PAVO: PAthological Visualization Obsession

Welcome to pavo 👋, a visualization tool for pado datasets.

pavo's goal is to provide a testbed for easy prototyping of data visualizations of whole slide images and metadata of digital pathology datasets.

We strive to make your lives as easy as possible: If setting up pavo is hard or unintuitive, if its interface is slow or if its documentation is confusing, it's a bug in pavo. Always feel free to report any issues or feature requests in the issue tracker!

Development happens on github

Installation

To install pavo clone the repo and run pip install . Note that you need a "nodejs==16.*" installation to be able to build from source.

Usage

pavo is used to visualize pado datasets. If you have a pado dataset just run:

pavo production run /path/to/your/dataset

and access the web ui under the printed address.

Development Environment Setup

1. Install git and conda and conda-devenv
2. Clone pavo git clone https://github.com/bayer-group/pavo.git
3. Change directory cd pavo
4. Run conda devenv --env PAVO_DEVEL=TRUE -f environment.devenv.yml --print > environment.yml
5. Run conda env create -f environment.yml
6. Activate the environment conda activate pavo
7. Setup the javascript dependencies npm install . (optional, handled in setup.py)

Note that in this environment pavo is already installed in development mode, so go ahead and hack.

• Run tests via pytest
• Run the static type analysis via mypy pavo
• Launch a development instance via pavo development run

Contributing Guidelines

• Check the contribution guidelines
• Please use numpy docstrings.
• When contributing code, please try to use Pull Requests.
• tests go hand in hand with modules on tests packages at the same level. We use pytest.

Acknowledgements

Build with love by the Machine Learning Research group at Bayer.

pavo: copyright 2020 Bayer AG