Keypoint MoSeq
latest

Setup

  • Local installation
  • Google colab

Tutorial

  • Project setup
  • Model fitting
  • Visualization
  • Changepoints

FAQs

  • Code usage
  • Input data
  • Modeling
  • Troubleshooting

Developer API

  • Model fitting
  • Visualization
  • Input/Output
  • Utilities
  • Error Calibration
Keypoint MoSeq
  • Keypoint MoSeq
  • Edit on GitHub

Keypoint MoSeq

GitHub

Colab

Paper

Slack

License

_images/logo.jpg

Motion Sequencing (MoSeq) is an unsupervised machine learning method for animal behavior analysis. Given behavioral recordings, MoSeq learns a set of stereotyped movement patterns and when they occur over time. This package provides tools for fitting a MoSeq model to keypoint tracking data.

Setup

  • Local installation
    • Install using conda
    • Install using pip
  • Google colab

Tutorial

  • Project setup
    • Edit the config file
    • Load data
    • Calibration
    • Fit PCA
  • Model fitting
    • Setting hyperparameters
    • Initialization
    • Fitting an AR-HMM
    • Fitting the full model
    • Extract model results
      • Save results in csv format
    • Apply to new data
  • Visualization
    • Trajectory plots
    • Crowd & grid movies
  • Changepoints
    • Load data
    • Find changepoints
    • Visualize changepoints

FAQs

  • Code usage
  • Input data
    • How much data do I need?
    • How many/which keypoints?
    • Multiple animals
    • Keypoints are noisy
    • High proportion of NaNs
    • Should I preprocess keypoints?
    • Head-fixed animals
    • Non-rodents
    • Loading data from methods other than SLEAP or DeepLabCut
    • Size variation between animals
  • Modeling
    • Validating model outputs
    • Choosing the target syllable duration
    • Number of model fitting iterations
    • Detecting existing syllables in new data
    • Continue model fitting but with new data
    • Interpreting model outputs
  • Troubleshooting
    • Dead kernel
    • Out of memory
    • NaNs during fitting
    • Installation errors

Developer API

  • Model fitting
  • Visualization
  • Input/Output
  • Utilities
  • Error Calibration
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