A Python toolbox for analysing animal body movements across space and time.
Create and activate a conda environment with movement installed (including the GUI):
conda create -n movement-env -c conda-forge movement napari pyqt
conda activate movement-envNote
Read the documentation for more information, including full installation instructions and examples.
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.
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.
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
⚖️ BSD 3-Clause
This package layout and configuration (including pre-commit hooks and GitHub actions) have been copied from the python-cookiecutter template.
