doe_morris — MULTIVAR 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_morris(r=10, factors=4, levels=4, seed=0, n_candidates=0) -> 'dict' (to call the implementation directly, import sensitivity; sensitivity.doe_morris(r=10, factors=4, levels=4, seed=0, n_candidates=0) -> 'dict'; from the registry, opsmultivar.get("doe_morris"))

Usage

Morris one-at-a-time screening plan: r trajectories of k+1 runs -> design.

Each trajectory starts at a random point of the levels-level grid on the unit cube and moves one factor at a time by delta = levels / (2 (levels - 1)) (Morris 1991; with an even levels every grid level is equally likely to be visited), so r (k+1) runs give r elementary effects per factor. n_candidates > r draws that many trajectories and keeps r of them that are spread out: the trajectory distance of Campolongo et al. 2007 (sum over point pairs of their Euclidean distances), chosen greedily — a pair of the farthest trajectories first, then repeatedly the trajectory that adds the most distance (an approximation of their exhaustive search, stated in meta['selection']).

The coded matrix is 2u - 1 of the unit-cube points; meta keeps the trajectory bookkeeping that sens_morris needs (changed factor and sign per step).

Detailed usage guide

• design_of_experiments family guide

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• sensitivity_tour — py -3.11 examples/sensitivity_tour.py

Ops the type connects to (they accept design as input)

doe_decode · rsm_fit · doe_main_effects · sens_morris · sens_sobol

Same category (sensitivity)

sens_morris · doe_saltelli · 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.