covadoe_create_design
Create an experimental design (factorial, fractional, CCD, BBD, D/I-optimal, Taguchi, LHS, simplex mixture, etc.).
Optional: `constraints` — list of {terms:{factor:coef}, op:"<="|">="|"==", rhs} in REAL units —
filters the D/I-optimal candidate set. `mixture=True` with a simplex `design_type`
("simplex_lattice"/"simplex_centroid") builds a bounded-mixture design (L-pseudocomponent,
components sum to 1, each ≥ its `low`); `degree` sets the simplex-lattice degree; `n_levels` sets
the D/I-optimal candidate-grid density.
Model-aware optimal designs: `model_terms` ("linear"|"interaction"|"quadratic") sets the model
matrix the D/I-optimal criterion optimizes for. `n_blocks` (>1) adds a "Block" column, confounding
the highest-order interaction(s) for factorial designs. `n_runs` / `resolution` / `generators`
control the `fractional_factorial` fraction, in that precedence order: `n_runs` (a power of 2,
counting base runs before center points) pins the fraction exactly — e.g. n_runs=16 on 5 factors
gives 2^(5-1), resolution V; `resolution` (3/4/5...) picks the smallest fraction reaching it;
`generators` gives the defining generators verbatim as pyDOE2 tokens, one per factor
(e.g. ["a","b","c","ab","ac"] for 2^(5-2)).
`design_type="taguchi"` builds an orthogonal-array design (`taguchi_levels` 2 or 3). `hard_to_change`
(list of factor names) produces a split-plot layout with a "WholePlot" column. `process_factors`
(with `mixture=True`) builds a mixture-process crossed design (`process_design_type`,
`process_center_points`). `n_runs` also sets the run count for D/I-optimal designs (default
len(factors)*3+1) and the sample count for latin_hypercube / sobol_sequence.
CCD variant: `face` — "circumscribed" (default), "faced" (star points on the faces, α = 1, for
factors that cannot physically exceed their range) or "inscribed" (the CCC scaled so the star
points land on the stated range). `axial_alpha` sets the axial distance; the default is
ROTATABLE, (2**k)**0.25, and "orthogonal" gives the α where Σx² = n_runs. The choice is worth
several points of power — check it with `covadoe_design_power` before running the design.
Common `design_type` aliases resolve ("full_factorial", "ccd", "dsd", "bbd"); the response
reports the canonical name actually built.Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| face | No | ||
| degree | No | ||
| n_runs | No | ||
| api_key | No | ||
| factors | Yes | ||
| mixture | No | ||
| n_blocks | No | ||
| n_levels | No | ||
| generators | No | ||
| resolution | No | ||
| axial_alpha | No | ||
| constraints | No | ||
| design_type | Yes | ||
| model_terms | No | linear | |
| n_replicates | No | ||
| hard_to_change | No | ||
| taguchi_levels | No | ||
| n_center_points | No | ||
| process_factors | No | ||
| process_design_type | No | full_factorial_2k | |
| process_center_points | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |