Design of experiments for Python
The pyDOE package is designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs.
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)
- Response-Surface Designs
- Box-Behnken (bbdesign)
- Central-Composite (ccdesign)
- Randomized Designs
- Latin-Hypercube (lhs)
See the package homepage for details on usage and other notes
In this release, an incorrect indexing variable in the internal LHS function _pdist has been corrected so point-distances are now calculated accurately.
Installation and download
See the package homepage for helpful hints relating to downloading and installing pyDOE.
Any feedback, questions, bug reports, or success stores should be sent to the author. I’d love to hear from you!
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
This package is provided under two licenses:
- The BSD License (3-clause)
- Any other that the author approves (just ask!)
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