Pyneapple is an advanced tool for analysing multi-exponential signal data in MR DWI images. It is able to apply a variety of different fitting algorithms (NLLS, NNLS, ...) to the measured diffusion data and to compare multi-exponential fitting methods. Thereby it can determine the total number of components contributing to the corresponding multi-exponential signal fitting approaches and analyses the results by calculating the corresponding diffusion parameters. Fitting can be customised to be performed on a pixel by pixel or segmentation-wise basis.
There are different ways to get Pyneapple running depending on the desired use case. If you want to integrate Pyneapple in your existing workflow to use the processing utilities the best way to go is using pip with the git tag.
pip install git+https://github.com/darksim33/PyneappleIf your planing on altering the code by forking or cloning the repository, Pyneapple is capable of using poetry. There are different ways to install poetry. For Windows and Linux a straight forward approach is using pipx. First you need to install pipx using pip which basically follows the same syntax as pip itself. Afterward you can install poetry in an isolated environment created by pipx.
python -m pip install pipx
python -m pipx install poetryTo use an editable installation of Pyneapple navigate to the repository directory make sure all submodules are initialized properly and perform the installation using the local virtual environment.
cd <path_to_the_repository>
git submodule update --init --recursive
poetry installThere are tow additional options for development. If you want to take advantage of the testing framework you can install the required dependencies by:
poetry install --with dev