pyDOE2 1.2.1

Design of experiments for Python

pyDOE2: An experimental design package for python

pyDOE2 is a fork of the pyDOE package that is designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs.

This fork came to life to solve bugs and issues that remained unsolved in the original package.

Capabilities

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

• Factorial Designs
  • General Full-Factorial (fullfact)
  • 2-level Full-Factorial (ff2n)
  • 2-level Fractional Factorial (fracfact)
  • Plackett-Burman (pbdesign)
  • Generalized Subset Designs (gsd)
• Response-Surface Designs
  • Box-Behnken (bbdesign)
  • Central-Composite (ccdesign)
• Randomized Designs
  • Latin-Hypercube (lhs)

See the original pyDOE homepage for details on usage and other notes.

What's new?

Generalized Subset Designs

In pyDOE2 version 1.1 the Generalized Subset Design (GSD) is introduced. GSD is a generalization of traditional fractional factorial designs to problems where factors can have more than two levels.

In many application problems factors can have categorical or quantitative factors on more than two levels. Previous reduced designs have not been able to deal with such types of problems. Full multi-level factorial designs can handle such problems but are however not economical regarding the number of experiments.

The GSD provide balanced designs in multi-level experiments with the number of experiments reduced by a user-specified reduction factor. Complementary reduced designs are also provided analogous to fold-over in traditional fractional factorial designs.

GSD is available in pyDOE2 as:

import pyDOE2

levels = [2, 3, 4]  # Three factors with 2, 3 or 4 levels respectively.
reduction = 3       # Reduce the number of experiment to approximately a third.

pyDOE2.gsd(levels, reduction)

Requirements

• NumPy
• SciPy

Installation and download

Through pip:

pip install pyDOE2

Credits

pyDOE original code was originally converted from code 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

• Website: forge.scilab.org/index.php/p/scidoe/sourcetree/master/macros

pyDOE was converted to Python by the following individual:

• Copyright (c) 2014, Abraham D. Lee

The following individuals forked and works on pyDOE2:

• Copyright (C) 2018 - Rickard Sjögren and Daniel Svensson

License

This package is provided under two licenses:

1. The BSD License (3-clause)
2. Any other that the author approves (just ask!)

References

• Factorial designs
• Plackett-Burman designs
• Box-Behnken designs
• Central composite designs
• Latin-Hypercube designs
• Surowiec, Izabella, Ludvig Vikström, Gustaf Hector, Erik Johansson, Conny Vikström, and Johan Trygg. “Generalized Subset Designs in Analytical Chemistry.” Analytical Chemistry 89, no. 12 (June 20, 2017): 6491–97. https://doi.org/10.1021/acs.analchem.7b00506.