Skip to main content

Megabouts: a toolbox to quantify zebrafish locomotion

Project description

Megabouts 🐟

Megabouts Logo

PyPI version Documentation Status

Overview

Megabouts is a Python toolbox designed to quantify zebrafish larval locomotion. It supports in both freely swimming and head-restrained conditions, adapting to various experimental setups and recording framerate.

📚 Full documentation and tutorials available at: megabouts.ai

Features

  • Flexible Data Input:

    • Multiple tracking methods (full tracking, head tracking, tail tracking)
    • Support various frame rates (20-700 Hz) and resolutions
    • Compatible with different tracking software (e.g., SLEAP)
  • Analysis Pipeline:

    • Tail angle and trajectory preprocessing
    • Tail bout detection
    • Movement classification using transformer neural networks
    • Sparse coding for head-restrained experiments

Installation

pip install megabouts

Quick Start

from megabouts.tracking_data import TrackingConfig, FullTrackingData, load_example_data
from megabouts.pipeline import FullTrackingPipeline

# Load example data
df_recording, fps, mm_per_unit = load_example_data("fulltracking_posture")

# Configure tracking
tracking_cfg = TrackingConfig(fps=fps, tracking="full_tracking")

# Create tracking data
head_x = df_recording["head_x"].values * mm_per_unit
head_y = df_recording["head_y"].values * mm_per_unit
head_yaw = df_recording["head_angle"].values
tail_angle = df_recording.filter(like="tail_angle").values

tracking_data = FullTrackingData.from_posture(
    head_x=head_x, 
    head_y=head_y, 
    head_yaw=head_yaw, 
    tail_angle=tail_angle
)

# Create pipeline and run
pipeline = FullTrackingPipeline(tracking_cfg)
tracking, tail, traj, segments, bouts, ethogram = pipeline.run(tracking_data)

License

This software is available for non-commercial research and academic use only. The software was developed by Adrien Jouary, Ph.D. in the laboratory of Michael B. Orger at Fundação Champalimaud. For full license terms, please see the LICENSE.md file.

Citation

@article{jouary2024megabouts,
    title={Megabouts: a flexible pipeline for zebrafish locomotion analysis},
    author={Jouary, Adrien and Laborde, Alexandre and Silva, Pedro T and Mata, J Miguel 
            and Marques, Joao C and Collins, Elena and Peterson, Randall T 
            and Machens, Christian K and Orger, Michael B},
    journal={bioRxiv},
    pages={2024--09},
    year={2024},
    publisher={Cold Spring Harbor Laboratory}
}

Contact

  • Author: Adrien Jouary
  • Email: adrien.jouary at research.fchampalimaud.org
  • Organization: Champalimaud Foundation

Acknowledgments

This work was developed at the Champalimaud Foundation in the laboratory of Michael B. Orger.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

megabouts-0.1.2.tar.gz (78.2 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

megabouts-0.1.2-py3-none-any.whl (78.8 MB view details)

Uploaded Python 3

File details

Details for the file megabouts-0.1.2.tar.gz.

File metadata

  • Download URL: megabouts-0.1.2.tar.gz
  • Upload date:
  • Size: 78.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for megabouts-0.1.2.tar.gz
Algorithm Hash digest
SHA256 f27e5b26b99b26d6772888bd0cd9e098785a633a6864dee8828ef4ae9ebaa5d2
MD5 3de85b705b1b3d3522519553ba0d3d26
BLAKE2b-256 c107b519f3eb148ed782080d330db824bebb512c15b25ec2cb176dbe1753f16f

See more details on using hashes here.

Provenance

The following attestation bundles were made for megabouts-0.1.2.tar.gz:

Publisher: publish.yml on orger-lab/megabouts

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file megabouts-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: megabouts-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 78.8 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for megabouts-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 90f3c14d08e024f2520111455a81e375400e2244c9c03e08283249b7914e0754
MD5 de57479426f71ac2f1c7a14adccf20e5
BLAKE2b-256 edc67ac13caedd875c0e0da0042e45e340c96558a75e396943a840153b1dd1a2

See more details on using hashes here.

Provenance

The following attestation bundles were made for megabouts-0.1.2-py3-none-any.whl:

Publisher: publish.yml on orger-lab/megabouts

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page