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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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