Datasets:

azithromycin
/

SahilChande_so100_pick_carrot

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "tsfile/tsfile_py_cpp.pyx", line 567, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
              tsfile.exceptions.FileOpenError: 28: 
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
                  with self._open_reader(file) as reader:
                       ~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
                  return TsFileReader(file)
                File "tsfile/tsfile_reader.pyx", line 323, in tsfile.tsfile_reader.TsFileReaderPy.__init__
              SystemError: <class '_weakrefset.WeakSet'> returned a result with an exception set
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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SO100 Pick Carrot TsFile

This Apache TsFile dataset is derived from SahilChande/so100_pick_carrot, a LeRobot v2.1 SO100 robot-manipulation dataset.

Source Dataset

Original dataset: SahilChande/so100_pick_carrot
Author, repository owner, and sole contributor: SahilChande
License: Apache-2.0
Robot type: so100
LeRobot codebase version: v2.1
Task: Use the robotic gripper to securely grasp a visible carrot and accurately place it within the clearly marked square area on the surface.
Split: train
Scale: 91 episodes, 39,357 frames, 1 task, 30 fps
Source frame files: 91 Parquet files
Source frame layout: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
The source card does not provide a paper or completed citation.

TsFile Data

Path: data/sahilchande_so100_pick_carrot.tsfile
Table: sahilchande_so100_pick_carrot
Rows: 39,357
Time precision: milliseconds
Episodes are represented by TsFile TAG values rather than separate files.

Schema

Time is round(timestamp * 1000) milliseconds and restarts within each episode.

Column	TsFile role	Type	Source mapping
`Time`	TIME	`INT64`	`round(timestamp * 1000)`
`episode_index`	TAG	`STRING` device/tag value	Source `episode_index`
`task_index`	TAG	`STRING` device/tag value	Source `task_index`
`frame_index`	FIELD	`INT64`	Preserved
`sample_index`	FIELD	`INT64`	Renamed from source `index`
`action_0` ... `action_5`	FIELD	`FLOAT`	Flattened from `action[6]`
`observation_state_0` ... `observation_state_5`	FIELD	`FLOAT`	Flattened from `observation.state[6]`

The six action and state dimensions are main_shoulder_pan, main_shoulder_lift, main_elbow_flex, main_wrist_flex, main_wrist_roll, and main_gripper.

Conversion Notes

All 91 episode Parquet files are merged into one table-model TsFile. Filter by episode_index and task_index to select a device/episode.
Vector columns are flattened to scalar fields. Full source prefixes are retained, with . replaced by _.
The source timestamp column is omitted after creating Time because the same value is recoverable as Time / 1000 seconds.
No numeric rows, episodes, action dimensions, or state dimensions are omitted.
Encoding and compression: FLOAT/DOUBLE use GORILLA + LZ4; INT32/INT64 and Time use TS_2DIFF + LZ4; BOOLEAN fields would use RLE + LZ4; TAG values use the TsFile table/device mechanism. This source schema has no BOOLEAN field.

Videos

Videos are not included here. They remain in the original repository under videos/chunk-000:

Each stream has one MP4 per episode, for 182 source videos total. Numeric rows remain frame-aligned through episode_index and frame_index.

Usage

from tsfile import TsFileReader

reader = TsFileReader("data/sahilchande_so100_pick_carrot.tsfile")
table_name = "sahilchande_so100_pick_carrot"
columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "sample_index",
    "action_0",
    "observation_state_0",
]

with reader.query_table(table_name, columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())

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