Document full schema with per-caller onset/offset fields

README.md

@@ -117,17 +117,104 @@ The flat WAV mirrors are best-effort: HuggingFace caps each directory at 10 000
 
 ## Data fields (parquet)
 
-All V33DA fields are preserved — audio (inline bytes), accelerometer (inline bytes), 3D pose, 2D reprojections, FM radio telemetry, vocalizer index, bird color, experiment, date — plus the V33DA++ extensions:
+Each parquet row is one vocalization event. Most binary array columns are NumPy `np.save()` output; load them with `np.load(io.BytesIO(row[col]))`.
 
-| Field | Buckets | Description |
+### Identity & metadata
+
+| Field | Type | Description |
+|---|---|---|
+| `id` | string | Globally unique row identifier: `"{experiment}/{video_stem}/{mid_frame}/{bird_name}"` |
+| `experiment` | string | One of `juvExpBP01`, `juvExpBP02`, `juvExpBP05` |
+| `date` | string | Recording date `YYYY-MM-DD` |
+| `video_stem` | string | Source recording stem (e.g. `BP_2021-08-05_08-40-49_416952_0460000`) |
+| `bird_name` | string | Internal vocalizer ID (per-experiment, e.g. `b14o14_j`) |
+| `bird_color` | string | Vocalizer's backpack color (per-experiment identifier; not global) |
+| `vocalizer_idx` | int32 | Vocalizer's index within this experiment's bird set (0-based) |
+
+### Event timing (this row's vocalization)
+
+| Field | Type | Description |
+|---|---|---|
+| `event_onset_sec` | float32 | Voc onset in seconds, relative to the source recording |
+| `event_offset_sec` | float32 | Voc offset in seconds, relative to the source recording |
+| `event_onset_frame` | int32 | Voc onset in 47.68 fps video frames |
+| `event_offset_frame` | int32 | Voc offset in 47.68 fps video frames |
+| `context_pad_sec` | float32 | Audio/video pad on each side of the event (≈0.01 for `overlap`, 2.0 for the `*_padded` buckets) |
+| `n_frames` | int32 | Number of video frames covered by the keypoint window |
+
+### Audio & accelerometer
+
+| Field | Type | Description |
+|---|---|---|
+| `audio` | bytes | NumPy-serialized `(5, T)` float32 array — 5 cage microphones at 24,414 Hz, padded by `context_pad_sec` on each side |
+| `audio_path` | string | Path to a separately exported multichannel WAV mirror (best-effort, see Release contents) |
+| `accelerometer` | bytes | NumPy-serialized `(N_birds, T)` float32 array — per-bird on-body vibration channel at 24,414 Hz, used as the verification oracle |
+| `accelerometer_path` | string | Path to a separately exported multichannel WAV mirror (best-effort) |
+| `video_path` | string | Path to a composite multi-view MP4 (top, back, side) — `null` if not available |
+
+### 3D pose (every visible bird, every frame)
+
+5 keypoints per bird: `beak`, `head`, `backpack`, `tail base`, `tail tip`.
+
+| Field | Type | Description |
+|---|---|---|
+| `keypoints_3d` | bytes | NumPy `(T_frames, N_birds, 5, 3)` float32 — 3D positions triangulated from top + back views, in BirdPark cage coordinates (mm) |
+| `keypoints_2d_top` | bytes | NumPy `(T_frames, N_birds, 5, 2)` float32 — 2D keypoints in the top-camera image (pixels) |
+| `keypoints_2d_back` | bytes | NumPy `(T_frames, N_birds, 5, 2)` float32 — 2D keypoints in the back-camera image (pixels) |
+
+Per-frame indexing: bird `i`'s keypoint `k` at frame `t` is `kp3d[t, i, k]` (and similarly for the 2D arrays). NaN values may appear in rows that were extracted under a lenient filter (see *Lenient extraction*, below). Bird index → bird color/name mapping is fixed within an experiment (see `metadata.json`).
+
+### Radio telemetry (every visible bird, every frame)
+
+Per-bird FM radio signals from a 4-antenna receiver array (4 antennas: A, B, C, D; M = mean over antennas). Each is a NumPy `(N_birds, T_radio)` float32 byte blob.
+
+| Field | Description |
+|---|---|
+| `radio_frq` | Per-bird FM carrier frequency offset (Hz) |
+| `radio_thetaS` | Source-side phase used for triangulation |
+| `radio_thetaA / B / C / D` | Per-antenna received phase (rad) |
+| `radio_phiA / B / C / D / M` | Per-antenna and mean phase difference (rad) |
