check op• データ種: calresult → calresult
• 呼び出し: import fullseye as fs; fs.ledger.simcal_discrepancy(result=None, level=0.99) -> 'dict' (実装を直接呼ぶなら import simcalcheck; simcalcheck.simcal_discrepancy(result=None, level=0.99) -> 'dict'、台帳から引くなら opssimcal.get("simcal_discrepancy"))
Is there structure in the noise-normalised residuals that the model cannot produce (model discrepancy)?
The residual of every valid pixel is divided by the noise standard deviation (camera noise plus the model's own sample variance), so white residuals have unit variance. With three or more conditions the matrix is conditions x pixels valid in all of them; with fewer it is the rows x columns of the (first) residual image (a smooth spatial pattern is low-rank there). sensitivity.mv_residual_pca counts the components whose singular value exceeds the Tracy-Widom level point of white noise of that shape (Johnstone 2001; level in 0.90 / 0.95 / 0.99 — the bare Marchenko-Pastur edge sqrt(n) + sqrt(p) is exceeded by pure noise 10-14 % of the time, measured). Structure found: verdict = "structure", the loadings as maps (where the model is wrong), and a warning that every uncertainty of the result is optimistic — a calibrated knob may be absorbing the missing physics.
• sim_to_real_calibration ファミリ ガイド
• サンプルデータ カタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。
• 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 を入力に取れる)simcal_identifiability · simcal_uncertainty · simcal_validate · simcal_report
check)simcal_identifiability · simcal_uncertainty · simcal_validate
*Provenance: simcalcheck.py — SIMCAL operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.