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Feature: example linear registration pipeline #116
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Pull request overview
This PR implements a feature to support linear registration workflows with custom clipping behavior for annotation layers across different coordinate spaces. The implementation adds a clipDimensionsWeight property that allows fine-grained control over which dimensions should be clipped during annotation rendering.
Changes:
- Added
clipDimensionsWeighttracking and state management across viewer components - Implemented Python bindings for
clip_dimensions_weightin annotation layers and viewer state - Created a comprehensive example script demonstrating interactive linear registration using point annotations
Reviewed changes
Copilot reviewed 9 out of 9 changed files in this pull request and generated 6 comments.
Show a summary per file
| File | Description |
|---|---|
| src/layer_groups_layout.ts | Added clipDimensionsWeight to common viewer state |
| src/layer_group_viewer.ts | Added clipDimensionsWeight property and type import |
| src/layer/annotation/index.ts | Added JSON key constant and state management for clipDimensionsWeight |
| src/data_panel_layout.ts | Added clipDimensionsWeight to ViewerUIState interface and common state |
| src/annotation/renderlayer.ts | Implemented clip dimension weight application in annotation projection parameters |
| src/annotation/annotation_layer_state.ts | Created TrackableClipDimensionsWeight class for state management |
| python/neuroglancer/viewer_state.py | Added Python bindings for clip_dimensions_weight and LinkedDisplayDimensions |
| python/neuroglancer/init.py | Exported LinkedDisplayDimensions |
| python/examples/example_linear_registration.py | Added comprehensive linear registration workflow example |
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| wireFrame: viewer.wireFrame, | ||
| enableAdaptiveDownsampling: viewer.enableAdaptiveDownsampling, | ||
| visibleLayerRoles: viewer.visibleLayerRoles, | ||
| clipDimensionsWeight: viewer.clipDimensionsWeight, |
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not sure this needed on both the data panel layout and layer group viewer
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