flowshape 0.1.5

Cell shape analysis using the spherical harmonics decomposition

FlowShape

This package provides functionality for the analysis of cell shape using the spherical harmonics decomposition. Please refer to our paper "Cell shape characterization, alignment, and comparison using FlowShape" for more information.

A local branch of lie_learn that does not depend on cython is included (spheremesh/lie_learn).

Installation

Flow shape is available on pypi. Install via:

pip install flowshape

Demo

For the demos, you will need JupyterLab, as well as Meshplot for plotting.

To install both, run:

conda install -c conda-forge jupyterlab meshplot

Then, to open JupyterLab, run:

jupyter-lab

Download the demo folder from this repository and open the demo.ipynb notebook.

Further, demo_alignment.ipynb shows how to align meshes. demo_from_img.ipynb shows how to use marching cubes to make meshes from image stacks. There are some additional dependencies for this notebook.

How to use

See the demos for examples on how to use the package. The API consists only of functions operating on NumPy ndarrays and there are no classes. Most functions have docstrings in the source.

Citation

Bibtex:

@article{10.1093/bioinformatics/btad383,
    author = {van Bavel, Casper and Thiels, Wim and Jelier, Rob},
    title = "{Cell shape characterization, alignment, and comparison using FlowShape}",
    journal = {Bioinformatics},
    volume = {39},
    number = {6},
    pages = {btad383},
    year = {2023},
    month = {06},
    issn = {1367-4811},
    doi = {10.1093/bioinformatics/btad383},
    url = {https://doi.org/10.1093/bioinformatics/btad383},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/39/6/btad383/50738096/btad383.pdf},
}