spatial_image
A multi-dimensional spatial image data structure for scientific Python.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low install friction; pure Python wheel.
- Maintenance status is aging—last release 371 days ago—so expect slower bug fixes and feature updates, though the package remains functional for current Python versions.
License · maintenance · safety
permissive license (permissive) — MIT license (permissive); you may use, modify, and distribute this package freely in commercial or private projects with minimal restrictions.
last release 2025-08-08 (371 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 93,920 downloads/mo, #13,358 on PyPI
Alternatives
Verify before relying
pip install spatial-image
import numpy as np
from spatial_image import to_spatial_image
array = np.random.rand(10, 10, 10)
img = to_spatial_image(array)- Whether the package is actively maintained or in maintenance-only mode given the 371-day gap since last release.
- Performance characteristics when working with very large multi-dimensional arrays or distributed computing via Dask.
- Compatibility with specific downstream libraries beyond what the description claims.
What it is and 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
spatial-image on PyPI
Before you install
Low install friction; pure Python wheel. Maintenance status is aging—last release 371 days ago—so expect slower bug fixes and feature updates, though the package remains functional for current Python versions.
Requires Python 3.10 or later.
License in practice
MIT license (permissive); you may use, modify, and distribute this package freely in commercial or private projects with minimal restrictions.
Quickstart
pip install spatial-image
import numpy as np
from spatial_image import to_spatial_image
array = np.random.rand(10, 10, 10)
img = to_spatial_image(array)Verify before relying
- Whether the package is actively maintained or in maintenance-only mode given the 371-day gap since last release.
- Performance characteristics when working with very large multi-dimensional arrays or distributed computing via Dask.
- Compatibility with specific downstream libraries beyond what the description claims.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumpyxarray-dataclassxarray |
| Maintenance | Aging 371 days since the last release |
| First released | |
| Downloads | 93,920 / month, #13,358 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: spatial_image-1.2.3-py3-none-any.whl
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