Scilpy

Scilpy is the main library supporting research and development at the Sherbrooke Connectivity Imaging Lab (SCIL).

Scilpy mainly comprises tools and utilities to quickly work with diffusion MRI. Most of the tools are based on or are wrappers of the DIPY library, and most of them will eventually be migrated to DIPY. Those tools implement the recommended workflows and parameters used in the lab.

:warning: We highly suggest to install uv to speedup scilpy installation: https://docs.astral.sh/uv/getting-started/installation/

:point_up: BUT, if you don't want to use uv, scilpy can still be installed by omitting the uv from all the installation command lines below.

Make sure your pip is up-to-date before trying to install:

uv pip install --upgrade pip

The library's structure is mostly aligned on that of DIPY.

We highly encourage to install scilpy in a virtual environnement. Once done and you're in your virtual environnement, the library and scripts can be installed locally by running these commands:

Install scilpy as a user

# If you are using Python3.11, export this variable before installing
export SETUPTOOLS_USE_DISTUTILS=stdlib

uv pip install scilpy # For the most recent release from PyPi

Install scilpy as a developer

# If you are using Python3.11, export this variable before installing
export SETUPTOOLS_USE_DISTUTILS=stdlib

uv pip install -e . # Install from source code (for development)

EXTRAS

On Linux, most likely you will have to install libraries for COMMIT/AMICO

sudo apt install libblas-dev liblapack-dev

While on MacOS you will have to use (most likely)

brew install openblas lapack

On Ubuntu >=20.04, you will have to install libraries for matplotlib

sudo apt install libfreetype6-dev

Note that using this technique will make it harder to remove the scripts when changing versions. We highly recommend working in a Python Virtual Environment.

Scilpy documentation is available: https://scilpy.readthedocs.io/en/latest/