Design of experiments for Python
pyDOE: The experimental design package for python
pyDOEpackage 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:
#. General Full-Factorial (
#. 2-level Full-Factorial (
#. 2-level Fractional Factorial (
#. Plackett-Burman (
#. Box-Behnken (
#. Central-Composite (
#. Latin-Hypercube (
package homepage_ for details on usage and other notes
In this release, an incorrect indexing variable in the internal LHS function
_pdisthas been corrected so point-distances are now calculated accurately.
package homepage_ for helpful hints relating to downloading and installing pyDOE.
The latest, bleeding-edge but working
documentation source_ are available
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) 2009 - CEA - Jean-Marc Martinez
Much thanks goes to these individuals.
And thanks goes out to the following for finding and offering solutions for bugs:
This package is provided under two licenses:
Central composite designs_
.. author: mailto:[email protected] .. _Factorial designs: http://en.wikipedia.org/wiki/Factorialexperiment .. Box-Behnken designs: http://en.wikipedia.org/wiki/Box-Behnkendesign .. Central composite designs: http://en.wikipedia.org/wiki/Centralcompositedesign .. _Plackett-Burman designs: http://en.wikipedia.org/wiki/Plackett-Burmandesign .. Latin-Hypercube designs: http://en.wikipedia.org/wiki/Latinhypercube_sampling .. _package homepage: http://pythonhosted.org/pyDOE .. _lhs documentation: http://pythonhosted.org/pyDOE/randomized.html#latin-hypercube