Add TsFile (data/), README, and meta_data (info.json + safetensors)

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/tutorial_ball_2.tsfile filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: other
+language:
+  - en
+pretty_name: "tutorial-ball-2 (LeRobot) — TsFile"
+tags:
+  - time-series
+  - tsfile
+  - robotics
+  - lerobot
+  - imitation-learning
+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.

data/tutorial_ball_2.tsfile

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+size 6199765

meta_data/episode_data_index.safetensors

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+oid sha256:1a6e7b7ca41d3ee170e91b7fc8dbf0cbbff243bcc9179a2704bae225c046a96f
+size 12152

meta_data/info.json

@@ -0,0 +1,32 @@
+{
+  "fps": 30,
+  "video": 0,
+  "tsfile_conversion": {
+    "data_format": "tsfile",
+    "data_path": "data/tutorial_ball_2.tsfile",
+    "note": "data/ converted from the original LeRobot parquet to a single Apache TsFile. No video (video=0).",
+    "tsfile_table_name": "tutorial_ball_2",
+    "time_column": "Time",
+    "time_unit": "ms",
+    "time_definition": "round(frame_index * 1000/30) ms (30 fps); frame_index restarts per episode",
+    "tag_columns": [
+      "episode_id"
+    ],
+    "tag_definition": "episode_id = \"episode_{episode_index}\", one device per episode",
+    "column_mapping": {
+      "observation.state[4]": [
+        "state_0",
+        "state_1",
+        "state_2",
+        "state_3"
+      ],
+      "action[2]": [
+        "action_0",
+        "action_1"
+      ],
+      "timestamp": "episode_timestamp_s",
+      "next.done": "next_done",
+      "index": "sample_index"
+    }
+  }
+}

meta_data/stats.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f3e83fc308516d7c042d55add3f0dfe2cf3e061e43453d3b061336e0fd88fed6
+size 2112