QC curation loop

The QC tab in the Results Viewer composes one Plotly card per configured QualityCheck analyzer. Each card subscribes to the same STORE_REMOVED_KEYS store that backs the “remove colony” toggle, so every time you curate a flagged colony the card’s figure, summary strip, and status badge re-render automatically — no manual refresh, no full page reload.

The loop:

1. Configure one or more checks (e.g. ExpectedVsDetectedCount, GridOccupancy, ReplicateAgreement).

2. The card highlights every flagged group with its severity.

3. Mark all flagged colonies for removal (per-card button) or curate individuals from the Plate / Colony tabs.

4. The card re-renders; remaining severity drops; status badge transitions fail → warn → pass.

Prerequisites

• A finished CLI run with deliverables/master_measurements.parquet + the post-applied deliverables/measurements.parquet mirror under <output>/deliverables/measurements.parquet. See Run Locally to produce one.

• A metadata.csv describing the expected plate layout if you plan to configure ExpectedVsDetectedCount or GridOccupancy (their groupby columns must resolve against the master measurements schema). Both read the layout’s per-group row count as the expected colony/cell count; GridOccupancy additionally counts distinct filled grid cells (Grid_RowMajorIdx), so it reports occupancy without double-counting doublets.

Walkthrough

Open the Viewer tab in the hub and pick the QC sub-tab:

The empty state shows a placeholder explaining that no checks are configured yet. The top strip carries:

• + Add check — opens a modal listing every concrete QualityCheck subclass discovered by OperationRegistry.

• Export QC report — disabled until at least one check exists; emits <output>/qc.parquet + <output>/qc_summary.json keyed by QC_Check_Class + QC_Check_Instance_Id so downstream notebooks can pivot per check.

Click + Add check. The modal opens with a class dropdown plus an inline param form that adapts to the chosen check’s constructor signature:

Pick ExpectedVsDetectedCount, fill in the path to your plate-layout metadata.csv, then click Save. A new card slides into the cards container with:

• a Plotly figure showing per-group Delta (expected − detected),

• a summary strip reading groups: N | flagged: K | max severity: X.YZ,

• a status badge coloured by the worst per-group status (green / yellow / red),

• per-card buttons: Edit, Duplicate, Toggle enabled, Delete, and Mark all flagged for removal.

Switch to the Plate tab and remove a flagged colony (or hit the per-card Mark all flagged for removal button to union every flagged (ImageFile, Object_Label) into STORE_REMOVED_KEYS in one click). Switching back to the QC tab shows the card re-rendered with the new severity.

Common gotchas

• Metadata schema: ExpectedVsDetectedCount reads Metadata_ImageFile + Object_Label from the supplied CSV. If the CSV has a different column name (e.g. legacy ImageName), the check raises at instantiation time and the recipe records a load-warning instead of breaking the boot. The warning banner at the top of the QC tab lists the affected instance_id and the underlying error.

• QC recipe: every add / edit / delete writes the qc array in the pipeline config under <output>/deliverables/ (pipeline.json.pht-pipe on current runs). A legacy <output>/.viewer_cache/qc_recipe.json sidecar is migrated once at viewer startup. Concurrent viewer sessions on the same output dir are still unsupported; if you reopen the viewer on a different machine, the recipe travels with the output directory.

• Severity legend: check-side severity_warn / severity_fail thresholds default to 0.05 / 0.10. Tune them on the per-check edit modal to match your QC tolerance.

Where to next

• Heatmap exploration — pivot the same measurements into a plate-shaped heatmap and watch edge / contamination patterns light up.

• View Results — the curation primitives the QC tab piggybacks on.

• Analysis — once the QC chain is happy, configure filters + an endpoint model and emit analysis.{csv,parquet}.