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)
- General 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)
- Simplex-Lattice 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)
- Box-Behnken (
-
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)
- Latin-Hypercube (
-
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)
- Sukharev Grid (
-
Clustering Designs
- Random K-Means (
random_k_means)
- Random K-Means (
-
Sensitivity Analysis Designs
- Morris Method (
morris_sampling) - Saltelli Sampling (
saltelli_sampling) - Iman-Conover Method (
iman_conover)
- Morris Method (
-
Taguchi Designs
- Orthogonal arrays and robust design utilities (
taguchi_design,compute_snr,get_orthogonal_array,list_orthogonal_arrays,TaguchiObjective)
- Orthogonal arrays and robust design utilities (
-
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)
- Advanced optimal design algorithms (
-
Sparse Grid Designs
- Sparse Grid Design (
doe_sparse_grid) - Sparse Grid Dimension (
sparse_grid_dimension)
- Sparse Grid Design (
-
Specialized Designs
- Definitive Screening Design (
definitive_screening_design) - Supersaturated Design (
supersaturated_design)
- Definitive Screening Design (
-
Sequential / Adaptive Designs
- Sequential Design Driver (
sequential_design) - Gaussian Process Surrogate (
GaussianProcessRegressor) - Acquisition Functions (
expected_improvement,probability_of_improvement,upper_confidence_bound)
- Sequential Design Driver (
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)
4 maintainers
Metadata
4 maintainers
Release files for pydoe 1.6.0
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