check op• Data kinds: calresult × simmodel → calresult
• Call: import fullseye as fs; fs.ledger.simcal_validate(result=None, holdout=None, locked=None, tau=None, dims=None, n_boot=200, alpha=0.001, seed=0) -> 'dict' (to call the implementation directly, import simcalcheck; simcalcheck.simcal_validate(result=None, holdout=None, locked=None, tau=None, dims=None, n_boot=200, alpha=0.001, seed=0) -> 'dict'; from the registry, opssimcal.get("simcal_validate"))
Over-fitting gates on held-out images; any one failing rejects the calibration (the estimates are withdrawn).
holdout (and locked): observed simmodels of the same knobs (simcal_observe with split='holdout' / 'locked') or image lists for the fitted model's conditions. Errors are reduced chi^2 (1 = the residuals are exactly the noise). Gates, each recorded in validation.gates with its value and limit:
1. beats_initial — holdout error at the estimate < holdout error at the initial knobs;
2. holdout_over_train — holdout / train <= 1 + tau, tau = the bootstrap 95 % relative spread of the train error over its pixels (n_boot resamples) unless given;
3. beats_gain_offset — better than the initial simulation re-scaled by a fitted gain and offset only (the cheapest model that could explain the images);
4. estimate_plausible — every estimate inside its range, not within 1 % of an end, and within 3 sd of its prior when one is given;
5. knobs_within_dims — free knobs <= output directions above noise (dims, or computed from a Latin hypercube over the knobs with sensitivity.mv_residual_pca);
6. fit_consistent_with_noise — the absolute gate: on the train images (nu = pixels − free knobs) and on the holdout images (nu = pixels), chi^2 of the noise-normalised residuals has an upper-tail probability P(chi^2_nu >= chi^2) of at least alpha (default 1e-3). Gates 1-5 are relative comparisons and pass a fit that is 60 times the noise everywhere (a thin film in the wrong fringe order, measured); this one does not. Judged on p, not on chi^2/nu, because chi^2/nu concentrates on 1 for large nu (both are returned). The variance is the noise model's plus, for a stochastic model, its sample variance. A failure means: the fit is bad for this noise model — a local minimum, a model discrepancy, or a noise estimate that is too small. ★It does NOT detect a noise model that is too large: over-estimating the variance by 4x drives p to 1 and the gate passes (measured) — that direction is visible only as chi^2/nu << 1.
locked is scored once (errors.locked) and is not used by any gate; a result that already carries a LOCKED score is refused (the robust.py discipline: the untouched number is untouched only once).
• 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_pocket_interreflection — py -3.11 examples/poc_pocket_interreflection.py
• poc_simcal_thin_film — py -3.11 examples/poc_simcal_thin_film.py
• simcal_checks_tour — py -3.11 examples/simcal_checks_tour.py
calresult as input)simcal_identifiability · simcal_uncertainty · simcal_discrepancy · simcal_report
check)simcal_identifiability · simcal_uncertainty · 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.