Skip to main content

Generate and characterize designs with four-and-two-level (FATL) factors

Project description

Four And Two Level Designs

PyPI version CI

The fatld package contains functionality to generate and characterize designs with four-and-two-level (FATL) factors. Design characteristics include word length pattern, defining relation, and number of clear interactions. For more information about the package see the documentation at https://abohyndoe.github.io/fatld/. A large collection of FATL designs can be explored interactively using a web app at https://abohyndoe.shinyapps.io/fatldesign-selection-tool/.

Usage

The package can be used from Python:

>>> import fatld
>>> D = fatld.Design(runsize=32, m=1, cols=[21, 27, 29])
>>> D.wlp()
[1, 3, 3, 0, 0]
>>> D.defining_relation()
['A1cef', 'A3deg', 'A1cdeh']
>>> print("There are %s 2-2 interactions clear from any main effect or other two-factor interaction." % D.clear('2-2'))
There are 6 2-2 interactions clear from any main effect or other two-factor interaction.
>>> print("The design contains %s four-level factors and %s two-level factors" % (D.m, D.n))
The design contains 1 four-level factors and 6 two-level factors

For more examples see the documentation.

Installation

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

pip install fatld

Development

All development is done in a virtual environment based on poetry, activate it using:

poetry shell

Code style

  • Try to follow the PEP 8 style guide. A usefull tool for automated formatting is black. We do allow lines upto 120 characters.

  • Document functions using the Numpy docstring convention

  • Linting is based on ruff, configuration is found in the pyproject.toml file.

  • Tests are ran using pytest and a coverage report can be generated using coverage inside the virtual environment:

    coverage run -m pytest tests
    coverage report -m
    

Submitting code

If you would like to contribute, please submit a pull request. (See the Github Hello World example, if you are new to Github).

By contributing to the repository you state you own the copyright to those contributions and agree to include your contributions as part of this project under the BSD license.

Bugs reports and feature requests

To submit a bug report or feature request use the Github issue tracker. Search for existing and closed issues first. If your problem or idea is not yet addressed, please open a new issue.

Contact

For further information please contact alexandre dot bohyn at kuleuven dot be

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fatld-0.1.8.tar.gz (18.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

fatld-0.1.8-py3-none-any.whl (18.5 kB view details)

Uploaded Python 3

File details

Details for the file fatld-0.1.8.tar.gz.

File metadata

  • Download URL: fatld-0.1.8.tar.gz
  • Upload date:
  • Size: 18.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.4.2 CPython/3.8.5 Linux/4.4.0-19041-Microsoft

File hashes

Hashes for fatld-0.1.8.tar.gz
Algorithm Hash digest
SHA256 a78b0866ffe0f2ee866119ada04641450d7aa9e67e20b7f24021fedd613d350e
MD5 bcec61d92a8d275aac8e70e56127fcac
BLAKE2b-256 818d0806ec49322a4b37244f58d5a4bce56d0e4c9fbc3f53cc27820fa56de33e

See more details on using hashes here.

File details

Details for the file fatld-0.1.8-py3-none-any.whl.

File metadata

  • Download URL: fatld-0.1.8-py3-none-any.whl
  • Upload date:
  • Size: 18.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.4.2 CPython/3.8.5 Linux/4.4.0-19041-Microsoft

File hashes

Hashes for fatld-0.1.8-py3-none-any.whl
Algorithm Hash digest
SHA256 24b0181a2cc19a3f5d5b8a4714853a36c1bd09a584610e90bbfdd3363867a231
MD5 a8db529b20e3bf502cbd5c29af3879f2
BLAKE2b-256 bb44ff5da47f1fe0a80526ffd05086001f4a898f2646ef23d00a0601410aa342

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page