wiring op• Datenarten: labels2d × table → table
• Aufruf: import fullseye as fs; fs.ledger.seg_wiring_exposure(labels, synapses, spacing=None, background=0, p=0.5) (die Implementierung direkt: import segcompare; segcompare.seg_wiring_exposure(labels, synapses, spacing=None, background=0, p=0.5); aus dem Register: opsemproof.get("seg_wiring_exposure"))
> Für diesen Operator gibt es noch keine Übersetzung. Es folgt der Originaltext unverändert.
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.
• Leitfaden zur Familie emproof
• Katalog der Beispieldaten (Download-URLs / Lizenzen) — 2-D nutzt skimage.data (BSD/Public Domain) plus synthetische Bilder, 3-D nennt Download-URLs echter Datenquellen (Stanford, PDS, …).
• Herkunft und Literatur der Operatoren — die Quellen der Forschung/Verfahren, auf denen diese Operatorfamilie beruht.
• Der kanonische Algorithmus (Autor, Jahr) und seine Anwendungen stehen im Familienleitfaden oben.
• poc_em_wiring_errors — py -3.11 examples/poc_em_wiring_errors.py
table als Eingabe)seg_synapse_partners · seg_wiring_variation · seg_synapse_nri
wiring)seg_synapse_partners · seg_wiring_variation · seg_synapse_nri
*Provenance: segcompare.py — EMPROOF Operator-Registry. Diese Notiz wird von tools/opdocs.py md erzeugt (nicht von Hand bearbeiten).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.