taco

Read and write TACO datasets in Python.

pip install taco-eo
python examples/minimal.py

Published wheels include the native TACO reader. Building from the source distribution requires a C++23 compiler, CMake, Ninja, pkg-config, libcurl 7.83 or newer, and OpenSSL 3 or newer.

import taco

samples = taco.read("dataset.zip")
parts = taco.read(["part-0.zip", "part-1.zip"])

Versioned dataset roots select their declared default release without listing remote storage. Use a release URL to open an immutable version directly.

dataset = taco.open_dataset("https://data.source.coop/major-tom/core-dem/")
print(dataset.version)
print(dataset.versions)

previous = taco.open_dataset("https://data.source.coop/major-tom/core-dem/1.0.0/")

export() writes a smaller dataset with the same contract. where filters the rows returned by read(), and the subset needs its own id and description.

taco.export(
    "dataset.zip",
    "test.zip",
    where="\"ml:split\" = 'test'",
    id="dataset-test",
    description="Test split of the dataset",
)

Examples

Every example is self-contained, uses synthetic data, and writes its output in the current directory.

Spatial and temporal metadata use separate profiles: Spatial for regular spatial grids, ISpatial for irregular footprints, and Temporal for time alone. STAC combines regular spatial + temporal metadata; ISTAC combines irregular spatial + temporal metadata.

Example What it demonstrates
minimal.py Smallest possible single-file dataset
numpy_minimal.py NumPy image and mask assets with a train/test split
change_detection.py Metadata on before/ and after/ folders
sequence.py Variable-length asset sequences
time_series.py Per-observation time and cloud metadata
geospatial.py Compact STAC metadata and derived MajorTOM cells
stac_segmentation.py STAC extensions for regular raster chips, labels, bands, and scaling
oceantaco_istac.py OceanTACO-inspired ISTAC metadata for irregular SWOT swaths and Argo collocations
partitioned.py ZIP partitions and their TACOCAT catalog

Project links

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Credits

Author: Cesar Aybar

GitHub Statistics

Data verified by PyPI on Sep 15, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
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Requires

Python >=3.10

Provides Extra

antimeridian dev geoenrich progress rumi test test-eo

Tags

cloud-optimized cozip earth-observation geospatial machine-learning parquet taco

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