docs/quality.md

# Data quality

A small fraction of frames contain known sensor artifacts. The repo ships [`../bad_frames.json`](../bad_frames.json) as a skip-list so downstream code can avoid sampling on top of these intervals — important since this dataset is intended for **short-window dynamics / world-model learning**, where a glitch landing inside a training window can dominate the loss for that step.

> **All frame indices in `bad_frames.json` are in TRIMMED `.pt` coordinates.** The original H5 recordings have not been edited; only the published `.pt` files have had their OT-uninitialized prefixes cut (see [`caveats.md`](caveats.md) §OT track loss). The per-file trim offset is stored in `_contact_meta.trim_offset` inside each `.pt`.

## Headline numbers

| | |
|---|---:|
| Total synchronized frames (post-trim) | 190,231 (105.7 min @ 30 Hz) |
| Recording files | 27 |
| Frames flagged in `bad_frames.json` | **1,768 (0.929 %)** |
| Recording files with ≥ 1 flagged frame | 11 |

## Failure modes — quantitative breakdown

| # | Mode | Frames | % of dataset | Files | Symptom | Example |
|---|---|---:|---:|---:|---|---|
| 1 | **GelSight LED flicker** | 56 | **0.029 %** | 5 / 27 | 1–2 frames of uniform pink/magenta wash across the gel surface; adjacent frames normal. | [`intensity_spike_overview.png`](../figures/dataset_figures/intensity_spike_samples/intensity_spike_overview.png) |
| 2 | **OptiTrack pose teleport** | 56 | **0.029 %** | 3 / 27 | Translation velocity > 5 m/s **or** angular velocity > 15 rad/s in a single OT sample (solver flip — physically impossible for human-hand motion). | [`pose_teleport_samples/`](../figures/dataset_figures/pose_teleport_samples/) |
| 3 | **OptiTrack track loss** | 1,680 | **0.883 %** | 6 / 27 | Sensor pose held at a stale value for ≥ 0.25 s; cross-modal motion check confirms hand was actually moving. Cause: marker briefly left the mocap volume / camera FOV. | [`freeze_diagnose/`](../figures/dataset_figures/freeze_diagnose/) |

**Union of modes 1+2+3: 1,768 / 190,231 = 0.929 % of post-trim frames flagged.** Mode 3 is by far the largest — but it's already orders of magnitude smaller than what the *raw* recordings looked like (15 % of frames before the OT-uninitialized prefixes were trimmed). The remaining 0.88 % is genuine mid-episode mocap dropout that no amount of post-processing can recover.

## How to use the data — three recipes

### A. Easiest: the shipped example dataloader

`ReactWindowDataset` does the right thing out of the box. Just turn `skip_bad_frames=True`:

```python
from examples.react_window_dataset import ReactWindowDataset
from torch.utils.data import DataLoader

ds = ReactWindowDataset(
    data_root       = "processed/mode1_v1/motherboard",
    bad_frames_path = "bad_frames.json",
    tasks_json_path = "tasks.json",
    window_length   = 16,
    skip_bad_frames = True,    # ← drops windows touching ANY of modes 1/2/3
    respect_active_sensors = True,
)
```

Any window whose [t_start, t_end] range overlaps an `intensity_spikes` /
`pose_teleports_{L,R}` / `ot_loss_{L,R}` interval is silently dropped at
window-enumeration time, so your DataLoader never sees them.

