zarr-vectors stores three-dimensional spatial geometry — point clouds, streamlines, graphs, skeletons, and meshes — in spatially indexed Zarr v3 stores. Spatial queries touch only the chunks they need, whether the store sits on a local filesystem or a cloud object store (S3, GCS). Resolution pyramids are encoded natively so viewers like Neuroglancer can stream data progressively at any scale.
The library implements the Zarr Vector Format originally specified by Forest Collman at the Allen Institute for Brain Sciences, extended with separated chunk/bin sizes, per-level sparsity, and OME-Zarr-compatible multiscale metadata.
Where to start¶
Write and query your first vector store in a few lines of Python. |
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The mental model: chunk shapes, bin shapes, and resolution pyramids. |
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Full technical specification for the Zarr Vector Format. |
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Auto-generated reference for all public functions and classes. |