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https://github.com/Bayer-Group/pado
PAthological Data Obsession - cloud native digital pathology datasets
https://github.com/Bayer-Group/pado
digital-pathology python
Last synced: 13 days ago
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PAthological Data Obsession - cloud native digital pathology datasets
- Host: GitHub
- URL: https://github.com/Bayer-Group/pado
- Owner: Bayer-Group
- License: bsd-3-clause
- Created: 2022-09-08T00:40:24.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-03-01T13:46:57.000Z (almost 2 years ago)
- Last Synced: 2025-01-04T07:19:56.765Z (27 days ago)
- Topics: digital-pathology, python
- Language: Python
- Homepage:
- Size: 888 KB
- Stars: 18
- Watchers: 4
- Forks: 2
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: CODEOWNERS
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README
# PADO: PAthological Data Obsession
[](https://pypi.org/project/pado/)
[](https://anaconda.org/conda-forge/pado)
[](https://pado.readthedocs.io)
[](https://github.com/Bayer-Group/pado/actions)
[](https://github.com/Bayer-Group/pado)
[](https://github.com/Bayer-Group/pado/issues)
[](https://github.com/Bayer-Group/pado/milestones?direction=asc&sort=due_date&state=open)
Welcome to `pado` :wave:, a dataset library for accessing histopathological
datasets in a standardized way from [Python](https://www.python.org/).
`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](https://github.com/Bayer-Group/pado)
: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.
```pycon
>>> 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
```pycon
>>> 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
```bash
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`:
```bash
pip install "git+https://github.com/Bayer-Group/pado@main#egg=pado[cli,create]"
```
or for development you can clone and install via:
```bash
git clone https://github.com/Bayer-Group/pado.git
cd pathdrive-pado
pip install -e ".[cli,create,dev]"
```
if you prefer conda environments:
```bash
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](https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard).
- 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](https://pre-commit.com/) 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](https://github.com/sdvillal) 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