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Package to generate and analyse orthogonal arrays and optimal designs

Project description

Orthogonal Array Package

The Orthogonal Array package contains functionality to generate and analyse orthogonal arrays, optimal designs and conference designs. Features include generation of complete series of orthogonal arrays, reduction of arrays to normal form and calculation of properties such as the strength or D-efficiency of an array. For more information about the package see the documentation at oapackage.readthedocs.io and the webpage http://pietereendebak.nl/oapackage/.

Usage

The package can be used from Python:

>>> import oapackage
>>> al=oapackage.exampleArray(0)
>>> al.showarray()
array: 
  0   0
  0   0
  0   1
  0   1
  1   0
  1   0
  1   1
  1   1
>>> print('D-efficiency %f, rank %d' % (al.Defficiency(), al.rank()) )
D-efficiency 1.000000, rank 2
>>> print('Generalized wordlength pattern: %s' % str(al.GWLP()))
Generalized wordlength pattern: (1.0, 0.0, 0.0)

For more examples see the Jupyter notebooks in the docs/examples.

Acknowledgements

If you use this code or any of the results, please cite this program as follows:

The code was written by:

Ideas contributed by:

See the file LICENSE for copyright details.

Installation

PyPI version Build status Build Status

The Python interface to the package is available on the Python Package index. Installation can be done using the following command:

$ pip install OApackage 

(or pip install OApackage --user if you do not have admin rights). To compile the package you need Python, Numpy and Swig 3.x.

The command line tools have been tested using Linux, Windows XP/Win7/Win10 and Raspberry Pi. The program uses a cmake build system. From the command line type:

$ mkdir -p build; cd build
$ cmake ..
$ make
$ make install

Testing

To perform tests run pytest. To obtain a coverage report, run

$ coverage run --source='./oapackage' -m pytest
$ coverage report --omit oapackage/markup.py,oapackage/tests/*.py,oapackage/deprecated.py

Continuous integration and testing for the C++ library is performed on Travis and for the Python package on AppVeyor.

Project details


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