variation op• Data kinds: table × video → table
• Call: import fullseye as fs; fs.ledger.varmodel_calibrate(model, good_images, far=None) (implementation को सीधे call करना हो तो import varmodel; varmodel.varmodel_calibrate(model, good_images, far=None), registry से लेना हो तो opsspc.get("varmodel_calibrate"))
fit में न इस्तेमाल हुए अच्छे images से band की आधी चौड़ाई k फिर से निकालना, उन असली images के लिए जो normal मान्यता तोड़ते हैं (table)।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
model is a varmodel_fit table; good_images (m, H, W) are good samples that were NOT used to fit it. Each is scored (varmodel_score, align="translate") and the pooled |z| of all valid pixels gives the empirical (1 - far) quantile, which replaces k (far defaults to the model's declared rate). Defects are never looked at: the threshold still comes from the good distribution, only measured instead of assumed. Real images break the per-pixel independent-normal model (residual misregistration, compression, lighting drift); the closed-form k then under-states the false alarms and this is the honest fix.
Returns a copy of the model with k / lower / upper replaced and k_declared (the closed-form k), k_source = held-out quantile, calibration_n_images, calibration_n_pixels and calibration_far_declared_k (the false-alarm rate the closed-form k actually had on these good images).
Raises ValueError on an empty / malformed / non-finite set, a shape mismatch, a model without the fitted keys, far outside its range, or fewer than 10/far calibration pixels (the quantile would rest on fewer than 10 exceedances).
• 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_good_sample_variation_model — py -3.11 examples/poc_good_sample_variation_model.py
table को input के रूप में लेते हैं)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
variation)varmodel_alignment · varmodel_band_k · varmodel_fit · varmodel_score
*Provenance: varmodel.py — SPC 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.