fit op• Data kinds: simmodel → table
• Call: import fullseye as fs; fs.ledger.simcal_screen(model=None, method='morris', size=8, threshold=10.0, seed=0) -> 'dict' (implementation को सीधे call करना हो तो import simcal; simcal.simcal_screen(model=None, method='morris', size=8, threshold=10.0, seed=0) -> 'dict', registry से लेना हो तो opssimcal.get("simcal_screen"))
कौन-से knobs images को camera noise से अधिक बदलते हैं, कौन-से confounded हैं, और output की कितनी दिशाएँ हैं।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
Runs the model on a screening plan over the free knobs' ranges — Morris trajectories (sensitivity.doe_morris, size of them, (k+1) runs each) or a Saltelli plan (sensitivity.doe_saltelli with n = the power of two >= 8 x size rows, (k+2) n runs) — and whitens every output by the noise standard deviation at the initial knobs. The whitened outputs are reduced to principal components and the indices are computed per component with sensitivity.sens_morris / sens_sobol (reused):
Morris mu_star_whitened = sqrt(sum over components of mu*^2) — roughly how many noise standard deviations a full-range move of the knob shifts the images; Sobol' gsi_st = the variance-weighted total index over components (Lamboni et al. 2011). output_dims = number of principal directions of the centred whitened runs above the noise edge (sensitivity.mv_residual_pca with sigma = 1): more free knobs than output dims means the data cannot fix them all. Local confounding at the initial knobs:
sensitivity.mv_confounding on the whitened Jacobian (gamma_K > threshold).
class per knob: no_influence (Morris: mu*_whitened < 1.96, i.e. a full-range move is within noise; Sobol': gsi_st < 0.001), combination (in a confounded group: only a combination is determined), else determined. Needs a noise model (refused without).
• 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_simcal_thin_film — py -3.11 examples/poc_simcal_thin_film.py
• simcal_tour — py -3.11 examples/simcal_tour.py
table को input के रूप में लेते हैं)—
fit)*Provenance: simcal.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.