sensitivity op• Data kinds: none → design (an op determined by its arguments alone — it takes no image or data input)
• Call: import fullseye as fs; fs.ledger.doe_saltelli(n=1024, factors=3, seed=0) -> 'dict' (to call the implementation directly, import sensitivity; sensitivity.doe_saltelli(n=1024, factors=3, seed=0) -> 'dict'; from the registry, opsmultivar.get("doe_saltelli"))
Sample matrices for Sobol' indices (Saltelli et al. 2010) -> design of n (k + 2) runs.
A Sobol' sequence of dimension 2k gives two independent n x k matrices A (first k columns) and B (last k). For each factor i, A_B^(i) is A with column i taken from B. Rows are stacked as [A; B; A_B^(1); ...; A_B^(k)] (meta['blocks']), which is the layout sens_sobol reads.
n must be a power of two: the balance properties of a Sobol' sequence hold for 2^m points, and the estimators' error behaves erratically otherwise — the op refuses rather than silently degrading. seed scrambles the sequence (Owen scrambling via scipy); seed=None uses the plain sequence.
• design_of_experiments 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.
• sensitivity_tour — py -3.11 examples/sensitivity_tour.py
design as input)doe_decode · rsm_fit · doe_main_effects · sens_morris · sens_sobol
sensitivity)doe_morris · sens_morris · sens_sobol
*Provenance: sensitivity.py — MULTIVAR 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.