check op• Data kinds: calresult → calresult
• Call: import fullseye as fs; fs.ledger.simcal_discrepancy(result=None, level=0.99) -> 'dict' (implementation को सीधे call करना हो तो import simcalcheck; simcalcheck.simcal_discrepancy(result=None, level=0.99) -> 'dict', registry से लेना हो तो opssimcal.get("simcal_discrepancy"))
क्या noise-normalised residuals में ऐसी संरचना है जो model नहीं बना सकता (model discrepancy)?
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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 family guide
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• 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 को input के रूप में लेते हैं)simcal_identifiability · simcal_uncertainty · simcal_validate · simcal_report
check)simcal_identifiability · simcal_uncertainty · simcal_validate
*Provenance: simcalcheck.py — SIMCAL operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.