check op• Data kinds: calresult → calresult
• Call: import fullseye as fs; fs.ledger.simcal_identifiability(result=None, at=None, profile=True, level=0.95, threshold=None) -> 'dict' (to call the implementation directly, import simcalcheck; simcalcheck.simcal_identifiability(result=None, at=None, profile=True, level=0.95, threshold=None) -> 'dict'; from the registry, opssimcal.get("simcal_identifiability"))
Fisher information, Cramér-Rao bound, correlations, collinearity and profile likelihood of every fitted knob.
Computed at the fit's end point (or at at = {knob: value}, e.g. the true values of a synthetic study, without touching the estimates). Returned in identifiability:
fisher (natural units), crlb (sqrt of the diagonal of its inverse — the smallest standard deviation any unbiased estimator can have under the noise model), condition (of the Fisher matrix on the unit-scaled knobs; > 1e6 adds a warning), correlation, gamma_full and confounded_groups (sensitivity.mv_confounding), and with profile=True a profile per knob: the likelihood-ratio interval where chi^2 stays within chi^2_1(level) of its minimum (3.84 at 95 %), each other knob re-fitted at every step (Raue et al. 2009). Knobs whose profile does not rise above the threshold on both sides inside the range lose their point estimate: upper_only / lower_only (only one limit exists) or undetermined. Each profile keeps its trace (knob value, chi^2 rise) of the evaluated points. No noise model: refused.
• 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
• simcal_checks_tour — py -3.11 examples/simcal_checks_tour.py
calresult as input)simcal_uncertainty · simcal_validate · simcal_discrepancy · simcal_report
check)simcal_uncertainty · 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.