Tools for neuroglancer scenes.
Project description
NGSidekick
Tools for working with neuroglancer. See docs.
Feature Highlights
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Segment properties: read and write neuroglancer's precomputed segment properties format from a pandas DataFrame.
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Local annotations: construct local annotations directly in viewer state. (API subject to change.)
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Precomputed annotations: export annotations in neuroglancer's precomputed annotations format from a pandas DataFrame.
- Supports all five annotation types:
pointlineaxis_aligned_bounding_boxellipsoidpolyline
- Per-annotation properties (numeric, enum/categorical, and rgb/rgba color).
- Per-annotation relationships (lists of related segment IDs, used by neuroglancer to filter annotations by segment).
- Written to all "index" types (annotation id, related segment, and multi-level spatial grid)
- Sharded output, written in parallel via tensorstore.
- Note: writing directly to cloud storage is not yet supported; outputs must be written to a local filesystem (you must upload to cloud storage afterwards).
- Supports all five annotation types:
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Installation
Packages are available from both PyPI and conda-forge.
Using pixi:
pixi add ngsidekick
Using conda:
conda install -c conda-forge ngsidekick
Using pip:
pip install ngsidekick
Using uv:
uv add ngsidekick
# or in an existing environment
uv pip install ngsidekick
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