wiring op• 資料種類:labels2d × table → table
• 呼叫: import fullseye as fs; fs.ledger.seg_wiring_exposure(labels, synapses, spacing=None, background=0, p=0.5)(要直接呼叫實作,import segcompare; segcompare.seg_wiring_exposure(labels, synapses, spacing=None, background=0, p=0.5);從台帳取用則 opsemproof.get("seg_wiring_exposure"))
> 該運算子的說明尚無譯文,以下照原文給出。
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.
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者、年份)與用途見上面的族使用指南。
• poc_em_wiring_errors — py -3.11 examples/poc_em_wiring_errors.py
table 作為輸入)seg_synapse_partners · seg_wiring_variation · seg_synapse_nri
wiring)seg_synapse_partners · seg_wiring_variation · seg_synapse_nri
*Provenance: segcompare.py — EMPROOF 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.