(target-movement)=
# movement

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

::::{grid} 1 2 2 3
:gutter: 3

:::{grid-item-card} {fas}`book;sd-text-primary` User guide
:link: user_guide/index
:link-type: doc

Installation, supported formats and key concepts.
:::

:::{grid-item-card} {fas}`chalkboard-user;sd-text-primary` Examples
:link: examples/index
:link-type: doc

A gallery of examples using `movement`.
:::

:::{grid-item-card} {fas}`comments;sd-text-primary` Join the movement
:link: community/index
:link-type: doc

How to get in touch and contribute.
:::
::::

![](_static/movement_overview.png)

## 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](dlc:) and [SLEAP](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](target-mission) statement and our [roadmap](target-roadmaps).

```{include} /snippets/admonitions.md
```

## Citation
```{include} ../../README.md
:start-after: '## Citation'
:end-before: '## License'
```

```{toctree}
:maxdepth: 2
:hidden:

user_guide/index
examples/index
community/index
api_index
blog/index
```