SLEAPyFaces


A package for extracting facial expressions from SLEAP analyses with sensible assumptions.

Based on these scripts

PyPI - Version PyPI - Python Version Hatch project

Description

Sleapyfaces is a data analysis package for extracting facial expressions of mice from SLEAP analyses. It is designed to work with the SLEAP software package, which provides a graphical user interface for annotating animal behavioral videos. More information on SLEAP is available at https://sleap.ai. This package also depends on many assumptions about the data format and structure of the SLEAP analyses. It is not intended to be a general tool for extracting facial expressions from SLEAP analyses, but rather a tool for extracting facial expressions from the specific data format and structure used in the lab of Dr. Kay Tye at The Salk Institute for Biological Studies.


Table of Contents

Pages


Citing:

If you use SLEAPyFaces in your research, this does not fall under standard software according to the Publication Manual for the APA, MLA, AMA, Turabian, IEEE, Vancouver style, Harvard style, or Chicago style guides. Please cite the following:

A. Ehler, J. Delahantey, A. Coley, D. LeDuke, L. Keyes, T.D. Pereira, and K. Tye. SLEAPyFaces: A package for extracting facial expressions from SLEAP analyses with sensible assumptions. SLEAPyFaces python package, v1.0.1, 2023. Retrieved from https://github.com/annie444/sleapyfaces/

BibTeX:

 @misc{Ehler2023sleapyfaces,
    title={SLEAPyFaces: A package for extracting facial expressions from SLEAP analyses with sensible assumptions.},
    author={
        Ehler, Analetta and
        Delahantey, Jeremey and
        Coley, Austin and
        LeDuke, Deryn and
        Keyes, Laurel and
        Pereira, Talmo D and
        Tye, Kay},
    url={https://github.com/annie444/sleapyfaces/},
    journal={SLEAPyFaces python package},
    publisher={GitHub repository},
    volume={v1.0.1},
    year={2023},
    month={Dec},
    day={27}
}

Please also cite the original SLEAP paper:

T.D. Pereira, N. Tabris, A. Matsliah, D. M. Turner, J. Li, S. Ravindranath, E. S. Papadoyannis, E. Normand, D. S. Deutsch, Z. Y. Wang, G. C. McKenzie-Smith, C. C. Mitelut, M. D. Castro, J. D’Uva, M. Kislin, D. H. Sanes, S. D. Kocher, S. S-H, A. L. Falkner, J. W. Shaevitz, and M. Murthy. Sleap: A deep learning system for multi-animal pose tracking. Nature Methods, 19(4), 2022

BibTeX:

@ARTICLE{Pereira2022sleap,
   title={SLEAP: A deep learning system for multi-animal pose tracking},
   author={Pereira, Talmo D and
      Tabris, Nathaniel and
      Matsliah, Arie and
      Turner, David M and
      Li, Junyu and
      Ravindranath, Shruthi and
      Papadoyannis, Eleni S and
      Normand, Edna and
      Deutsch, David S and
      Wang, Z. Yan and
      McKenzie-Smith, Grace C and
      Mitelut, Catalin C and
      Castro, Marielisa Diez and
      D'Uva, John and
      Kislin, Mikhail and
      Sanes, Dan H and
      Kocher, Sarah D and
      Samuel S-H and
      Falkner, Annegret L and
      Shaevitz, Joshua W and
      Murthy, Mala},
   journal={Nature Methods},
   volume={19},
   number={4},
   year={2022},
   publisher={Nature Publishing Group}
   }
}

License

sleapyfaces is distributed under the terms of the MIT license. The full license text is available in the LICENSE file.