paquo 0.5.0

library for interacting with QuPath

PAQUO: PAthological QUpath Obsession

Welcome to paquo :wave:, a library for interacting with QuPath from Python.

paquo's goal is to provide a pythonic interface to important features of QuPath, and to make creating and working with QuPath projects intuitive for Python programmers.

We strive to make your lives as easy as possible: If paquo is not pythonic, unintuitive, slow or if its documentation is confusing, it's a bug in paquo. Feel free to report any issues or feature requests in the issue tracker!

Development happens on github :octocat:

Documentation

You can find paquo's documentation at paquo.readthedocs.io :heart:

Installation

paquo's stable releases can be installed via pip:

pip install paquo

or via conda:

conda install -c conda-forge paquo

Getting QuPath

After installing, paquo requires a QuPath installation to run. To get QuPath follow the installation instructions. If you choose the default installation paths paquo should autodetect your QuPath.

Or you can run the following command to download a specific version of QuPath to a location on your machine. Follow the printed instructions to configure paquo to use that version.

> paquo get_qupath --install-path "/some/path/on/your/machine" 0.3.2
# downloading: https://github.com/qupath/qupath/releases/download/v0.3.2/QuPath-0.3.2-Linux.tar.xz
# progress ................... OK
# extracting: [...]/QuPath-0.3.2-Linux.tar.xz
# available at: /some/path/on/your/machine/QuPath-0.3.2
#
# use via environment variable:
#   $ export PAQUO__QUPATH_DIR=/some/path/on/your/machine/QuPath-0.3.2
#
# use via .paquo.toml config file:
#   qupath_dir="/some/path/on/your/machine/QuPath-0.3.2"
/some/path/on/your/machine/QuPath-0.3.2

Development Installation

1. Install conda and git
2. Clone paquo git clone https://github.com/bayer-science-for-a-better-life/paquo.git
3. Run conda env create -f environment.devenv.yaml
4. Activate the environment conda activate paquo

Note that in this environment paquo is already installed in development mode, so go ahead and hack.

Contributing Guidelines

• Please follow pep-8 conventions but:
  • We allow 120 character long lines (try anyway to keep them short)
• 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.

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 Andreas Poehlmann and Santi Villalba from the Machine Learning Research group at Bayer. In collaboration with the Pathology Lab 2 and the Mechanistic and Toxicologic Pathology group.

paquo: copyright 2020-2022 Bayer AG, licensed under GPL-3.0