QC review walkthrough

The QC tab in the Results Viewer has two modes, switched by a Configure | Review segmented toggle:

• Configure — the per-check card editor (see QC curation loop). Adding, editing, or deleting a check now writes the qc array of the output’s deliverables/pipeline.json (a legacy .viewer_cache/qc_recipe.json sidecar is folded in once on first open).

• Review — a master–detail walkthrough that walks the worst-agreeing groups for one QC module, lets you curate the offending colonies in a tile gallery, and recomputes the module in place after each curated group.

The review loop:

1. Pick a module (one configured check) from the toolbar.

2. The worklist lists that module’s groups sorted worst-first.

3. Open a group; its detail gallery shows every member colony as a tile (one row per timepoint for time-course checks).

4. Curate — remove the bad colonies (per tile or multi-select bulk).

5. Mark reviewed (or Next). If you changed anything, the module recomputes on the curated data and the group’s metric updates in place with a before → after delta.

Prerequisites

• A finished CLI run whose deliverables/pipeline.json carries at least one QC check, so the CLI wrote the qc/ artifact (qc/qc_summary.parquet, qc/qc_members.parquet, qc/qc_config.json — these stay at the output-dir root). Configure a check in the Configure sub-view, then re-run python -m phenotypic --mode recompile --output <output> (or --mode measure --pipeline <pipeline.json> --output <output>) to compute it. See Run Locally.

• The post-applied deliverables/measurements.parquet mirror under <output>/deliverables/measurements.parquet — the recompute reads this frame (it carries any PostMeasurement columns and joined metadata) and anti-joins your removals, so the in-session metric matches the CLI’s.

Walkthrough

Open the Viewer tab in the hub and pick the QC sub-tab. Before an output root is bound the tab shows its empty state:

With an output root that carries a qc/ artifact, flip the toggle to Review. The toolbar exposes the module picker, the on / groupby chips, a Show: unreviewed / all / fail+warn filter, and a ↻ Re-sort queue button. Below it, the summary header tiles the per-module counts — total, fail, warn, pass, insufficient (groups whose metric could not be computed — never shown as a green pass), reviewed, removed, and a robust median metric. The worklist sidebar lists the module’s groups worst-first:

Click the worst (top) group. The detail pane renders its header — the group key, the metric (with a before → after delta once you recompute), the status badge, member count, and removed count — above the tile gallery. Each tile is a centred crop of the colony served by the QC crop route; removed colonies dim. The action bar carries ✓ Mark reviewed, Next group →, and bulk Remove selected / Restore selected:

Remove the outlying colonies (click each tile’s ✕, or shift-select a range and hit Remove selected), then click ✓ Mark reviewed. Because you made changes, the module recomputes on the curated frame and the group’s metric/badge update in place — the row keeps its position and gains a ⤳ “moved/changed” hint. The queue only reorders when you click ↻ Re-sort queue.

Common gotchas

• Recompute reads deliverables/measurements.parquet, not the master. The in-session recompute uses the post-applied + metadata-joined mirror minus your removals, so it matches the artifact the CLI would write. The clean deliverables/master_measurements.parquet is metadata-free and is not used for recompute.

• insufficient is not pass. A check like ICC returns NaN on a sparse or under-powered group; the summary header counts those as insufficient and they sort to the bottom of the worklist — they are “no signal”, not “good”.

• Review progress is per-module and resets on re-run. Marked-reviewed groups live in GUI-owned qc/review_state.json, keyed by check instance_id. An in-session recompute preserves it; the next CLI recompile or measure-mode run clears it so a fresh run starts the queue over.

• Curation is shared. Removing a colony in Review removes it everywhere (the Plate / Colony tabs and the heatmap) — it writes the same deliverables/measurements.parquet removal set.

Where to next

• QC curation loop — the Configure sub-view’s per-check card editor.

• Heatmap exploration — pivot the same measurements (and any QC_*_Metric column) into a plate-shaped heatmap.

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