NiPreps Book is a Jupyter Book that accompanies the NiPreps community workshops. The material walks readers through implementing a head-motion–correction workflow for diffusion MRI (dMRI) data using tools from DIPY, NiTransforms, and the broader NiPreps ecosystem. Besides explaining the motivation behind the "analysis-grade" data philosophy, the book provides hands-on notebooks that show how to assemble reproducible preprocessing components.
If you prefer not to install anything locally, launch the notebooks in an ephemeral Binder session:
Binder sessions can take a few minutes to start and reset after inactivity.
This repository is configured with pixi for reproducible environments:
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Create the environment and install the dependencies:
pixi install
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Build the static HTML version of the book:
pixi run build-book
The rendered site will be available under docs/_build/html. To explore the
notebooks interactively, launch JupyterLab with pixi run serve and open the
files inside docs/notebook/.
Several chapters rely on a small diffusion MRI dataset. The helper module
tutorial_data.py automatically downloads the dataset to data/ the first time
it is requested. If you already have a copy of the file, set the
NIPREPS_TUTORIAL_DATA environment variable to point to it before running the
notebooks.
docs/– Markdown content, notebook sources, and static assets for the book.data/– Cached example diffusion MRI dataset used throughout the tutorial.binder/– Configuration used to build the Binder environment.pixi.toml– Environment definition and common development tasks.tutorial_data.py– Utility functions for fetching the tutorial dataset.
Issues, feature requests, and pull requests are welcome! Please review the
open issues on GitHub and let us know how the material can be improved. When
submitting changes, follow the standard GitHub workflow and ensure the book
builds locally (pixi run build-book) before opening a pull request.
If you use the material in your own work, please cite the project as described in CITATION.cff.