---

name: dipy description: "Use Dipy for diffusion MRI IO, preprocessing, reconstruction, tractography, registration, visualization-aware workflows, and command-line operations." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Dipy Repo Skill

Use this skill when a task involves Dipy, DIPY, diffusion MRI, diffusion-weighted imaging, tractography, reconstruction models, b-values/b-vectors, NIfTI DWI data, streamline tractograms, medical-image registration, Dipy dipy_* commands, or debugging Dipy installation/runtime behavior.

Dipy is a research-focused Python package for diffusion MRI imaging workflows. It is not a clinical-deployment guide; keep user guidance framed as research/analysis support.

Start Here

1. Identify whether the task is about files, preprocessing, reconstruction, tractography/segmentation, registration/alignment, or command-line workflow mechanics.
2. Read the matching sub-skill below before answering detailed API or CLI questions.
3. Run python scripts/check_dipy_install.py when the environment is uncertain or the user reports missing commands/imports.
4. Prefer the bundled smoke scripts for tiny deterministic validation before running user data or expensive workflows.
5. Treat network fetches, GUI visualization, neural-network helpers, and whole-brain tracking/registration as optional or potentially expensive unless the user explicitly authorizes them.

Route By Task

• sub-skills/io-data/: NIfTI image IO, bvals/bvecs, GradientTable, StatefulTractogram, tractogram/PAM formats, dataset listing/fetching, and IO conversion commands.
• sub-skills/denoising-preprocessing/: NLMeans, local PCA, MPPCA, Patch2Self, Gibbs ringing removal, noise estimation, DWI bias correction, and pre-fit validation.
• sub-skills/reconstruction-models/: DTI, DKI, CSD/MSMT-CSD, CSA/QBall/OPDT, DSI/DSID, GQI, MAPMRI, SFM, FORECAST, FWDTI, FORCE, RUMBA API surfaces, ODFs, scalar maps, peaks, and reconstruction CLIs.
• sub-skills/tracking-segmentation/: tractography, seeds, direction getters, stopping criteria, streamline operations, QuickBundles, RecoBundles, LabelsBundles, brain masks, tissue classification, AFQ, BUAN, and tractometry handoffs.
• sub-skills/registration-alignment/: reslicing, affine/SyN registration, transform application, motion correction, streamline linear registration, BundleWarp, and registration CLIs.
• sub-skills/cli-workflows/: dipy_* entry-point discovery, parser behavior, --help probes, command families, output naming, and translation between APIs and CLI workflows.

Cross-Cutting References

• references/troubleshooting.md: install/import, source-checkout shadowing, optional dependency, data validation, CLI, and workflow safety issues shared across sub-skills.
• references/repo-provenance.md: generation baseline, source commit, package version, dirty-state summary, and relative evidence paths.
• references/repo-routing-metadata.json: structured import metadata consumed by the managed repo-skills-router.
• scripts/check_dipy_install.py: safe installed-package and CLI-flow probe with JSON/text output.

Safe Validation Commands

Run these from the root of this skill directory, or adapt paths to wherever the skill is installed:

python scripts/check_dipy_install.py --format text
python sub-skills/io-data/scripts/dipy_io_probe.py --check-imports --check-signatures --tiny-gradient
python sub-skills/reconstruction-models/scripts/dipy_tensor_smoke.py --json
python sub-skills/denoising-preprocessing/scripts/dipy_denoise_smoke.py --json
python sub-skills/registration-alignment/scripts/dipy_reslice_smoke.py
python sub-skills/tracking-segmentation/scripts/dipy_streamline_smoke.py
python sub-skills/cli-workflows/scripts/dipy_cli_probe.py --format text

These probes use synthetic or import-only checks. They do not fetch datasets, train models, open GUIs, or require the original source repository.

Environment Expectations

Install Dipy with a public package manager before using the runtime checks:

pip install dipy
# or
conda install -c conda-forge dipy

• Base Dipy workflows require Python plus Dipy's core runtime dependencies such as NumPy, SciPy, nibabel, h5py, tqdm, packaging, and tractogram support dependencies.
• Visualization workflows can require FURY, matplotlib, display/OpenGL support, or headless rendering setup.
• Neural-network workflows such as EVAC+ or neural bias correction can require optional ML packages and model assets.
• Patch2Self can require scikit-learn-compatible estimator support depending on the selected model.
• CLI entry points must come from the same installed environment as the imported Dipy package; use sub-skills/cli-workflows/ when shell commands and Python imports disagree.

Common Routing Patterns

• Data-to-FA map: io-data to load NIfTI and gradients, denoising-preprocessing if cleanup is needed, reconstruction-models for TensorModel, then io-data to save outputs.
• Data-to-tractography: io-data for DWI/gradients, denoising-preprocessing as needed, reconstruction-models for peaks/PAM, tracking-segmentation for seeds/stopping/streamlines, then io-data for tractogram saving.
• Registration before analysis: io-data for files/headers, registration-alignment for reslice/affine/SyN/motion correction, then return to the scientific owner.
• Command-line request: cli-workflows for command discovery/parser/output mechanics, then the scientific sub-skill for parameter meaning and validation.

Safety And Scope

• Do not run Dipy dataset fetchers, long registration, whole-brain tracking, large model fits, GUI visualization, or neural-network model downloads without user intent.
• Use explicit output directories and avoid overwriting user data unless requested.
• Validate DWI shape, bvals/bvecs length, masks, affine/reference metadata, and optional dependencies before expensive work.
• If an import works only inside a source checkout or fails because generated version metadata is missing, prefer a normal installed package environment and use cli-workflows troubleshooting.