Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
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 68, 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.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Flock Demo Glucose Time-Series — TsFile format
This repository is a conversion to TsFile format of the Hugging Face dataset
random-sequence/flock-demo-time-series-prediction-sections.
⚠️ This is a demo dataset: simulated patient glucose / insulin / carbohydrate / activity monitoring time series, not real clinical data.
- Original dataset: random-sequence/flock-demo-time-series-prediction-sections
- License: the original dataset declares no license (this conversion follows suit and adds none).
- Original README: only field definitions (YAML frontmatter), no body description. The data-semantics description below comes from inspecting the actual data.
Data content
Simulated continuous monitoring records for 10 patients, one sample every 5 minutes. The original dataset is split by time into 8 sections (section_01 … section_08, each a Hugging Face config); the 8 sections are contiguous in time with no overlap or gaps:
| section | rows | patients | time range |
|---|---|---|---|
| section_01 | 250 | 10 | 2024-01-01 00:00 → 2024-01-01 20:45 |
| section_02 | 250 | 10 | 2024-01-01 20:50 → 2024-01-02 17:35 |
| section_03 | 250 | 10 | 2024-01-02 17:40 → 2024-01-03 14:25 |
| section_04 | 250 | 10 | 2024-01-03 14:30 → 2024-01-04 11:15 |
| section_05 | 250 | 10 | 2024-01-04 11:20 → 2024-01-05 08:05 |
| section_06 | 250 | 10 | 2024-01-05 08:10 → 2024-01-06 04:55 |
| section_07 | 250 | 10 | 2024-01-06 05:00 → 2024-01-07 01:45 |
| section_08 | 250 | 10 | 2024-01-07 01:50 → 2024-01-07 22:35 |
Within each section: the global timestamp is unique and strictly increasing, with the 10 patients interleaved; per patient, each patient's own records are also monotonic in time with no duplicates.
Files (one TsFile per section)
section_01.tsfile … section_08.tsfile, one-to-one with the original dataset's 8 configs, not merged.
TsFile structure
- TAG (device dimension) =
patient_id: each patient is a device. Within each patient the timestamp is monotonic with no duplicates. - Time: parsed from the original
timestamp(2024-01-01 00:00:00text, 5-minute interval) into INT64 milliseconds. - FIELD:
glucose_mg_dl(DOUBLE, blood glucose mg/dL)insulin_units(DOUBLE, insulin units)carbs_grams(DOUBLE, carbohydrate grams)activity_level(STRING, activity level: rest / light / moderate / intense)
What changed vs the original dataset
| Item | Original | This TsFile version | Notes |
|---|---|---|---|
| Format | Parquet (8 configs) | TsFile (8 .tsfile) | one section per TsFile, preserving the original split |
timestamp column |
string (2024-01-01 00:00:00) |
parsed into Time (INT64 ms) and the original text column dropped |
time info fully preserved; only the format changes from string to millisecond integer |
patient_id |
int64 regular field | declared as TAG (device), stored as STRING in TsFile | TAG columns in the TsFile table model are unified to STRING; the integer value is preserved verbatim (1 → "1") |
| other columns | — | kept as-is | glucose_mg_dl / insulin_units / carbs_grams → DOUBLE, activity_level → STRING |
| row order | by global time | sorted by (patient_id, Time) | guarantees monotonic time within each device, as required for TsFile writes |
The only dropped column: the original timestamp text column (its info is folded into Time). Apart from that, no columns are dropped and no values are modified.
Reading example
from tsfile import TsFileReader
reader = TsFileReader("section_01.tsfile")
for name, schema in reader.get_all_table_schemas().items():
print(name, [c.get_column_name() for c in schema.get_columns()])
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