Metadata-Version: 2.4
Name: pyDOE3
Version: 1.2.0
Summary: Design of experiments for Python
Project-URL: source, https://github.com/relf/pyDOE3
Author-email: Rémi Lafage <remi.lafage@onera.fr>
License-Expression: BSD-3-Clause
License-File: LICENSE
Keywords: DOE,design of experiments,experimental design,optimization,python,statistics
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 :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
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
Requires-Dist: numpy
Requires-Dist: scipy
Description-Content-Type: text/markdown

pyDOE3: An experimental design package for python
=================================================

[![Tests](https://github.com/relf/pyDOE3/actions/workflows/tests.yml/badge.svg)](https://github.com/relf/pyDOE3/actions/workflows/tests.yml)
[![Documentation](https://readthedocs.org/projects/pydoe3/badge/?version=latest)](https://pydoe3.readthedocs.io/en/latest/?badge=latest)
[![DOI](https://zenodo.org/badge/709347557.svg)](https://zenodo.org/doi/10.5281/zenodo.10958492)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

`pyDOE3` is a fork of the [`pyDOE2`](https://github.com/clicumu/pyDOE2) 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``)
  - Doehlert Design (``doehlert_shell_design``, ``doehlert_simplex_design``)
- Randomized Designs
  - Latin-Hypercube (``lhs``)
- Taguchi Designs
  - Orthogonal arrays and robust design utilities (``taguchi_design``, ``compute_snr``)
  
See [Documentation](https://pydoe3.readthedocs.io).

Installation
------------

```bash
pip install pyDOE3
```

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

`pyDOE` was converted to Python by the following individual:

- Copyright (c) 2014, Abraham D. Lee

The following individuals forked `pyDOE` and worked on `pyDOE2`:

- Copyright (C) 2018, Rickard Sjögren and Daniel Svensson

License
-------

This package is provided under the *BSD License* (3-clause)

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)
- [Doehlert Design](https://academic.oup.com/jrsssc/article/19/3/231/6882590)
- [Latin-Hypercube designs](http://en.wikipedia.org/wiki/Latin_hypercube_sampling)
- [Taguchi designs](http://en.wikipedia.org/wiki/Taguchi_methods)
- [Generalized Subset Designs](https://doi.org/10.1021/acs.analchem.7b00506)
