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"])
dataset = taco.open_dataset("dataset.zip")
targets = dataset.read(files="target.tif")
train = dataset.sql("SELECT * FROM data WHERE \"ml:split\" = 'train'")
export() writes a smaller dataset with the same contract. samples is a
PyArrow-compatible table, normally selected from the data SQL relation.
Keyword arguments replace fields of the collection, such
as id or description; the rest is inherited. For a remote source, metadata
is cached and only the payload files belonging to the selected samples are
downloaded. Pass overwrite=True to replace an existing TACO output.
source = "https://data.source.coop/major-tom/core-dem/"
dataset = taco.open_dataset(source)
rows = dataset.sql('SELECT * FROM data ORDER BY "taco:sample_index" LIMIT 10')
taco.export(
source,
"core-dem-sample.zip",
samples=rows,
id="core-dem-sample",
description="Ten samples from Core-DEM",
)
Remote reads and exports show download progress in interactive terminals.
Writers show their build progress when opened with progress=True.
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 |
1 maintainer
Release files for taco-eo 0.9.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| taco_eo-0.9.0.tar.gz | 778.1 kB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| taco_eo-0.9.0-py3-none-win_amd64.whl | Python 3 | none | Windows x86-64 | Details |
| taco_eo-0.9.0-py3-none-manylinux_2_28_x86_64.whl | Python 3 | none | Linux glibc 2.28+ x86-64 | Details |
| taco_eo-0.9.0-py3-none-manylinux_2_28_aarch64.whl | Python 3 | none | Linux glibc 2.28+ ARM64 | Details |
| taco_eo-0.9.0-py3-none-macosx_11_0_x86_64.whl | Python 3 | none | macOS 11.0+ x86-64 | Details |
| taco_eo-0.9.0-py3-none-macosx_11_0_arm64.whl | Python 3 | none | macOS 11.0+ ARM64 | Details |
Total release size: 21.3 MB
Release files / taco_eo-0.9.0.tar.gz
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