draft: Zarr vectors pointcloud#995
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# Conflicts: # src/skeleton/frontend.ts # src/ui/spatial_skeleton_edit_tool.ts
fix: transform points between model space and global space
# Conflicts: # src/skeleton/frontend.ts # src/ui/spatial_skeleton_edit_tool.ts
…if no previous entry
Fix: Inputting a non-existent ID and then going to skeleton tab fills screen with errors
…into feature/edit-mode
fix: correct isReady check on spatial skeleton layers
feat: add saturation support and seg higlight to spatial skeleton render
…ring fix: remove skeleton double rendering in browse pass
…-logic feat: add python state, remove uneeded functions, convert node type to enum
…nds_code refactor: Deduplicate catmaid spatially indexed skeletons code
chore: Add no-op imports for catmaid
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feat: debounce node radius changes in selection details
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I have been developing a proposal for a zarr based storage mechanism for Nd vector data (zarr-vectors) and made a draft specification, repo here. You can leave comments directly on the page by highlighting content and using (https://hypothes.is/) or open issues/PRs on the repo.
@Andrew-Keenlyside took this and started a python library for writing/reading it . So as part of that learning process I tried to vibe code a data source that would start reading this format into neuroglancer in order to prove out that the access patterns would work for visualization
I'm opening this PR not because I think the format is ready, but in order to spark conversation within this community about whether this works for visualization purposes, and to be able to have a demo location for folks to try out examples that utilize the draft format.
When the PR preview is done I'll share a link here that has an example dataset of a medium sized point cloud of cells from the mouse brain with some cluster annotations on them.
gs://allen_neuroglancer_ccf/zarrvectors/MERFISH-C57BL6J-638850-CCF5.zarrvectors/|zarr-vectors: