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 :octocat:

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