pado 0.12.0

Description:

pado 0.12.0

PADO: PAthological Data Obsession







Welcome to pado :wave:, a dataset library for accessing histopathological
datasets in a standardized way from Python.
pado's goal is to provide a unified way to access data from diverse
datasets. Its scope is very small and the design tries to keep everything
simple.
As always: If pado is not pythonic,
unintuitive, slow or if its documentation is confusing, it's a bug in
pado. Feel free to report any issues or feature requests in the issue
tracker!
Development
happens on github
:octocat:
Quickstart
To quickly get a pado dataset, for testing and familiarizing with the interface
you can create a fake dataset, that's also used in the internal tests.
>>> from pado.mock import mock_dataset
>>> ds = mock_dataset(None)
>>> ds
PadoDataset('memory://pado-f5869e41-5246-4378-9057-96fda1c40edf', mode='r+')

This creates a test dataset in memory with 3 images and some fake metadata
>>> len(ds)
3
>>> ds.index
(ImageId('mock_image_0.svs', site='mock'),
ImageId('mock_image_1.svs', site='mock'),
ImageId('mock_image_2.svs', site='mock'))
>>> ds[0].image
Image(...)
>>> ds[0].metadata
A B C D
ImageId('mock_image_0.svs', site='mock') a 2 c 4

Documentation
The documentation is currently provided in this repository and has to be
build via sphinx. It'll be available online soon.
To build it, in the repository root, run
python -m pip install -e ".[docs]"
cd docs
make html

Access the documentation then at docs/build/html/index.html
Development Installation
pado can be installed directly via pip:
pip install "git+https://github.com/Bayer-Group/pado@main#egg=pado[cli,create]"

or for development you can clone and install via:
git clone https://github.com/Bayer-Group/pado.git
cd pathdrive-pado
pip install -e ".[cli,create,dev]"

if you prefer conda environments:
git clone https://github.com/Bayer-Group/pado.git
cd pathdrive-pado
conda install conda-devenv
conda devenv
conda activate pado

Note that in this environment pado is already installed in development mode,
so go ahead and hack.
Contributing 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.
Please install pre-commit and install the hooks by running pre-commit install in the project root folder.

You can setup your IDE to help you adhering to these guidelines.

(Santi is happy to help you setting up pycharm in 5 minutes)
Acknowledgements
Build with love by Santi Villalba and Andreas Poehlmann from the Machine Learning Research group at Bayer.
pado: copyright 2020-2022 Bayer AG