Datasets:

azithromycin
/

williamdgomez_SNOW.SB

license: apache-2.0
authors:
  - williamdgomez
task_categories:
  - robotics
tags:
  - tsfile
  - timeseries
  - tabular
  - robotics
  - lerobot
  - manipulation
modality:
  - timeseries
  - tabular
pretty_name: SNOW.SB TsFile
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/williamdgomez_snow_sb.tsfile
size_categories:
  - 10K<n<100K

SNOW.SB TsFile

This repository is an Apache TsFile conversion of the LeRobot dataset williamdgomez/SNOW.SB. The original Hugging Face repository and its commit history identify williamdgomez as the dataset author/uploader. The source dataset was created with LeRobot v3.0 for the lekiwi_client robot.

Source Dataset

Dataset: williamdgomez/SNOW.SB
Author/uploader: williamdgomez
License: Apache-2.0
Task: Pick up the block and place it in the bin
Split: train (0:91)
Sampling rate: 24 fps
Scale: 91 episodes, 73,578 frames, 1 task
Source data shards: 91 Parquet files under data/chunk-000/file-*.parquet
Source video files: 273 MP4 files, 91 per camera stream

The source video streams are stored at videos/:

videos/observation.images.front/chunk-000/file-{file_index:03d}.mp4
videos/observation.images.wrist/chunk-000/file-{file_index:03d}.mp4
videos/observation.images.top/chunk-000/file-{file_index:03d}.mp4

Videos are not included in this TsFile repository. They remain available in the original Hugging Face dataset and can be aligned with rows using episode_index, frame_index, and the source meta/episodes offsets.

Converted Files

TsFile: data/williamdgomez_snow_sb.tsfile
Table: williamdgomez_snow_sb
Rows: 73,578
Devices: 91 (episode_index x task_index)
Time precision: milliseconds
Metadata: meta/ mirrors the source metadata; meta/info.json records the TsFile path and conversion mapping.

Schema

Time is round(timestamp * 1000) and is stored in milliseconds. The source timestamp is dropped after this conversion because it is exactly the seconds representation of Time; Time restarts at zero for each episode.

Column group	TsFile columns	Type / role
Time	`Time`	INT64/TIMESTAMP, TIME
Tags	`episode_index`, `task_index`	TAG/device segments
Frame metadata	`frame_index`, `sample_index`	INT64 FIELD; `sample_index` is source `index`
Action	`action_0` ... `action_8`	FLOAT FIELD, flattened from `action[9]`
Observation	`observation_state_0` ... `observation_state_8`	FLOAT FIELD, flattened from `observation.state[9]`

Vector names preserve their source prefixes (. becomes _) and append a zero-based element index. The three video features (observation.images.front, .wrist, .top) are omitted from the TsFile because TsFile stores the numeric time-series table while the original MP4 files remain at the source URL.

Encoding and Compression

The local conversion used the type-aware TsFile writer profile requested for this dataset:

FLOAT/DOUBLE: GORILLA with LZ4
INT32/INT64 and Time: TS_2DIFF with LZ4
BOOLEAN (if present): RLE with LZ4
TAG columns: TsFile table/device TAG mechanism

The generated TsFile is 1,456,157 bytes. The merged staged Parquet is 1,235,578 bytes, and the 91 source Parquet shards total 3,152,836 bytes.

Validation

Apache TsFile Python SDK readback succeeded. The TsFile metadata row count and query readback both equal the staged Parquet count: 73,578 rows. All 91 episode devices have monotonic Time values with no duplicate (episode_index, task_index, Time) keys.

Usage

from tsfile import TsFileReader

reader = TsFileReader("data/williamdgomez_snow_sb.tsfile")
table = reader.get_all_table_schemas()["williamdgomez_snow_sb"]
columns = [c.get_column_name() for c in table.get_columns() if c.get_column_name() != "Time"]

with reader.query_table("williamdgomez_snow_sb", columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()

Citation

The original dataset card does not provide a paper or BibTeX citation.