Datasets:

azithromycin
/

tlpss_ur5e-pour-cup

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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UR5e Pour Cup TsFile

This Apache TsFile edition comes from tlpss/ur5e-pour-cup, a LeRobot v2.1 robot-manipulation dataset for pouring a white mug into a transparent cup.

Source Dataset and Attribution

Original dataset: tlpss/ur5e-pour-cup
Repository owner and uploader: tlpss
Author note: the source repository does not provide a separate author list.
License: Apache-2.0
Task: pour the white mug in the transparent cup
Robot platform indicated by the dataset name: UR5e
LeRobot codebase version: v2.1
Split: train; sampling rate: 10 fps
Scale: 101 episodes, 36,863 frames, 1 task
Source layout: 101 Parquet files at data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
The source card provides no paper or completed BibTeX citation.

TsFile Data

Path: data/tlpss_ur5e_pour_cup.tsfile (3,836,679 bytes)
Table: tlpss_ur5e_pour_cup
Rows: 36,863; episodes/devices: 101; tasks: 1
Time precision: milliseconds
TsFile/source-Parquet size ratio: 0.554
Source metadata remains under meta/; the main Parquet files are not copied into meta/.

Schema

Time = round(timestamp * 1000) milliseconds. Time restarts at zero in each episode. Source timestamp is omitted afterward because it equals Time / 1000 seconds.

TAG columns, stored through the TsFile table/device mechanism:

episode_index
task_index

Scalar FIELD columns:

frame_index
sample_index (renamed from source index)
next_reward (from next.reward, FLOAT)
next_success (from next.success, BOOLEAN)
seed (INT64)
gripper_state (FLOAT)

Flattened FLOAT FIELD groups:

robot_pose[6] -> robot_pose_0 ... robot_pose_5
joints[6] -> joints_0 ... joints_5
action[10] -> action_0 ... action_9
observation.state[7] -> observation_state_0 ... observation_state_6

No numeric row, episode, task, scalar field, or vector dimension is dropped.

Encodings and Compression

FLOAT/DOUBLE: GORILLA + ZSTD
INT32/INT64: TS_2DIFF + ZSTD
Time: TS_2DIFF + LZ4
BOOLEAN: RLE + LZ4
TAG: TsFile table/device storage

The physical table schema, Time codec, every FIELD codec, and all 36,863 rows were read back with the Apache TsFile Java API.

Videos

Videos are not included in this TsFile repository. They remain in the original dataset under videos/, with 202 H.264 MP4 files (250,413,212 bytes), 240x320, 10 fps, and no audio:

observation.images.scene_image: 101 files, 138,932,857 bytes
observation.images.wrist_image: 101 files, 111,480,355 bytes

The source template is videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4. Use episode_index, frame_index, and meta/episodes.jsonl to align numeric rows with the source videos.

Usage

from tsfile import TsFileReader

reader = TsFileReader("data/tlpss_ur5e_pour_cup.tsfile")
with reader.query_table(
    "tlpss_ur5e_pour_cup",
    ["episode_index", "task_index", "frame_index", "sample_index",
     "next_success", "action_0", "observation_state_0"],
    batch_size=65536,
) as result:
    print(result.read_arrow_batch().to_pandas().head())
reader.close()

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Total file size:

4.29 MB