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' (to call the implementation directly, import simcal; simcal.simcal_screen(model=None, method='morris', size=8, threshold=10.0, seed=0) -> 'dict'; from the registry, opssimcal.get("simcal_screen"))
Which knobs move the images above the camera noise, which are confounded, and how many output directions exist.
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 (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_simcal_thin_film — py -3.11 examples/poc_simcal_thin_film.py
• simcal_tour — py -3.11 examples/simcal_tour.py
table as input)—
fit)*Provenance: simcal.py — SIMCAL operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.