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Description:

pavo 0.3.0

PAVO: PAthological Visualization Obsession
Welcome to pavo :wave:, 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

Install git and conda and conda-devenv
Clone pavo git clone https://github.com/bayer-group/pavo.git
Change directory cd pavo
Run conda devenv --env PAVO_DEVEL=TRUE -f environment.devenv.yml --print > environment.yml
Run conda env create -f environment.yml
Activate the environment conda activate pavo
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