---
license: other
language:
- en
pretty_name: tutorial-ball-2 (LeRobot) — TsFile
tags:
- time-series
- tsfile
- robotics
- lerobot
- imitation-learning
- timeseries
task_categories:
- time-series-forecasting
- robotics
---

# tutorial-ball-2 (LeRobot) — TsFile

This dataset is a **lossless conversion to the [Apache TsFile](https://tsfile.apache.org/)
format** of the HuggingFace LeRobot dataset
[`notmahi/tutorial-ball-2`](https://huggingface.co/datasets/notmahi/tutorial-ball-2):
a low-dimensional robot tutorial trajectory dataset (**no video**).

## Original dataset

- **Source dataset**: [notmahi/tutorial-ball-2](https://huggingface.co/datasets/notmahi/tutorial-ball-2)
- **Format**: early LeRobot format (`meta_data/` + safetensors)
- **Content**: purely numeric low-dimensional state/action trajectories —
  **314,074 frames / 751 episodes / 30 fps**. No images or video
  (`meta_data/info.json`: `video=0`).

## What is in this repository

```
data/
└── tutorial_ball_2.tsfile     # numeric time-series (converted)
meta_data/
├── info.json                  # original fps/video flags + tsfile_conversion notes
├── stats.safetensors          # per-feature statistics (copied verbatim)
└── episode_data_index.safetensors  # episode boundaries (copied verbatim)
```

## TsFile storage mapping (table model)

| Role | Column(s) | Type | Notes |
|------|-----------|------|-------|
| **TAG** | `episode_id` | STRING | `episode_{episode_index}`, 751 devices (one per episode) |
| **Time** | `round(frame_index * 1000 / 30)` ms | INT64 (ms) | 30 fps; frame_index restarts at 0 each episode |
| **FIELD** | `state_0` … `state_3` | FLOAT | `observation.state[4]` expanded |
| **FIELD** | `action_0`, `action_1` | FLOAT | `action[2]` expanded |
| **FIELD** | `episode_index`, `frame_index`, `sample_index` | INT64 | indices (`index` → `sample_index`) |
| **FIELD** | `episode_timestamp_s` | FLOAT | (`timestamp`) |
| **FIELD** | `next_done` | BOOLEAN | (`next.done`) |

## Conversion notes

- **Purely numeric** — the source has no images or video, so only `data/` is
  converted; nothing else needed.
- **TAG = `episode_id`** (751 devices). **Time = `round(frame_index × 1000/30)` ms**.
  Because `frame_index` restarts at 0 within each episode and is strictly increasing,
  and `round(k × 1000/30)` is also strictly increasing in `k` (step ≥ 33 ms), every
  device's time axis is strictly increasing — no de-duplication or offset needed.
  (30 fps gives a ~33.333 ms frame interval; with millisecond precision the per-frame
  times are 0, 33, 67, 100, … — consecutive and collision-free.)
- **Array columns expanded**: `observation.state[4]` → `state_0..state_3`,
  `action[2]` → `action_0..action_1` (FLOAT, matching the source float32).
- **Column names** with dots made TsFile-safe (`next.done` → `next_done`, …).
- **No columns dropped, no rows dropped**: all 314,074 frames preserved.
- `meta_data/` (info / stats / episode index) is copied over; `info.json` gains a
  `tsfile_conversion` block describing the table layout.

## Usage

```python
from tsfile import TsFileReader

reader = TsFileReader("data/tutorial_ball_2.tsfile")
schemas = reader.get_all_table_schemas()
tname = next(iter(schemas))

cols = ["episode_id", "state_0", "state_1", "action_0", "action_1"]
with reader.query_table(tname, cols, batch_size=65536) as rs:
    while (batch := rs.read_arrow_batch()) is not None:
        df = batch.to_pandas()
        # ... process ...
reader.close()
```

## Citation

```bibtex
@misc{tutorial_ball_2,
  title  = {tutorial-ball-2 (LeRobot)},
  author = {notmahi},
  url    = {https://huggingface.co/datasets/notmahi/tutorial-ball-2},
  publisher = {Hugging Face}
}
```

The source HuggingFace dataset does not declare an explicit license.