+| `radio_powRA / B / C / D / M` | Per-antenna and mean received power (linear) |
+| `radio_powNA / B / C / D / M` | Per-antenna and mean noise floor (linear) |
+
+### Overlap-event metadata (`overlap` and `overlap_padded` only)
+
+These rows belong to **multi-caller** events: the audio window contains vocalizations from ≥2 birds at once. The fields below describe every concurrent vocalization that overlapped with this row's bird in any +/-PAD_SEC sliding window (including the row's own voc), enabling per-caller source separation, per-caller vocal-activity-detection, etc.
+
+There are **two views** of the same multi-caller event, kept separate for compatibility:
+
+#### Color-set view (existing fields, unique colors)
+
+| Field | Type | Description |
 |---|---|---|
-| `event_onset_sec`, `event_offset_sec` | all | Original V33DA event boundaries in seconds, relative to the (possibly padded) clip |
-| `event_onset_frame`, `event_offset_frame` | all | Same boundaries in 47.68 fps video frames |
-| `context_pad_sec` | all | Pad applied on each side (0 for `overlap`, 2.0 for the padded buckets) |
-| `overlap_callers` | overlap, overlap_padded | List of accelerometer-verified concurrent caller colors |
-| `overlap_count` | overlap, overlap_padded | Number of concurrent callers (≥ 1) |
+| `overlap_callers` | list[string] | Backpack colors of all concurrent callers, **deduplicated** and sorted alphabetically (e.g. `['blue', 'white']`) |
+| `overlap_count` | int32 | `len(overlap_callers)` — number of unique caller-birds in the event |
+
+#### Vocalization-list view (new fields, one entry per voc)
+
+These six lists are **parallel** (same length, same index order — sorted by `bird_color`, ties broken by onset). Length is `overlap_voc_count`, which can be larger than `overlap_count` if the same bird vocalized more than once in the active window or if the row participated in two adjacent overlap windows that share a partner.
+
+| Field | Type | Description |
+|---|---|---|
+| `overlap_caller_bird_names` | list[string] | Internal bird names (e.g. `'b14o14_j'`) — one per voc |
+| `overlap_caller_bird_colors` | list[string] | Backpack colors (may contain duplicates), same order as bird_names |
+| `overlap_caller_onset_sec` | list[float32] | Each voc's onset in seconds (same time frame as `event_onset_sec`) |
+| `overlap_caller_offset_sec` | list[float32] | Each voc's offset in seconds |
+| `overlap_caller_onset_frame` | list[int32] | Each voc's onset in 47.68 fps video frames |
+| `overlap_caller_offset_frame` | list[int32] | Each voc's offset in 47.68 fps video frames |
+| `overlap_voc_count` | int32 | `len(overlap_caller_bird_names)` |
+
+> **Tip.** To find when caller `'white'` is vocalizing inside this event, do `i = overlap_caller_bird_colors.index('white'); on, off = overlap_caller_onset_sec[i], overlap_caller_offset_sec[i]`. If `'white'` appears multiple times (multiple vocs from the same bird), iterate through all matches.
+
+### Lenient extraction
+
+A small fraction of rows (~6% of `overlap` and `overlap_padded`) are extracted under a **lenient policy**: the per-frame motion-displacement filter is bypassed and any NaN keypoints are zero-filled. These rows have valid audio, radio, accelerometer, and event-timing fields but possibly noisier 3D pose. They are included so the bucket size matches the paper's event count.
+
+You can recognise them by checking for any zeros (or large displacements) in `keypoints_3d`. Strictly extracted rows (under V33DA's normal filter) have all-finite keypoints with frame-to-frame displacement ≤ 40 px.
+
+### Per-bucket metadata
 
-Each bucket's `metadata.json` contains the full schema and the filter chain used to build the bucket.
+Each bucket ships a `metadata.json` listing exact row counts, per-bird counts, schema, filter settings, frame rate, and pad-frame counts.
 
 > **Note on bird identity.** Backpack color codes are experiment-local identifiers, not global bird IDs. A color reused across experiments (e.g. `red` in BP01 and BP02) refers to two different physical individuals; all 10 released birds are distinct.