plan 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_lhs(n=10, factors=3, seed=0, maximin=0, centered=False) -> 'dict' (to call the implementation directly, import doe; doe.doe_lhs(n=10, factors=3, seed=0, maximin=0, centered=False) -> 'dict'; from the registry, opsmultivar.get("doe_lhs"))
Latin hypercube design (McKay, Beckman & Conover 1979) -> design.
Each factor's range is cut into n equal strata and every stratum holds exactly one run, so each factor is sampled evenly however many factors there are (a random design of the same size leaves strata empty). Construction (McKay et al. 1979, section 2): for factor j draw an independent random permutation pi_j of 0..n-1 and put run i at u_ij = (pi_j(i) + U_ij) / n with U_ij ~ Uniform[0, 1) (centered=True uses the stratum mid-point, U = 1/2). Coded value = 2u - 1.
maximin=m (m >= 2) draws m independent hypercubes and keeps the one with the largest smallest inter-point distance (each candidate is still a Latin hypercube, so the stratum property is never traded away); 0 or 1 = one plain draw.
Parameters: n runs (2..1e6), factors (int or factor list, see module docstring), seed (int, reproducible), maximin (0..1000 candidates), centered.
Returns a design whose meta holds strata_ok (every factor has one point per stratum — the defining property, checked on the returned matrix) and min_distance (in the unit cube).
• 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.
• doe_plan_tour — py -3.11 examples/doe_plan_tour.py
• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py
• sensitivity_tour — py -3.11 examples/sensitivity_tour.py
• simcal_checks_tour — py -3.11 examples/simcal_checks_tour.py
design as input)doe_decode · rsm_fit · doe_main_effects · sens_morris · sens_sobol
plan)doe_orthogonal_array · doe_fractional_factorial · doe_central_composite · doe_box_behnken
*Provenance: doe.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.