PyDOE¶

An Experimental Design Package for Python

The PyDOE package is designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs.

>>> from pydoe import *

Overview¶

The package provides extensive support for design-of-experiments (DOE) methods and is capable of creating designs for any number of factors.

It provides:

• Factorial Designs
• General Full-Factorial (fullfact)
• 2-level Full-Factorial (ff2n)
• 2-level Fractional Factorial (fracfact, fracfact_aliasing, fracfact_by_res, fracfact_opt, alias_vector_indices)
• Plackett-Burman (pbdesign)
• Generalized Subset Designs (gsd)
• Fold-over Designs (fold)
• John's 3/4 Fractional Factorial (john_three_quarter_design)
• Latin Square Designs (latin_square)
• Graeco-Latin Square Designs (graeco_latin_square)
• Hyper-Graeco-Latin Square Designs (hyper_graeco_latin_square)

• Blocking of Full Factorial Designs (block_full_factorial)

• Mixture Designs

• Simplex-Lattice Design (simplex_lattice_design)
• Simplex-Centroid Design (simplex_centroid_design)
• Axial (Screening) Design (mixture_axial_design)
• Extreme-Vertices Design (extreme_vertices_design)

• Mixture-Process Variable Design (mixture_process_design)

• Response-Surface Designs

• Box-Behnken (bbdesign)
• Central-Composite (ccdesign)
• Doehlert Design (doehlert_shell_design, doehlert_simplex_design)
• Star Designs (star)
• Union Designs (union)
• Repeated Center Points (repeat_center)
• Blocked Central Composite Design (block_ccdesign)

• Small Composite Design (small_composite_design)

• Space-Filling Designs

• Latin-Hypercube (lhs)
• Orthogonal Array-based Latin Hypercube (oa_lhd)
• Sliced Latin Hypercube (sliced_lhs)
• Nested Latin Hypercube (nested_lhs)
• Maximin Distance Design (maximin_design)
• Minimax Distance Design (minimax_design)
• Maximum Projection Design (maxpro_design)
• Nearly Orthogonal Latin Hypercube (nearly_orthogonal_lhs)

• Random Uniform (random_uniform)

• Low-Discrepancy Sequences

• Sukharev Grid (sukharev_grid)
• Sobol’ Sequence (sobol_sequence)
• Halton Sequence (halton_sequence)
• Hammersley Point Set (hammersley_sequence)
• Rank-1 Lattice Design (rank1_lattice)
• Korobov Sequence (korobov_sequence)
• Faure Sequence (faure_sequence)
• Niederreiter Sequence (niederreiter_sequence)

• Cranley-Patterson Randomization (cranley_patterson_shift)

• Clustering Designs

• Random K-Means (random_k_means)

• Sensitivity Analysis Designs

• Morris Method (morris_sampling)
• Saltelli Sampling (saltelli_sampling)

• Iman-Conover Method (iman_conover)

• Taguchi Designs

• Orthogonal arrays and robust design utilities (taguchi_design, compute_snr, get_orthogonal_array, list_orthogonal_arrays, TaguchiObjective)

• Optimal Designs

• Advanced optimal design algorithms (optimal_design)
• Optimality criteria (a_optimality, c_optimality, d_optimality, e_optimality, g_optimality, i_optimality, s_optimality, t_optimality, v_optimality)
• Efficiency measures (a_efficiency, d_efficiency)
• Search algorithms (sequential_dykstra, simple_exchange_wynn_mitchell, fedorov, modified_fedorov, detmax)

• Design utilities (criterion_value, information_matrix, build_design_matrix, build_uniform_moment_matrix, generate_candidate_set)

• Sparse Grid Designs

• Sparse Grid Design (doe_sparse_grid)

• Sparse Grid Dimension (sparse_grid_dimension)

• Specialized Designs

• Definitive Screening Design (definitive_screening_design)

• Supersaturated Design (supersaturated_design)

• Sequential / Adaptive Designs

• Sequential Design Driver (sequential_design)
• Gaussian Process Surrogate (GaussianProcessRegressor)
• Acquisition Functions (expected_improvement, probability_of_improvement, upper_confidence_bound)