napari-stress 0.0.5

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

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.

Installation

Create a new conda environment with

conda create -n napari-stress Python=3.9
conda activate napari-stress

Install a few necessary plugins:

conda install -c conda-forge napari jupyterlab

To install the plugin, clone the repository and install it:

git clone https://github.com/BiAPoL/napari-stress.git
cd napari-stress
pip install -e .

Usage

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

Plugins

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

Function	Links
	Spherical harmonics expansion: Interactive Code
	Surface tracing: Code

Recipes

Napari-stress provides jupyter notebooks with complete workflows for different types of input data and objectives. Napari-stress currently provides notebooks for the following data/image types:

• Confocal data (.tif), 3D+t: This type of data can be processed with napari-stressed as show in this notebook

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.

Issues

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