score op• Data kinds: voxel × voxel → score
• Call: import fullseye as fs; fs.ledger.correlation_score(voxel_a, voxel_b) (to call the implementation directly, import reprconv; reprconv.correlation_score(voxel_a, voxel_b); from the registry, opsreprconv.get("correlation_score"))
Two voxel → a normalised cross-correlation score volume. The only entry into this type.
score is a type that refine_peak_newton consumes, but no op in the ledger produced score (measured; it was reached only after a generator seed was planted). Here it returns the circular cross-correlation via FFT:
score[s] = sum_x (a[x] - mean_a) * (b[x + s] - mean_b) / (N * std_a * std_b)
So if b is a shifted by s0 with np.roll, the peak stands exactly at s0 (closed-form ground truth; the test uses this). Being a circular correlation, the edges wrap around -- it is stated explicitly that this is not a truncated correlation.
Args:
voxel_a: (D, H, W).
voxel_b: (D, H, W), same shape as voxel_a.
Returns:
(D, H, W) float64, range roughly [-1, 1] (1.0 for a perfect match).
Raises:
ValueError: not 3-D / shapes differ / constant volume (standard deviation 0) / non-finite.
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_leak_localization — py -3.11 examples/poc_leak_localization.py
• representation_conversion — py -3.11 examples/representation_conversion.py
• representation_roundtrip — py -3.11 examples/representation_roundtrip.py
score as input)score_to_position · score_to_image2d
score)score_to_position · score_to_image2d
*Provenance: reprconv.py — REPRCONV operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.