multiscale-spatial-image
Generate a multiscale, chunked, multi-dimensional spatial image data structure that can be serialized to OME-NGFF.
Decision gist · record as of 2026-08-14
Yes, if you work with multidimensional spatial imaging data and need to generate and store multiscale pyramids in a standard format. The package has low install friction, permissive licensing, and no known vulnerabilities. Maintenance is aging but not abandoned; the API is explicitly pre-1.0 and subject to change, so pin versions carefully in production. Best suited for scientific and research use cases rather than production services.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later; spatial-image dependency must be installed first to create the base spatial image object.
- Low friction: pure Python wheel with no compiled dependencies.
- Maintenance status is aging—274 days since last release—but the repository remains active with recent commits and no archived status.
License · maintenance · safety
permissive license (permissive) — Apache 2.0 permissive license allows commercial and derivative use with minimal restrictions; attribution and license notice required in distributions.
last release 2025-11-13 (274 days) · last repo commit 2025-11-13 · 57 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 95,614 downloads/mo, #13,260 on PyPI
Alternatives
Verify before relying
pip install multiscale_spatial_image
import numpy as np
from spatial_image import to_spatial_image
from multiscale_spatial_image import to_multiscale
array = np.random.randint(0, 256, size=(128, 128), dtype=np.uint8)
image = to_spatial_image(array)
multiscale = to_multiscale(image, [2, 4])- Performance characteristics and memory efficiency when handling very large image datasets
- Supported downsampling methods beyond the examples shown (coarsen, resampling strategies)
- Compatibility with specific OME-NGFF versions and zarr store configurations
What it is and what it does
multiscale-spatial-image builds multiscale image pyramids—progressively downsampled versions of an image—and organizes them into an Xarray Datatree structure where each scale level is a separate node. Each scale is a chunked Dask array, enabling lazy evaluation and out-of-core processing. The package preserves spatial metadata (coordinates, dimensions) across all scales and can serialize the entire pyramid to OME-NGFF (Open Microscopy Environment Next Generation File Format) via Zarr, making it suitable for scientific imaging workflows that need efficient storage and access to multiscale data.
The package is built on top of spatial-image, xarray, and Dask, and provides convenience methods like transpose, reindex, and assign_coords that operate across all scales while skipping non-dimensional nodes. It targets scientific Python users working with microscopy, medical imaging, or other multidimensional spatial data who need to generate and store image hierarchies in a standard, interoperable format.
Use it for
- Generate multiscale pyramids from microscopy images for efficient zoom-and-pan visualization in web or desktop viewers
- Prepare large volumetric datasets for hierarchical processing pipelines that operate at different resolutions
- Convert single-scale images to OME-NGFF Zarr stores for archival and sharing with other imaging software
- Apply transformations (transpose, reindex) uniformly across all scales of a multiscale dataset
- Build image analysis workflows that downsample data for preview or coarse-to-fine processing strategies
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with multidimensional spatial imaging data and need to generate and store multiscale pyramids in a standard format.
The package has low install friction, permissive licensing, and no known vulnerabilities. Maintenance is aging but not abandoned; the API is explicitly pre-1.0 and subject to change, so pin versions carefully in production. Best suited for scientific and research use cases rather than production services.
Install
multiscale-spatial-image on PyPI
Before you install
Low friction: pure Python wheel with no compiled dependencies. Maintenance status is aging—274 days since last release—but the repository remains active with recent commits and no archived status.
Requires Python 3.11 or later; spatial-image dependency must be installed first to create the base spatial image object.
License in practice
Apache 2.0 permissive license allows commercial and derivative use with minimal restrictions; attribution and license notice required in distributions.
Quickstart
pip install multiscale_spatial_image
import numpy as np
from spatial_image import to_spatial_image
from multiscale_spatial_image import to_multiscale
array = np.random.randint(0, 256, size=(128, 128), dtype=np.uint8)
image = to_spatial_image(array)
multiscale = to_multiscale(image, [2, 4])Verify before relying
- Performance characteristics and memory efficiency when handling very large image datasets
- Supported downsampling methods beyond the examples shown (coarsen, resampling strategies)
- Compatibility with specific OME-NGFF versions and zarr store configurations
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesdaskngff-zarrnumpypython-dateutilspatial-imagexarray-dataclassxarrayzarr |
| Maintenance | Aging 274 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 95,614 / month, #13,260 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 :: Apache Software LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: multiscale_spatial_image-2.1.0-py3-none-any.whl
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See also ome-zarr · spatial_image · tiled · spatialdata · odc-loader · xarray · tensorstore · xarray-dataclass · odc-stac