A tool to easily write, validate and convert ODC datasets and metadata.
pip install eodatasets3
Python 3.6+ is supported.
The assembler api aims to make it easy to write datasets.
from eodatasets3 import DatasetAssembler
from datetime import datetime
from pathlib import Path
with DatasetAssembler(
Path('/some/output/collection/path'),
naming_conventions='default') as p:
# Add some common metadata fields.
p.platform = 'landsat-7'
p.instrument = 'ETM'
p.datetime = datetime(2019, 7, 4, 13, 7, 5)
p.processed_now()
# Support for custom metadata fields
p.properties['fmask:cloud_shadow'] = 42.0
# If you have a source dataset, you can include it as provenance.
# Assembler can also copy common metadata properties from it.
# (... so we didn't need to set the "platform" above!)
p.add_source_path(source_dataset, auto_inherit_properties=True)
# Write measurements. They can be from numpy arrays, open rasterio datasets,
# file paths, ODC Datasets...
p.write_measurement("red", red_path)
... # now write more measurements
# Create a jpg thumbnail image using the measurements we've written
p.write_thumbnail(red="swir1", green="swir2", blue="red")
# Validate the dataset and write it to the destination folder atomically.
p.done()The Assembler will write a folder of COG imagery, an eo3 metadata doc for Open Data Cube, and create appropriate file and folder structures for the chosen naming conventions.
If you already have existing imagery, you can use DatasetAssembler to create a matching metadata document.
See the documentation guide for more features and examples.
The assembler writes a format called "eo3", which will be the native metadata format for Open Data Cube 2.0. We recommend new products are written with this format, even if targeting Open Data Cube 1. Datacube versions from 1.8 onwards are compatible natively with eo3.
eo3 adds information about the native grid of the data, and aims to be more easily interoperable with the upcoming Stac Item metadata.
eo3-validate a lint-like checker to check ODC documents.
Give it ODC documents for your products, types and/or datasets to have them validated.
❯ eo3-validate my-product.odc-product.yaml /tmp/path/to/dataset.odc-metadata.yaml
❯ eo3-validate https://explorer.dea.ga.gov.au/products/ga_ls_fc_3.odc-product.yaml
You can also run with --thorough to have it open imagery files too, checking
their properties match the product (nodata, dtype etc)
❯ eo3-validate --help
Usage: eo3-validate [OPTIONS] [PATHS]...
Validate ODC dataset documents
Paths can be products, dataset documents, or directories to scan (for
files matching names '*.odc-metadata.yaml' etc), either local or URLs.
Datasets are validated against matching products that have been scanned
already, so specify products first, and datasets later, to ensure they can
be matched.
Options:
--version Show the version and exit.
-W, --warnings-as-errors Fail if any warnings are produced
--thorough Attempt to read the data/measurements, and
check their properties match
--expect-extra-measurements / --warn-extra-measurements
Allow some dataset measurements to be
missing from the product definition. This is
(deliberately) allowed by ODC, but often a
mistake. This flag disables the warning.
--explorer-url TEXT Use product definitions from the given
Explorer URL to validate datasets. Eg:
"https://explorer.dea.ga.gov.au/"
--odc Use product definitions from datacube to
validate datasets
-q, --quiet Only print problems, one per line
--help Show this message and exit.
eo3-to-stac: Convert an EO3 metadata doc to a Stac Item
❯ eo3-to-stac --help
Usage: eo3-to-stac [OPTIONS] [ODC_METADATA_FILES]...
Convert an EO3 metadata doc to a Stac Item.
Options:
-v, --verbose
-u, --stac-base-url TEXT Base URL of the STAC file
-e, --explorer-base-url TEXT Base URL of the ODC Explorer
--validate / --no-validate Validate output STAC Item against online
schemas
--help Show this message and exit.
Example usage:
❯ eo3-to-stac LT05_L1TP_113081_19880330_20170209_01_T1.odc-metadata.yaml
❯ ls
LT05_L1TP_113081_19880330_20170209_01_T1.odc-metadata.yaml
LT05_L1TP_113081_19880330_20170209_01_T1.stac-item.json
Some scripts are included for preparing common metadata documents, such as landsat scenes.
eo3-prepare: Prepare ODC metadata from the commandline.
Some sub-commands need the ancillary dependencies, for reading from
exotic formats: pip install .[ancillary]
❯ eo3-prepare --help
Usage: eo3-prepare [OPTIONS] COMMAND [ARGS]...
Options:
--version Show the version and exit.
--help Show this message and exit.
Commands:
landsat-l1 Prepare eo3 metadata for USGS Landsat Level 1 data.
modis-mcd43a1 Prepare MODIS MCD43A1 tiles for indexing into a Data...
noaa-prwtr Prepare NCEP/NCAR reanalysis 1 water pressure datasets...
sentinel-l1 Prepare eo3 metadata for Sentinel-2 Level 1C data produced...
eo3-package-wagl: Convert and package WAGL HDF5 outputs.
Needs the wagl dependencies group: pip install .[wagl]
❯ eo3-package-wagl --help
Usage: eo3-package-wagl [OPTIONS] H5_FILE
Package WAGL HDF5 Outputs
This will convert the HDF5 file (and sibling fmask/gqa files) into
GeoTIFFS (COGs) with datacube metadata using the DEA naming conventions
for files.
Options:
--level1 FILE Optional path to the input level1 metadata
doc (otherwise it will be loaded from the
level1 path in the HDF5)
--output DIRECTORY Put the output package into this directory
[required]
-p, --product [nbar|nbart|lambertian|sbt]
Package only the given products (can specify
multiple times)
--with-oa / --no-oa Include observation attributes (default:
true)
--with-oa / --no-oa Include observation attributes (default:
true)
--oa-resolution FLOAT Resolution choice for observation attributes
(default: automatic based on sensor)
--help Show this message and exit.
Run the tests using pytest.
❯ pytest
You may need to install test dependencies first:
❯ pip install -e .[test]
Dependencies such as gdal can be tricky to install on some systems. You
may prefer to use the included Docker file for development: run make build to create a container, and make test to run tests.
We have strict linting and formatting checks on this reposistory, so please run pre-commit (below) after checkout.
❯ pip install pre-commit
❯ pre-commit install
(if you are using Conda, you need to conda install pre_commit instead of using pip)
Your code will now be formatted and validated before each commit. You can also invoke it manually by running pre-commit run
This allows you to immediately catch and fix issues before you raise a pull request that fails.
Most notably, all code is formatted using black, and checked with pyflakes.
First, draft some release notes for users of the library.
Now tag and upload:
# Be up-to-date.
git fetch origin
# Create a tag for the new version
# (using semantic versioning https://semver.org/)
git tag eodatasets3-<version> origin/eodatasets3
# Create package
python3 setup.py sdist bdist_wheel
# Upload it (Jeremy, Damien, Kirill have pypi ownership)
python3 -m twine upload dist/*
# Push tag to main repository
git push origin --tags