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name: antspy description: "Use ANTsPy/antspyx for medical image IO, ANTsImage metadata, image operations, registration, segmentation, visualization, and learning helper workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

ANTsPy Repo Skill

Use this repo skill when a task involves the Python package distributed as antspyx and imported as ants: medical image IO, ANTsImage objects, preprocessing, registration, transforms, segmentation, labels, plotting, interoperability, or ANTsPy learning/deeplearn helper utilities.

Start Here

1. Install the package with pip install antspyx or conda install conda-forge::antspyx.
2. Verify the public import with python -c "import ants; print(ants.__name__)".
3. Run scripts/antspy_environment_check.py for a tiny import/API smoke check.
4. Read references/package-overview.md for package scope, compiled-backend caveats, and workflow boundaries.
5. Read references/troubleshooting.md when installation, import, compiled libraries, physical-space metadata, optional dependencies, or expensive algorithms fail.
6. Check references/repo-provenance.md before deciding whether this skill is stale for a newer ANTsPy checkout.

Route by Task

User task	Read
Create, read, write, clone, index, compare, or validate ants.ANTsImage objects and physical metadata	image-core
Smooth, denoise, mask, crop, pad, slice, resample, threshold, run iMath, compute image metrics, or use morphology	image-ops-math
Register images, apply transforms to images or points, inspect transform files, build templates, run motion correction, or compute Jacobians	registration-transforms
Run Atropos, k-means, Otsu, priors, joint label fusion, label stats, overlap, geometry, label matrices, centroids, or point images	segmentation-labels
Save plots headlessly, build overlays/grids/movies, handle RGB/vector channels, convert image lists/matrices, or interoperate with nibabel/SimpleITK	visualization-interop
Use sparse/eigen learning helpers, patch extraction/reconstruction, one-hot labels, random augmentation, simulated bias fields, or learning-oriented crop/pad helpers	learning-deeplearn

Cross-Cutting Rules

• Import with import ants; do not import antspyx as a module.
• ANTsPy wraps compiled ANTs/ITK code. A successful pip install is not enough if importing ants fails; run the environment check script before debugging workflow code.
• ANTsImage values and physical metadata are both important. Validate shape, dimension, origin, spacing, and direction before mixing images, masks, labels, transforms, overlays, or matrices.
• Use label-safe interpolation such as nearestNeighbor or genericLabel for labels and masks. Use linear-style interpolation for intensity images unless the workflow requires otherwise.
• Prefer small synthetic images for smoke checks and troubleshooting. Full nonlinear registration, joint label fusion, high-resolution 3-D operations, and template building can be expensive.
• Keep ANTsPyNet, neural-network model training, and application-specific medical pipelines outside this skill unless the task only uses ANTsPy helper utilities.

Shared References and Scripts

• Package overview: package identity, public workflow map, data model, optional dependencies, and version caveats.
• API index: quick map from common ants.* functions to the owning sub-skill and detailed reference.
• Troubleshooting: install/import failures, wheel/source-build problems, shadowed imports, physical-space errors, optional dependencies, and expensive workflow triage.
• Repo provenance: source commit, dirty-state baseline, package versions, and evidence paths used for this generated skill.
• Routing metadata: structured scenario metadata consumed by the managed repo-skills-router during import.
• Environment check: tiny import/API smoke check for antspyx.
• Smoke suite: runs the bundled sub-skill smoke scripts with the current Python interpreter.