Datasets:
The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column(/fixtures/[]/time/[]) changed from array to string in row 0
During handling of the above exception, another exception occurred:
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/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
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.
Rumi API Fixtures
A small, deterministic collection of Earth-observation arrays encoded as Rumi files.
This repository exists to test the Rumi API and its stateless remote range reads through Karu. It is not a training dataset, a scientific benchmark, or a general-purpose imagery archive.
What this dataset tests
The fixtures cover:
- complete and windowed reads;
- band selection and ordering;
- temporal selection;
- batch reads with
rumi.read_many; - different Rumi frame layouts;
- signed and unsigned integer types;
- single-band, multiband, and high-dimensional arrays;
- geospatial and temporal metadata;
- HTTP Range reads from Hugging Face.
Repository structure
data/ Rumi containers
headers/ external binary headers required for remote reads
verify/ local and Hugging Face verification programs
manifest.json source identity, storage metadata, and expected results
checksums.sha256 SHA-256 checksums for every container and header
SOURCE_DATA.md source attribution and licensing notes
Each .rumi file has a corresponding external header. The manifest records
its original EarthCompress sample, logical shape, data type, frame layout, tile
size, metadata, and checksums.
Reading a fixture
from pathlib import Path
import rumi
from huggingface_hub import hf_hub_download
repo = "asterisk-labs/rumi-api-fixtures"
name = "s2-00-tile"
header_path = hf_hub_download(
repo_id=repo,
repo_type="dataset",
filename=f"headers/{name}.header",
)
header = Path(header_path).read_bytes()
image = rumi.read(
f"hf://datasets/{repo}/data/{name}.rumi",
header,
bands=[0, 3],
window=(0, 0, 256, 256),
)
print(image.shape)
For reproducible tests, replace the default repository revision with a pinned commit or release tag.
Fixtures
| Fixture | Source corpus | Purpose |
|---|---|---|
s2-00-tile |
Sentinel-2 L1C | Window and band-selective reads |
s2-01-tile |
Sentinel-2 L1C | Batch reads |
s2-02-tile |
Sentinel-2 L1C | Batch reads |
s2-00-planar |
Sentinel-2 L1C | Planar frame layout |
s2-00-chunky |
Sentinel-2 L1C | Pixel-interleaved frame layout |
era5-t2m-00-time |
ERA5 | Temporal selections and ragged edges |
alphaearth-00 |
AlphaEarth | Signed int8 with 64 bands |
emit-00 |
EMIT L2A | Signed int16 with 285 bands |
s1-00 |
Sentinel-1 GRD | Signed radar values |
worldcover-00 |
ESA WorldCover | Single-band categorical data |
The three encodings of s2-00 contain the same logical array. They are
intentionally repeated to verify that frame layout changes storage and range
behavior without changing decoded results.
Verification
Validate the generated files locally:
python verify/verify_local.py
After publication, validate actual remote range reads:
python verify/verify_huggingface.py --revision main
The remote verifier downloads only the manifest and the small external
headers through huggingface_hub. Rumi reads the selected array windows
directly from the remote .rumi objects.
Data provenance
The arrays are selected from the EarthCompress benchmark corpora. Their values are preserved; only their storage representation changes when encoded as Rumi files.
manifest.json identifies the exact source corpus and sample for every
fixture and preserves the original array checksum. See SOURCE_DATA.md for
the required source attributions.
Licensing
The underlying observations remain subject to the terms of their respective
data providers. The applicable attribution and redistribution notes are
recorded in SOURCE_DATA.md.
Scope
These fixtures are deliberately small and are not statistically representative of their source datasets. Passing these tests establishes API and format compatibility; it does not measure compression performance or scientific fitness.
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