pyDOE 0.3.8

Design of experiments for Python

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

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)

• Response-Surface Designs

  1. Box-Behnken (bbdesign)

  2. Central-Composite (ccdesign)

• Randomized Designs

  1. Latin-Hypercube (lhs)

See the package homepage for details on usage and other notes

What’s New

In this release, an incorrect indexing variable in the internal LHS function _pdist has been corrected so point-distances are now calculated accurately.

Requirements

• NumPy

• SciPy

Installation and download

See the package homepage for helpful hints relating to downloading and installing pyDOE.

Source Code

The latest, bleeding-edge but working code and documentation source are available on GitHub.

Contact

Any feedback, questions, bug reports, or success stores should be sent to the author. I’d love to hear from you!

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

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

Much thanks goes to these individuals.

And thanks goes out to the following for finding and offering solutions for bugs:

• Ashmeet Singh

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