check op• Data kinds: calresult → calresult
• Call: import fullseye as fs; fs.ledger.simcal_uncertainty(result=None, method='laplace', n_boot=40, level=0.95, seed=0) -> 'dict' (to call the implementation directly, import simcalcheck; simcalcheck.simcal_uncertainty(result=None, method='laplace', n_boot=40, level=0.95, seed=0) -> 'dict'; from the registry, opssimcal.get("simcal_uncertainty"))
Standard deviations and intervals of the determined knobs: Laplace (inverse Fisher) or parametric bootstrap.
Only knobs that kept a point estimate are covered; knobs without one are held at the end point (the intervals are conditional on them — stated in note). laplace: covariance = inverse of J^T W J at the estimate on the unit-scaled knobs, intervals u ± z sd mapped back through each knob's scale (a log knob gets an asymmetric interval), std by the delta method. bootstrap: n_boot refits to the fitted prediction plus Gaussian noise of the noise model's variance (Efron 1979), percentile intervals at level. A result with no point estimate is refused. Under a model discrepancy both are optimistic (simcal_discrepancy says so).
• sim_to_real_calibration family guide
• 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_simcal_thin_film — py -3.11 examples/poc_simcal_thin_film.py
calresult as input)simcal_identifiability · simcal_validate · simcal_discrepancy · simcal_report
check)simcal_identifiability · simcal_validate · simcal_discrepancy
*Provenance: simcalcheck.py — SIMCAL 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.