Skip to main content

Interactive surface analysis in napari for measuring mechanical stresses in biological tissues

Project description

License PyPI Python Version tests codecov pre-commit PyPI - Downloads napari hub DOI

napari-stress

This plugin provides tools for the analysis of surfaces in Napari, such as utilities to determine and refine the surface-representation of objects using a ray-casting approach and calculate the curvature of surfaces. It re-implements code in Napari that was written for Gross et al. (2021): STRESS, an automated geometrical characterization of deformable particles for in vivo measurements of cell and tissue mechanical stresses and has been made open source in this repository.

Usage

Functionality in this repository is divided in two groups: Recipes and plugins.

Recipes

Receipes are workflows for processing images, points and surface data step-by-step.

Recipe Description
Confocal data (.tif), 3D+t: Interactive tutorial on how to extract surfaces from intensity image data
Confocal data (.tif), 3D+t: Interactive tutorial on how to extract curvature from surfaces
Confocal data (.tif), 3D+t: Jupyter notebook for processing single channel data and extracting gaussian curvature.

Plugins

All functions in napari-stress are documented separately for interactive usage from the napari viewer as well as Jupyter notebooks.

Function Links
Fit spherical harmonics: Interactive Code
Surface tracing: Code
Reconstruct surface: Code

Utilities

Data to be used for this plugin is typically of the form [TZYX] (e.g., 3D + time). Napari-stress offers convenient ways to use functions from other repositories (which are often made for 3D data) on timelapse data with the frame_by_frame function and the TimelapseConverter class. Both are described in more detail in this notebook.

Installation

Create a new conda environment with the following command. If you have never used conda before, please read this guide first.

conda create -n napari-stress Python=3.9 napari jupyterlab -c conda-forge
conda activate napari-stress

You can then install napari-stress using pip:

pip install napari-stress

Issues

To report bugs, request new features or get in touch, please open an issue or tag @EL_Pollo_Diablo on image.sc.

See also

There are other napari plugins with similar / overlapping functionality

Contributing

Contributions are very welcome. Tests can be run with pytest, please ensure the coverage at least stays the same before you submit a pull request.

License

Distributed under the terms of the BSD-3 license, "napari-stress" is free and open source software

Acknowledgements

This project was supported by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy – EXC2068 - Cluster of Excellence "Physics of Life" of TU Dresden.

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

napari-stress-0.0.15.tar.gz (96.1 kB view details)

Uploaded Source

Built Distribution

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

napari_stress-0.0.15-py3-none-any.whl (98.0 kB view details)

Uploaded Python 3

File details

Details for the file napari-stress-0.0.15.tar.gz.

File metadata

  • Download URL: napari-stress-0.0.15.tar.gz
  • Upload date:
  • Size: 96.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.12

File hashes

Hashes for napari-stress-0.0.15.tar.gz
Algorithm Hash digest
SHA256 e4fcc6285a49794c4a33c85fdc7bf6cb6327217e8d3daa213c6faa33c1e26ee5
MD5 02b29d3dbdbd02cec468c3c14d5b4e4b
BLAKE2b-256 97e476d19b5ef335d67726a5e9318037e754ecb4ec72749677d3efb7cd03fbd1

See more details on using hashes here.

File details

Details for the file napari_stress-0.0.15-py3-none-any.whl.

File metadata

  • Download URL: napari_stress-0.0.15-py3-none-any.whl
  • Upload date:
  • Size: 98.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.12

File hashes

Hashes for napari_stress-0.0.15-py3-none-any.whl
Algorithm Hash digest
SHA256 3fc8ca42facd0f36f0c36f4172cceba058e9863cf7d877e43f96cef6ea46fe73
MD5 82ce0e6455b104986c467810c0b8a460
BLAKE2b-256 52f840ffea6a97e3cfd53415be710315b4d13cb7e77b328e267ebf1471430e98

See more details on using hashes here.

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