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_central_composite(factors=3, alpha='rotatable', center=4, generators=None, inscribed=False) -> 'dict' (to call the implementation directly, import doe; doe.doe_central_composite(factors=3, alpha='rotatable', center=4, generators=None, inscribed=False) -> 'dict'; from the registry, opsmultivar.get("doe_central_composite"))
Central composite design (Box & Wilson 1951) -> design.
Three parts: the two-level factorial (full 2^k, or a fraction from generators as in doe_fractional_factorial — it must be resolution V or better so that every two-factor interaction of the quadratic model stays estimable), 2k axial points at ±alpha on each axis, and center centre points.
alpha: "rotatable" — alpha = F^(1/4) with F the number of factorial points (the prediction variance then depends only on the distance from the centre; Box & Hunter 1957), "face" — alpha = 1 (face-centred, three levels only), "spherical" — alpha = sqrt(k), or a number > 0.
inscribed=True shrinks the whole design by 1/alpha so that the axial points sit at the ends of the range (lo/hi) and the factorial points inside it; otherwise the factorial points sit at lo/hi and the axial points go outside the range — meta['outside_range'] then names the factors whose natural values leave lo..hi (check that the bench can be set there).
• 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
design as input)doe_decode · rsm_fit · doe_main_effects · sens_morris · sens_sobol
plan)doe_lhs · doe_orthogonal_array · doe_fractional_factorial · 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.