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animal-behavior-analysis is a Python repository to analyze animal behavior in an unsupervised fashion. It uses UMAP dimensionality reduction and watershed segmentation to classify preprocessed animal behavior data obtained from video-tracking animal body parts with LEAP or DeepLabCut.

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alvaro-concha/animal-behavior-analysis

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animal-behavior-analysis

Neurobiology of Movement Lab, Medical Physics Department, Bariloche Atomic Centre, CNEA

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Animal behavior analysis

Introduction

animal-behavior-analysis is a Python repository to produce animal behavior embeddings and behavior labels in an unsupervised way. It uses the UMAP dimensionality reduction algorithm to create a low-dimensional behavior representation from a set of predefined features. The, it uses a watershed segmentation algorithm to identify clusters in the ethogram.

This repository was designed to be integrated after animal-behavior-preprocessing. animal-behavior-preprocessing is the preparatory step, before using animal-behavior-analysis. Both repositories are configured and optimized for analysing mouse behavior motion-tracking data, during the performance of accelerating rotarod tasks, recorded using both a frontal and a back camera, simultaneously.

animal-behavior-analysis is an implementation of a pipeline that runs several tasks. To do this, we use doit, a Python package that functions as a build-system tool, similar to make, but with Python syntax!. This build-system watches and keeps track of changes in files ("inputs and outputs", also known as "dependencies and targets", in build-system lingo) used by each task in the pipeline. In this way, it makes it easier to keep files up-to-date, when tweaking different aspects of the pipeline (like modifying parameters, adding or removing tasks, etc.). The build-system also figures out which tasks can be run in parallel, if a multi-core CPU is available.

animal-behavior-analysis module imports Modules in animal-behavior-analysis and their import structure. An arrow from module_a to module_b indicates that module_a is importing module_b. The dodo module is the main module of the pipeline, and is named this way to conform with doit naming conventions. IPython Notebooks are not displayed in the graph.

Instalation

In a terminal, run:

git clone
pip3 install -e git+http://github.com/alvaro-concha/animal-behavior-analysis.git#egg=AnimalBehaviorAnalysis

Organization of the project

The project has the following structure:

animal-behavior-preprocessing/
  |- LICENSE.md
  |- logo.svg
  |- module_imports.svg
  |- README.md
  |- requirements.txt
  |- setup.py
  |- animal_behavior_analysis/
     |- bundle_edges.py
     |- config_dodo.py
     |- config.py
     |- data_aggregate.py
     |- dodo.py
     |- dyadic_frequencies.py
     |- figures_step_feature_selection.ipynb
     |- mutual_info.ipynb
     |- transitions.ipynb
     |- umap_embed.py
     |- umap_paso_pose.ipynb
     |- umap_video.ipynb
     |- umap_video.py
     |- umap_wavelet.ipynb
     |- utilities.py
     |- watershed_segment.py

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animal-behavior-analysis is a Python repository to analyze animal behavior in an unsupervised fashion. It uses UMAP dimensionality reduction and watershed segmentation to classify preprocessed animal behavior data obtained from video-tracking animal body parts with LEAP or DeepLabCut.

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