Example Workflows

The release contains 19 example workflows under:

examples/

They are intended for regression tests, screenshots, tutorials, and manual review.

In VIPP, open them with:

Gear menu → Open example…

The chooser groups workflows by task and opens each template with its bundled sample Image Source nodes already configured. Use Open for custom or external workflow JSON files.

Workflow Index

Workflow	Input sample	Purpose
exhaustive-inspector-showcase.json	seven synthetic-data lanes including the threshold gallery	Comprehensive manual review of every palette operation, connected-input summaries, scientific controls, and result sections. Use a focused tutorial for a first workflow; this is a broad inspector acceptance example.
graph-authoring-acceptance.json	synthetic object morphology	Numbered canvas notes for tunnel insertion, value transfer, graph-fragment copy/paste, group movement, one-step undo/redo, and a qualified GPU dtype repair. Its deliberately loose demonstration fragments are not calculated.
responsive-volume-crop-acceptance.json	synthetic time-lapse multichannel	Numbered TCZYX checks for explicit-Z crop margins, immediate 2D/3D ROI feedback, one committed calculation and undo gesture, preserved T/C and physical origins, QYX safety, and truthful CPU/GPU status.
safe-node-bypass-acceptance.json	synthetic volume	Focused Crop Stack checks for exact pass-through data, would-run thumbnails, bypass styling, undo/save/export, GPU-neutral status, and batch Run/Bypass profiles.
general-node-bypass-acceptance.json	synthetic deconvolution image plus measured PSF	Generalized unary and multi-input bypass checks, including RL-TV forwarding Image port 0 while retaining but ignoring its PSF input and refusing unsafe boundaries.
synthetic-batch-provenance.json	generated two-source NumPy collection	Three paired items, explicit NPY/TIFF/TSV outputs, representative navigation, saved config/runner, exact ground truth, manifests, archives, and item sidecars. Open it through the chooser and create a writable working copy.
otsu-red-channel-labels.json	synthetic multichannel volume	Label cleanup: split the red/TRITC-like channel, blur, Otsu threshold, mask cleanup, connected components, border clearing, and volume filtering.
synthetic-gpu-segmentation-bridge.json	synthetic GPU segmentation cleanup	Annotated portable path through Extract Channel, exact float32 Preserve conversion, Gaussian Blur, fixed Binary Threshold, Boolean Remove Small Objects and Fill Holes, and 3D Connected Components. Unsupported GPU regions fall back visibly to CPU.
red-channel-object-intensity-measurements.json	synthetic multichannel volume	Multi-input object measurement using labels plus matching intensity image.
red-channel-merged-measurement-table.json	synthetic multichannel volume	Morphology, intensity, table merge, and metadata columns.
synthetic-measurement-summary.json	synthetic measurement summary	Grouped object-count and area summaries.
synthetic-derived-object-morphology.json	synthetic object morphology	Derived 2D morphology, circularity, perimeter/area ratio, Hu moments, and column selection.
synthetic-3d-mesh-morphology.json	synthetic 3D mesh morphology	Surface area, mesh volume, convex hull, sphericity, and tiny-object status.
synthetic-skeleton-qc.json	synthetic skeleton network	Skeleton keypoints, component/branch labels, pruning, branch tables, graph tables, and network summaries.
synthetic-advanced-skeleton-network.json	synthetic advanced skeleton network	Time-indexed 3D skeleton graph stress test.
synthetic-colocalization-racc.json	synthetic colocalization	Pixel and ROI-masked colocalization, scatter thresholds, colocalized voxels, and RACC-like output.
synthetic-object-colocalization-association.json	synthetic colocalization	Object colocalization, label overlap, nearest distance, event localization, and merged tables.
synthetic-deconvolution-rl-tv.json	2D deconvolution image plus measured PSF	2D measured-PSF restoration with ordinary RL and RL-TV.
synthetic-3d-deconvolution-rl-tv.json	3D deconvolution volume plus 3D measured PSF	Volumetric PSF-aware restoration with one shared visible float32 Preserve conversion feeding both 25-iteration branches at the authored 1e-12 filter epsilon.

Launcher Names

Use:

python scripts\launch_vipp_intensity_workflow.py <name>

Use --list to print the release's exact IDs. In 0.15.0a1 they are:

ID	Example title
exhaustive-inspector	Exhaustive Inspector Showcase
graph-authoring	Graph Editing Acceptance Check
responsive-crop	Responsive Volumetric Crop Acceptance
safe-node-bypass	Safe Node Bypass Acceptance
general-node-bypass	General Node Bypass Acceptance
batch-provenance	Deterministic Batch & Provenance
label-cleanup	Red-Channel Label Cleanup
gpu-segmentation	Portable GPU Segmentation Bridge
object-intensity	Object Intensity Measurements
merged-measurements	Merged Measurement Table
summary-table	Grouped Measurement Summary
derived-morphology	Derived 2D Object Morphology
mesh-morphology	3D Mesh Morphology
skeleton-qc	Skeleton QC
advanced-skeleton	Advanced Skeleton Network
racc-colocalization	RACC Colocalization
object-colocalization	Object Colocalization Association
deconvolution-2d	2D Richardson-Lucy / TV Deconvolution
deconvolution-3d	3D Richardson-Lucy / TV Deconvolution

Legacy launcher aliases such as intensity, merged, and mesh remain for maintainers, but documentation should use the canonical IDs above. An unknown ID is an error; it does not silently open another example.

Adding A Core Example Workflow

When adding a new core example:

1. Add a deterministic bundled sample or document the input source.
2. Save workflow JSON under examples/.
3. Add a row to this page and the repository example README.
4. Add a launcher shortcut if it is used often.
5. Add a focused test that checks the expected output type and one meaningful invariant.