pydoe 1.3.0

Design of Experiments for Python

PyDOE: An Experimental Design Package for Python

PyDOE is a Python package for design of experiments (DOE), enabling scientists, engineers, and statisticians to efficiently construct experimental designs.

• Website: https://pydoe.github.io/pydoe/
• Documentation: https://pydoe.github.io/pydoe/reference/factorial/
• Source code: https://github.com/pydoe/pydoe
• Contributing: https://pydoe.github.io/pydoe/contributing/
• Bug reports: https://github.com/pydoe/pydoe/issues

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)

Installation

pip install pydoe

Credits

For more info see: https://pydoe.github.io/pydoe/credits/

License

This package is provided under the BSD License (3-clause)