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update project overview
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README.md

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# movement
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A Python toolbox for analysing body movements across space and time, to aid the study of animal behaviour in neuroscience.
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A Python toolbox for analysing animal body movements across space and time.
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![](docs/source/_static/movement_overview.png)
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## Overview
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Pose estimation tools, such as [DeepLabCut](https://www.mackenziemathislab.org/deeplabcut) and [SLEAP](https://sleap.ai/) are now commonplace when processing video data of animal behaviour. There is not yet a standardised, easy-to-use way to process the pose tracks produced from these software packages.
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movement aims to provide a consistent modular interface to analyse pose tracks, allowing steps such as data cleaning, visualisation and motion quantification.
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We aim to support a range of pose estimation packages, along with 2D or 3D tracking of single or multiple individuals.
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Machine learning-based tools such as
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[DeepLabCut](https://www.mackenziemathislab.org/deeplabcut) and
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[SLEAP](https://sleap.ai/) have become commonplace for tracking the
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movements of animals and their body parts in videos.
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However, there is still a need for a standardized, easy-to-use method
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to process the tracks generated by these tools.
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`movement` aims to provide a consistent, modular interface for analyzing
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motion tracks, enabling steps such as data cleaning, visualization,
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and motion quantification. We aim to support a range of animal tracking
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frameworks, as well as 2D or 3D tracking of single or multiple individuals.
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Find out more on our [mission and scope](https://movement.neuroinformatics.dev/community/mission-scope.html) statement and our [roadmap](https://movement.neuroinformatics.dev/community/roadmaps.html).
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docs/source/index.md

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(target-movement)=
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# movement
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A Python toolbox for analysing body movements across space and time, to aid the study of animal behaviour in neuroscience.
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A Python toolbox for analysing animal body movements across space and time.
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## Overview
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Pose estimation tools, such as [DeepLabCut](dlc:) and [SLEAP](sleap:) are now commonplace when processing video data of animal behaviour. There is not yet a standardised, easy-to-use way to process the *pose tracks* produced from these software packages.
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Machine learning-based tools such as [DeepLabCut](dlc:) and [SLEAP](sleap:)
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have become commonplace for tracking the movements of animals and their body
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parts in videos. However, there is still a need for a standardized, easy-to-use method
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to process the tracks generated by these tools.
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movement aims to provide a consistent modular interface to analyse pose tracks, allowing steps such as data cleaning, visualisation and motion quantification.
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We aim to support a range of pose estimation packages, along with 2D or 3D tracking of single or multiple individuals.
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``movement`` aims to provide a consistent, modular interface for analyzing
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motion tracks, enabling steps such as data cleaning, visualization,
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and motion quantification. We aim to support a range of animal tracking
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frameworks, as well as 2D or 3D tracking of single or multiple individuals.
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Find out more on our [mission and scope](target-mission) statement and our [roadmap](target-roadmaps).
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