Design of Experiments for Python
Project description
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) - Plackett-Burman (
pbdesign) - Generalized Subset Designs (
gsd)
- General Full-Factorial (
-
Response-Surface Designs
- Box-Behnken (
bbdesign) - Central-Composite (
ccdesign) - Doehlert Design (
doehlert_shell_design,doehlert_simplex_design)
- Box-Behnken (
-
Randomized Designs
- Latin-Hypercube (
lhs) - Random K-Means (
random_k_means) - Random Uniform (
random_uniform)
- Latin-Hypercube (
-
Low-Discrepancy Sequences
- Sukharev Grid (
sukharev_grid) - Sobol’ Sequence (
sobol_sequence) - Halton Sequence (
halton_sequence) - Rank-1 Lattice Design (
rank1_lattice) - Korobov Sequence (
korobov_sequence) - Cranley-Patterson Randomization (
cranley_patterson_shift)
- Sukharev Grid (
-
Sampling Designs
- Morris Method (
morris_sampling) - Saltelli Sampling (
saltelli_sampling)
- Morris Method (
-
Taguchi Designs
- Orthogonal arrays and robust design utilities (
taguchi_design,compute_snr)
- Orthogonal arrays and robust design utilities (
-
Optimal Designs
- Advanced optimal design algorithms (
optimal_design) - Optimality criteria (
A,C,D,E,G,I,S,T,V) - Search algorithms (
Sequential (Dykstra),Simple Exchange (Wynn-Mitchell),Fedorov,Modified Fedorov,DETMAX)
- Advanced optimal design algorithms (
-
Sparse Grid Designs
- Sparse Grid Design (
doe_sparse_grid) - Sparse Grid Dimension (
sparse_grid_dimension)
- Sparse Grid Design (
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)
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