / 2n) / (voxels / N) — the object's share of ends over its share of the N voxels of the volume. For an edit, wiring bits / pixel bits = density × (H2 of the end fractions / H2 of the voxel fractions): merges have the second factor near 1 (ends follow voxels), so their ratio is the density; a cut's second factor is H2(s1 / s) / 1 and is what the binomial predicts.
Objects with no end are not listed. Returns labels, voxels, ends, density, bound, worst, expected (aligned arrays), n synapses, n_ends kept, n_background ends dropped, total_bound, total_expected.
Raises ValueError: as seg_synapse_partners; p not strictly inside (0, 1); no end outside the background.
• 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_em_wiring_errors — py -3.11 examples/poc_em_wiring_errors.py
table 를 입력으로 받는 것)seg_synapse_partners · seg_wiring_variation · seg_synapse_nri
wiring)seg_synapse_partners · seg_wiring_variation · seg_synapse_nri
*Provenance: segcompare.py — EMPROOF 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.