check op• 資料種類:calresult → calresult
• 呼叫: import fullseye as fs; fs.ledger.simcal_identifiability(result=None, at=None, profile=True, level=0.95, threshold=None) -> 'dict'(要直接呼叫實作,import simcalcheck; simcalcheck.simcal_identifiability(result=None, at=None, profile=True, level=0.95, threshold=None) -> 'dict';從台帳取用則 opssimcal.get("simcal_identifiability"))
每個已擬合旋鈕的 Fisher 資訊、Cramér-Rao 下界、相關性、共線性與剖面概似。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
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 族使用指南
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者、年份)與用途見上面的族使用指南。
• 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 作為輸入)simcal_uncertainty · simcal_validate · simcal_discrepancy · simcal_report
check)simcal_uncertainty · simcal_validate · simcal_discrepancy
*Provenance: simcalcheck.py — SIMCAL 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.