pyDOE3: An experimental design package for python

pyDOE3 is fork of pyDOE2 which is a fork of pyDOE.

As for pyDOE2 wrt to pyDOE, pyDOE3 came to life to solve bugs and issues that remained unsolved in pyDOE2.

The pyDOE3 package is designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs.

Hint

All available designs can be accessed after a simple import statement:

>>> from pyDOE3 import *

Capabilities

The package currently includes functions for creating designs for any number of factors:

• Factorial Designs

  1. General Full-Factorial (fullfact)

  2. 2-Level Full-Factorial (ff2n)

  3. 2-Level Fractional-Factorial (fracfact)

  4. Plackett-Burman (pbdesign)

  5. Generalized Subset Design (gsd)

• Response-Surface Designs

  1. Box-Behnken (bbdesign)

  2. Central-Composite (ccdesign)

• Randomized Designs

  1. Latin-Hypercube (lhs)

Requirements

• NumPy

• SciPy

Installation

pip install --upgrade pyDOE3

or with Anaconda distribution

conda install -c conda-forge pydoe3

Credits

This code was originally published by the following individuals for use with Scilab:

• Copyright (C) 2012 - 2013 - Michael Baudin

• Copyright (C) 2012 - Maria Christopoulou

• Copyright (C) 2010 - 2011 - INRIA - Michael Baudin

• Copyright (C) 2009 - Yann Collette

• Copyright (C) 2009 - CEA - Jean-Marc Martinez

pyDOE

• Copyright (c) 2014, Abraham D. Lee & tisimst

pyDOE2

• Copyright (c) 2018, Rickard Sjögren & Daniel Svensson

Much thanks goes to these individuals.

License

This package is provided under The BSD License (3-Clause)

References

• Factorial designs

• Plackett-Burman designs

• Box-Behnken designs

• Central composite designs

• Latin-Hypercube designs

There is also a wealth of information on the NIST website about the various design matrices that can be created as well as detailed information about designing/setting-up/running experiments in general.