Welcome to pAPRica (Pipelines for Adaptive Particle Representation Image Compositing and Analysis), a package based on Adaptive Particle Representation (APR) to accelerate
image processing and research involving imaging and microscopy.
pAPRica was built on:
For more information on usage, examples and notebooks, check our documentation.
Briefly, pAPRica allows to accelerate image processing for volumetric data-sets while lowering the hardware requirements. It
is made of several independent modules that are tailored to convert, stitch, segment, map to an atlas and visualize
data. pAPRica can work as a postprocessing tool and is also compatible with real time usage during acquisitions,
enabling minimal lead time between imaging and analysis.
pAPRica is only available for Linux at the moment. There are no limitations to port it to Windows and Mac and you
are welcome to contact us. It should run on any computer, it's best if the RAM is 3 times the size of a
single tile.
- Download from the repo
cdinto the folder- run
pip install -e .
To report bugs and code issues 🪲: please open an issue
If you use this pipeline for your research, please consider citing the following:
- Efficient image analysis for large-scale next generation histopathology using pAPRica: preprint of the paper on BioRxiv
- Adaptive particle representation of fluorescence microscopy images: original implementation of APR published in Nature Communications.
The documentation is automatically generated by GitHub workflows when committing to the master branch and deployed on gh-pages branch. When the repo is turned public, it will be published automatically.
Alternatively the documentation can be generated locally, first cd into the documentation folder:
cd doc
Then run:
make html (linux) or make.bat html (windows)
To run test locally:
python -m pytest --import-mode=append tests/
Tests are also run automatically on GitHub workflows for each pull request.