Skip to main content

A Python toolbox for analysing animal body movements across space and time

Project description

Python Version PyPI Version Conda Forge Version Downloads License CI codecov Binder Code style: Ruff pre-commit project chat DOI

movement

A Python toolbox for analysing animal body movements across space and time.

Quick install

Create and activate a conda environment with movement installed (including the GUI):

conda create -n movement-env -c conda-forge movement napari pyqt6
conda activate movement-env

[!Note] Read the documentation for more information, including full installation instructions and examples.

Overview

Deep learning methods for motion tracking have revolutionised a range of scientific disciplines, from neuroscience and biomechanics, to conservation and ethology. Tools such as DeepLabCut and SLEAP now allow researchers to track animal movements in videos with remarkable accuracy, without requiring physical markers. However, there is still a need for standardised, easy-to-use methods to process the tracks generated by these tools.

movement aims to provide a consistent, modular interface for analysing motion tracks, enabling steps such as data cleaning, visualisation, and motion quantification. We aim to support all popular animal tracking frameworks and file formats.

Find out more on our mission and scope statement and our roadmap.

[!Tip] If you prefer analysing your data in R, we recommend checking out the animovement toolbox, which is similar in scope. We are working together with its developer to gradually converge on common data standards and workflows.

Join the movement

movement is made possible by the generous contributions of many people.

We welcome and encourage contributions in any form—whether it is fixing a bug, developing a new feature, or improving the documentation—as long as you follow our code of conduct.

Go to our community page to find out how to connect with us and get involved.

Citation

If you use movement in your work, please cite the following Zenodo DOI:

Nikoloz Sirmpilatze, Chang Huan Lo, Sofía Miñano, Brandon D. Peri, Dhruv Sharma, Laura Porta, Iván Varela & Adam L. Tyson (2024). neuroinformatics-unit/movement. Zenodo. https://zenodo.org/doi/10.5281/zenodo.12755724

License

⚖️ BSD 3-Clause

Package template

This package layout and configuration (including pre-commit hooks and GitHub actions) have been copied from the python-cookiecutter template.

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

movement-0.15.0.tar.gz (111.9 kB view details)

Uploaded Source

Built Distribution

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

movement-0.15.0-py3-none-any.whl (115.3 kB view details)

Uploaded Python 3

File details

Details for the file movement-0.15.0.tar.gz.

File metadata

  • Download URL: movement-0.15.0.tar.gz
  • Upload date:
  • Size: 111.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for movement-0.15.0.tar.gz
Algorithm Hash digest
SHA256 a45c234b2ef1ff2f045844cb1bf55baf8d5711d39f38edfecc31be7cb54a6a0f
MD5 dc7f8af59a832cf18cedcaba5e78e5ac
BLAKE2b-256 5733a752ac735fc541b9274260163dc3cc5fbee2f33754d2d9c87bd9f80705b2

See more details on using hashes here.

File details

Details for the file movement-0.15.0-py3-none-any.whl.

File metadata

  • Download URL: movement-0.15.0-py3-none-any.whl
  • Upload date:
  • Size: 115.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for movement-0.15.0-py3-none-any.whl
Algorithm Hash digest
SHA256 9321a67e7f6c8908409902b62acf738c969126cf5f51203e5103a1f11228789a
MD5 829a69de1f9c5e874aaa94d50ddeef23
BLAKE2b-256 46c0d4b48a97f8545d7a7ac42cf48f2d628ef6affdf34b75c2f278ecb1de398d

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