Welcome to pavo
đź‘‹, 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
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.
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.
- Install
git
andconda
andconda-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 insetup.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
- 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 usepytest
.
Build with love by the Machine Learning Research group at Bayer.
pavo
: copyright 2020 Bayer AG