pyDOE2: An experimental design package for python
=====================================================
`pyDOE2` is a fork of the [`pyDOE`](https://github.com/tisimst/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](http://pythonhosted.org/pyDOE) for details
on usage and other notes.
What's new?
----------
### Generalized Subset Designs
In pyDOE2 version 1.1 the [Generalized Subset Design (GSD)](https://doi.org/10.1021/acs.analchem.7b00506)
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](http://en.wikipedia.org/wiki/Factorial_experiment)
- [Plackett-Burman designs](http://en.wikipedia.org/wiki/Plackett-Burman_design)
- [Box-Behnken designs](http://en.wikipedia.org/wiki/Box-Behnken_design)
- [Central composite designs](http://en.wikipedia.org/wiki/Central_composite_design)
- [Latin-Hypercube designs](http://en.wikipedia.org/wiki/Latin_hypercube_sampling)
- 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.
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"description": "pyDOE2: An experimental design package for python\n=====================================================\n\n`pyDOE2` is a fork of the [`pyDOE`](https://github.com/tisimst/pyDOE) package \nthat is designed to help the scientist, engineer, statistician, etc., to \nconstruct appropriate experimental designs.\n\nThis fork came to life to solve bugs and issues that remained unsolved in the\noriginal package.\n\nCapabilities\n------------\n\nThe package currently includes functions for creating designs for any \nnumber of factors:\n\n- Factorial Designs\n - General Full-Factorial (``fullfact``)\n - 2-level Full-Factorial (``ff2n``)\n - 2-level Fractional Factorial (``fracfact``)\n - Plackett-Burman (``pbdesign``)\n - Generalized Subset Designs (``gsd``)\n- Response-Surface Designs \n - Box-Behnken (``bbdesign``)\n - Central-Composite (``ccdesign``)\n- Randomized Designs\n - Latin-Hypercube (``lhs``)\n \nSee the original [pyDOE homepage](http://pythonhosted.org/pyDOE) for details\non usage and other notes.\n\nWhat's new?\n----------\n\n### Generalized Subset Designs\n\nIn pyDOE2 version 1.1 the [Generalized Subset Design (GSD)](https://doi.org/10.1021/acs.analchem.7b00506)\nis introduced. GSD is a generalization of traditional fractional factorial\ndesigns to problems where factors can have more than two levels.\n\nIn many application problems factors can have categorical or quantitative\nfactors on more than two levels. Previous reduced designs have not been\nable to deal with such types of problems. Full multi-level factorial\ndesigns can handle such problems but are however not economical regarding\nthe number of experiments.\n\nThe GSD provide balanced designs in multi-level experiments with the number\nof experiments reduced by a user-specified reduction factor. Complementary\nreduced designs are also provided analogous to fold-over in traditional\nfractional factorial designs.\n\nGSD is available in pyDOE2 as:\n\n```\nimport pyDOE2\n\nlevels = [2, 3, 4] # Three factors with 2, 3 or 4 levels respectively.\nreduction = 3 # Reduce the number of experiment to approximately a third.\n\npyDOE2.gsd(levels, reduction)\n```\n\n\nRequirements\n------------\n\n- NumPy\n- SciPy\n\nInstallation and download\n-------------------------\n\nThrough pip:\n\n```\npip install pyDOE2\n```\n\n\nCredits\n-------\n\n`pyDOE` original code was originally converted from code by the following \nindividuals for use with Scilab:\n \n- Copyright (C) 2012 - 2013 - Michael Baudin\n- Copyright (C) 2012 - Maria Christopoulou\n- Copyright (C) 2010 - 2011 - INRIA - Michael Baudin\n- Copyright (C) 2009 - Yann Collette\n- Copyright (C) 2009 - CEA - Jean-Marc Martinez\n\n- Website: forge.scilab.org/index.php/p/scidoe/sourcetree/master/macros\n\n`pyDOE` was converted to Python by the following individual:\n\n- Copyright (c) 2014, Abraham D. Lee\n\nThe following individuals forked and works on `pyDOE2`:\n\n- Copyright (C) 2018 - Rickard Sj\u00f6gren and Daniel Svensson\n\n\nLicense\n-------\n\nThis package is provided under two licenses:\n\n1. The *BSD License* (3-clause)\n2. Any other that the author approves (just ask!)\n\nReferences\n----------\n\n- [Factorial designs](http://en.wikipedia.org/wiki/Factorial_experiment)\n- [Plackett-Burman designs](http://en.wikipedia.org/wiki/Plackett-Burman_design)\n- [Box-Behnken designs](http://en.wikipedia.org/wiki/Box-Behnken_design)\n- [Central composite designs](http://en.wikipedia.org/wiki/Central_composite_design)\n- [Latin-Hypercube designs](http://en.wikipedia.org/wiki/Latin_hypercube_sampling)\n- Surowiec, Izabella, Ludvig Vikstr\u00f6m, Gustaf Hector, Erik Johansson,\nConny Vikstr\u00f6m, and Johan Trygg. \u201cGeneralized Subset Designs in Analytical\nChemistry.\u201d Analytical Chemistry 89, no. 12 (June 20, 2017): 6491\u201397.\nhttps://doi.org/10.1021/acs.analchem.7b00506.",
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