Metadata-Version: 2.1
Name: pyDOE2
Version: 1.2.1
Summary: Design of experiments for Python
Home-page: https://github.com/clicumu/pyDOE2
Author: Rickard Sjoegren
Author-email: r.sjogren89@gmail.com
License: BSD License (3-Clause)
Description: 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.
        
Keywords: DOE,design of experiments,experimental design,optimization,statistics,python
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Description-Content-Type: text/markdown
