schema_version int64 | description string | generator dict | fixtures list | batches list |
|---|---|---|---|---|
1 | Deterministic fixtures for the Rumi read API | {
"rumi": "0.19.0",
"geozl": "0.16.0",
"compression_graph": "planar>zigzag>zstd"
} | [
{
"name": "s2-00-tile",
"purpose": "window and band-selective reads",
"data": "data/s2-00-tile.rumi",
"header": "headers/s2-00-tile.header",
"source": {
"repository": "https://github.com/csaybar/ec-benchmark",
"revision": "6b3a606b75f18e99bffe8e87c97407b7d71962b3",
"corpus": "s... | [
{
"name": "s2-three-scenes",
"fixtures": [
"s2-00-tile",
"s2-01-tile",
"s2-02-tile"
],
"bands": [
3,
0
],
"windows": [
[
221,
217,
91,
107
],
[
221,
217,
91,
107
],
[
... |
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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