(target-changepoint-correction)=
# Changepoint correction

Once changepoints are detected, they can be used to refine label boundaries.
The correction pipeline snaps hand-drawn label edges to nearby changepoints,
producing more consistent annotations.

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## How it works

The correction runs four steps in sequence:

1. **Purge short intervals** — remove labels shorter than the minimum duration.
2. **Stitch gaps** — merge adjacent same-label intervals separated by a small gap.
3. **Snap boundaries** — move each label's start/end to the nearest changepoint, constrained by maximum expansion/shrink limits.
4. **Purge short intervals** (again) — snapping may create new short intervals.

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## Parameters

| Parameter | Description |
|-----------|-------------|
| **Min label length** | Labels shorter than this are removed. |
| **Stitch gap** | Maximum gap between same-label segments to merge. |
| **Max expansion** | How far a boundary can move outward toward a changepoint. |
| **Max shrink** | How far a boundary can move inward toward a changepoint. |
| **Per-label thresholds** | Override the global min label length for specific label classes. |

These four parameters can be set in frames or seconds. I would recommend
using frames for kinematics and seconds for audio data.

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## Automatic vs manual correction

**Automatic (during labelling):**
- **Checkbox "Changepoint correction"**: When enabled, label boundaries are snapped to changepoints as you draw them. This keeps hand-drawn annotations consistent in real time.

**Manual (post-hoc, bulk application):**
Manual correction is especially useful for cleaning up model predictions in bulk — run the detector once, then snap all predicted boundaries to changepoints across the dataset in one step.
- **Single Trial**: Applies the full correction pipeline to the current trial's labels.
- **All Trials**: Applies correction to every trial. The dataset is marked as corrected to prevent double-application.
- **Undo**: Reverts the last correction (single or all trials).

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