{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"spatial-image provides an N-dimensional spatial image data structure built on xarray that carries pixel values alongside spatial metadata\u2014origin, spacing, and dimension labels\u2014through image processing pipelines.","skillfed_tags":["image-processing","scientific-computing","xarray-ecosystem"],"use_cases":["Medical imaging: carry voxel spacing and anatomical axis labels through registration and resampling workflows.","Multi-scale image analysis: track origin and spacing metadata as images are processed at different resolutions.","Subregion parallel processing: slice spatial images with xarray and maintain valid coordinates for each chunk.","Image-to-mesh coupling: preserve spatial coordinates when linking pixel data to mesh or point-cloud annotations.","Distributed image processing: pass spatial images to Dask-based pipelines without losing spatial metadata."],"what_it_does":"spatial-image wraps xarray.DataArray to create a standardized data structure for scientific images that are typically multi-dimensional and anisotropic. It enforces a consistent schema: dimensions from the set {c, x, y, z, t}, uniform spacing per axis, and spatial metadata (origin, units, axis names) stored as coordinates and attributes. This allows pixel values and their spatial context to move together through processing pipelines without manual bookkeeping.\n\nThe package is designed for workflows involving registration, resampling, multi-scale analysis, and coupling with meshes or annotations. It integrates with the broader scientific Python ecosystem through numpy, xarray, and related tools, so that standard slicing operations preserve both data and metadata correctly.","worth_installing":"Yes, if you work with multi-dimensional scientific images and need spatial metadata (origin, spacing, axis labels) to persist through processing pipelines. The low install friction and permissive license make adoption straightforward. However, the aging maintenance status (371 days since last release) means you should accept slower bug fixes and check that the package's current dependencies remain compatible with your environment before committing to it for new projects."},"id":"spatial-image","links":{"html":"https://skillfed.io/packages/spatial-image","md":"https://skillfed.io/packages/spatial-image.md","pypi":"https://pypi.org/project/spatial-image/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-08-08","license_spdx":null,"license_treatment":"permissive","name":"spatial_image","python_support":"supports_current","summary":"A multi-dimensional spatial image data structure for scientific Python."},"popularity":{"monthly_downloads":93920,"position":13358,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.2.3"}