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 totally clear from any main effect or other interaction." % D.clear('2-2', 'all'))
There are 6 2-2 interactions totally clear from any main effect or other 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-0.1.5.tar.gz (16.4 kB view details)

Uploaded Source

Built Distribution

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

fatld-0.1.5-py3-none-any.whl (16.1 kB view details)

Uploaded Python 3

File details

Details for the file fatld-0.1.5.tar.gz.

File metadata

  • Download URL: fatld-0.1.5.tar.gz
  • Upload date:
  • Size: 16.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.2 CPython/3.8.5 Linux/4.4.0-19041-Microsoft

File hashes

Hashes for fatld-0.1.5.tar.gz
Algorithm Hash digest
SHA256 1d495f3b4b4f5ee354248620b14e4656202c9f1172c712ccd6a5dd8cb9965adc
MD5 2e9568760913f9d145994d4c8c084aa3
BLAKE2b-256 a0532c63dbb09f8f32cb9b10b5d632d171366aa97fe41130659f20a6d3eb7778

See more details on using hashes here.

File details

Details for the file fatld-0.1.5-py3-none-any.whl.

File metadata

  • Download URL: fatld-0.1.5-py3-none-any.whl
  • Upload date:
  • Size: 16.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.2 CPython/3.8.5 Linux/4.4.0-19041-Microsoft

File hashes

Hashes for fatld-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 e930cf7d862c17549aa02b45c4f61d1605c2bc05f8c11d439434414fad70da4b
MD5 99c1e2687396789c0c9f583b878f7c40
BLAKE2b-256 67f37156049cc9528ff318c8000a435e4ae57d0507ca5dac0aae39c6e2b7fda3

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