Various AWS helper methods

hacktoberfest, opendatacube, python3
pip install odc-cloud==0.2.5


Test Status codecov

DEA Prototype Code

This repository provides developmental libraries and CLI tools for Open Datacube.

  • AWS S3 tools
  • CLIs for using ODC data from AWS S3 and SQS
  • Utilities for data visualizations in notebooks
  • Experiments on optimising Rasterio usage on AWS S3

Full list of libraries, and install instructions:

  • odc.ui tools for data visualization in notebook/lab
  • odc.io common IO utilities, used by apps mainly
  • odc-cloud[ASYNC,AZURE,THREDDS] cloud crawling support package
    • odc.aws AWS/S3 utilities, used by apps mainly
    • odc.aio faster concurrent fetching from S3 with async, used by apps odc-cloud[ASYNC]
    • odc.{thredds,azure} internal libs for cloud IO odc-cloud[THREDDS,AZURE]

Promoted to their own repositories

  • odc.stats large scale processing framework (Moved to odc-stats)
  • odc.stac STAC to ODC conversion tools (Moved to odc-stac)
  • odc.dscache experimental key-value store where key=UUID, value=Dataset (moved to odc-dscache)


Libraries and applications in this repository are published to PyPI, and can be installed
with pip like so:

pip install \
  odc-ui \
  odc-stac \
  odc-stats \
  odc-io \
  odc-cloud[ASYNC] \

For Conda Users

Some odc-tools are available via conda from the conda-forge channel.

conda install -c conda-forge odc-apps-dc-tools odc-io odc-cloud 

Cloud Tools


Cloud tools depend on the aiobotocore package, which depends on specific versions of botocore. Another package we use, boto3, also depends on specific versions of botocore. As a result, having both aiobotocore and boto3 in one environment can be a bit tricky. The way to solve this is to install aiobotocore[awscli,boto3] before anything else, which will install compatible versions of boto3 and awscli into the environment.

pip install -U "aiobotocore[awscli,boto3]==1.3.3"
# OR for conda setups
conda install "aiobotocore==1.3.3" boto3 awscli
  1. For cloud (AWS only)
    pip install odc-apps-cloud
  2. For cloud (GCP, THREDDS and AWS)
    pip install odc-apps-cloud[GCP,THREDDS]
  3. For dc-index-from-tar (indexing to datacube from tar archive)
    pip install odc-apps-dc-tools


  1. s3-find list S3 bucket with wildcard
  2. s3-to-tar fetch documents from S3 and dump them to a tar archive
  3. gs-to-tar search GS for documents and dump them to a tar archive
  4. dc-index-from-tar read yaml documents from a tar archive and add them to datacube




s3-find "${s3_src}" | \
  s3-to-tar | \
    dc-index-from-tar --env s2 --ignore-lineage

Fastest way to list regularly placed files is to use fixed depth listing:


# only works when your metadata is same depth and has fixed file name

s3-find --skip-check "${s3_src}" | \
  s3-to-tar | \
    dc-index-from-tar --env s2 --ignore-lineage

When using Google Storage:


# Google Storage support
gs-to-tar --bucket data.deadev.com --prefix mangrove_cover
dc-index-from-tar --protocol gs --env mangroves --ignore-lineage metadata.tar.gz

Local Development

The following steps are used in the GitHub Actions workflow main.yml

# build environment from file
mamba env create -f tests/test-env.yml

# this environment name is defined in tests/test-env.yml file
conda activate odc-tools-tests

# install additional packages
./scripts/dev-install.sh --no-deps

# setup database for testing

# run test
echo "Running Tests"
pytest --cov=. \
--cov-report=html \
--cov-report=xml:coverage.xml \
--timeout=30 \
libs apps

# Optional, to delete the environment
conda env remove -n odc-tools-tests

Use conda env update -f <file> to install all needed dependencies for odc-tools libraries and apps.

Conda `environment.yaml` (click to expand)
  - conda-forge
  # Datacube
  - datacube>=1.8.5

  # odc.dscache
  - python-lmdb
  - zstandard

  # odc.ui
  - ipywidgets
  - ipyleaflet
  - tqdm

  # odc-apps-dc-tools
  - pystac>=1
  - pystac-client>=0.2.0
  - azure-storage-blob
  - fsspec
  - lxml  # needed for thredds-crawler

  # odc.{aio,aws}: aiobotocore/boto3
  #  pin aiobotocore for easier resolution of dependencies
  - aiobotocore==1.3.3
  - boto3

  # eodatasets3 (used by odc-stats)
  - boltons
  - ciso8601
  - python-rapidjson
  - requests-cache
  - ruamel.yaml
  - structlog
  - url-normalize

  # for dev
  - pylint
  - autopep8
  - flake8
  - isort
  - black
  - mypy

  # For tests
  - pytest
  - pytest-httpserver
  - pytest-cov
  - pytest-timeout
  - moto
  - deepdiff

  - pip>=20
  - pip:
      # odc.apps.dc-tools
      - thredds-crawler

      # odc.stats
      - eodatasets3

      # tests
      - pytest-depends

      # odc.ui
      - jupyter-ui-poll

      # odc-tools libs
      - odc-stac
      - odc-ui
      - odc-dscache
      - odc-stats

      # odc-tools CLI apps
      - odc-apps-cloud
      - odc-apps-dc-tools

Release Process

  1. Manually edit {lib,app}/{pkg}/odc/{pkg}/_version.py file to increase version number
  2. Merge changes to the develop branch via a Pull Request
  3. Fast-forward the pypi/publish branch to match develop
  4. Push to GitHub

Steps 3 and 4 can be done by an authorized user with ./scripts/sync-publish-branch.sh script.

Publishing to PyPi happens automatically when changes are pushed to the protected pypi/publish branch. Only members of Open Datacube Admins group have the permission to push to this branch.