Example Workflows

VIPP 0.15.0a5 contains 22 registered example workflows under:

examples/

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

New in 0.15.0a5: RACC Colocalization focuses on the RACC method; the new Colocalization, Overlap & Object Counts contains the general overlays, metrics and overlap-region counting workflow. See which example to choose.

Improved in 0.15.0a5: the full catalogue has been reviewed for clearer left-to-right flow, separate analysis branches, and readable notes and tunnel labels. Spacing also allows for the controls shown after calculation. The examples keep their existing analysis settings and coverage, including the recent RACC example settings. Reopen an example from the chooser to use its revised layout; your saved copies are not rearranged automatically.

Catalog updates in 0.15.0a3

VIPP 0.15.0a2 shipped 21 examples. 0.15.0a3 retains the saved interactive mesh workflow as Mesh Objects, Colours & Refinement and removes the duplicate entry. It also adds Separate Overlapping Objects, combining segmentation, binary logic and meshes. The inventory below reflects both changes.

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.

New in 0.15.0a5: Every example has a description, short plain-language What to explore and What you'll get bullet lists, and a Try this suggestion. Developer & testing workflows, at the bottom of the list, groups the five interface-checking examples separately from the analysis tutorials. Search also finds their descriptions when the group is collapsed. The RACC entry explains the method and offers Read the paper, which opens the original publication in your browser only when clicked.

Choose Open example to create a new workflow tab. Open batch demo... instead asks for a working folder before opening the configured batch workspace.

Workflow Index

Workflow	Input sample	Purpose
exhaustive-inspector-showcase.json	seven synthetic-data lanes including the threshold gallery and label-boundary QC	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-separate-overlapping-objects.json	synthetic volume	Separate overlapping objects: compare intensity masks, reconstruct a curve with XOR/OR and morphology, then create, measure and colour two meshes. Six notes explain the saved sample-specific choices.
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-mesh-objects.json	synthetic 3D mesh morphology	Saved interactive workflow with five split objects, Turbo colours by triangle count, two smoothing iterations at strength 1, and a target of 10% of triangles kept at aggressiveness 4. These are sample-specific choices; compare geometry and measurements before reuse.
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	New in 0.15.0a5: focused whole-image and ROI-masked RACC, with tuned manual thresholds, Magma output, method notes and paper citation.
synthetic-colocalization-overlap.json	synthetic colocalization	New in 0.15.0a5: whole-image and ROI-masked overlays/metrics, Boolean overlap masks, 3D cleanup and connected-region measurements.
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 your version's exact IDs. The table lists the 0.15.0a5 IDs:

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
separate-overlapping-objects	Separate Overlapping Objects
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
mesh-objects	Mesh Objects, Colours & Refinement
skeleton-qc	Skeleton QC
advanced-skeleton	Advanced Skeleton Network
racc-colocalization	RACC Colocalization
colocalization-overlap	Colocalization, Overlap & Object Counts (New in 0.15.0a5)
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.