### B. Loading a single `.pt` directly — DIY skip-list

If you're rolling your own sampler or scanning a single file:

```python
import json, torch

ep_key = "2026-05-11/episode_017"
ep = torch.load(f"processed/mode1_v1/motherboard/{ep_key}.pt", weights_only=False)
T = ep["view"].shape[0]
trim_offset = ep["_contact_meta"].get("trim_offset", 0)   # already applied to ep
bad = json.load(open("bad_frames.json"))["episodes"][ep_key]

# Build a per-frame boolean mask (True = drop)
import numpy as np
mask = np.zeros(T, dtype=bool)
for s_, e_ in (bad["intensity_spikes"]
               + bad["pose_teleports_L"] + bad["pose_teleports_R"]
               + bad["ot_loss_L"] + bad["ot_loss_R"]):
    mask[s_:e_ + 1] = True
print(f"{mask.sum()}/{T} flagged ({100 * mask.mean():.2f} %)")

# Then sample only clean windows:
def clean_window_start(t_start, win_len):
    return not mask[t_start:t_start + win_len].any()
```

### C. Only care about action labels (pose)? Skip just ot_loss

If your model treats vision + tactile as observations and sensor pose as the *action* label (UMI-style imitation learning), the LED flicker and translation teleports are observation-side noise that you can usually tolerate (single frames or short < 0.3 s spikes). The OT track losses are the one mode you *must* exclude, because the recorded "action" is stale:

```python
# Just the action-label cuts
def clean_pose_window(t_start, win_len, bad_ep):
    for s_, e_ in (bad_ep["ot_loss_L"] + bad_ep["ot_loss_R"]
                   + bad_ep["pose_teleports_L"] + bad_ep["pose_teleports_R"]):
        if s_ <= t_start + win_len - 1 and e_ >= t_start:
            return False
    return True
```

### What NOT to do

- Don't ignore `bad_frames.json` — for `ot_loss` intervals the *recorded pose looks plausible* (it's just a stale held value), but the actual sensor was moving. A model trained on those will learn that contact-rich tactile signals are uncorrelated with motion.
- Don't try to skip individual *frames* and stitch the rest — windows must be contiguous over time. Drop the **whole window**, not just the bad frames in it.

## Inspection figures

**Mode 1 — GelSight LED flicker** (overview across all affected files):

![Intensity spike overview](../figures/dataset_figures/intensity_spike_samples/intensity_spike_overview.png)

Per-file close-ups: [`figures/dataset_figures/intensity_spike_samples/`](../figures/dataset_figures/intensity_spike_samples/).

**Mode 2 — OptiTrack pose teleport.** GIFs (10 s playback at 2× speed) for all affected files: [`pose_teleport_samples/`](../figures/dataset_figures/pose_teleport_samples/).

**Mode 3 — OptiTrack track loss.** 15 sample clips (recording-viewer layout, 30 fps real time, red ring on the frozen-sensor projected dot): [`freeze_diagnose/ot_loss/`](../figures/dataset_figures/freeze_diagnose/ot_loss/).

## `bad_frames.json` schema

Frame indices are inclusive on both ends and pre-padded by `buffer_frames` (3) on each side so context windows don't bleed into the glitch:

```json
{
  "tau_intensity": 30.0,
  "tau_velocity_mps": 5.0,
  "tau_angular_rad_per_s": 15.0,
  "tau_opt_gap_s": 0.10,
  "freeze_threshold_s": 0.25,
  "buffer_frames": 3,
  "summary": {
    "n_episodes": 27,
    "total_frames": 190231,
    "total_bad_frames": 1768,
    "bad_fraction_overall": 0.009294,
    "n_episodes_with_bad_frames": 11
  },
  "episodes": {
    "2026-05-11/episode_003": {
      "n_frames": 10032,
      "duration_s": 334.4,
      "intensity_spikes": [[260, 268], "..."],
      "pose_teleports_L": [],
      "pose_teleports_R": [],
      "ot_loss_L": [],
      "ot_loss_R": [],
      "total_bad_frames": 19,
      "bad_fraction": 0.0019
    }
  }
}
```

A per-mode aggregate is also available: [`figures/dataset_figures/data_quality_breakdown.json`](../figures/dataset_figures/data_quality_breakdown.json).

## Full report (per-file CSV)

Every file's individual stats (frames, duration, contact %, max intensity left/right, drift, max pose velocity, etc.) are in [`figures/dataset_figures/data_quality_report.csv`](../figures/dataset_figures/data_quality_report.csv).