To help you get started, we've collected the most common ways that datacube is being used within popular public projects.

opendatacube / datacube-core / integration_tests / test_full_ingestion.py View on Github

def check_dataset_metadata_in_storage_unit(nco, dataset_dir):
    assert len(nco.variables['dataset']) == 1  # 1 time slice
    stored_metadata = nco.variables['dataset'][0]
    if not isinstance(stored_metadata, str):
        stored_metadata = netCDF4.chartostring(stored_metadata)
        stored_metadata = str(np.char.decode(stored_metadata))
    ds_filename = dataset_dir / 'agdc-metadata.yaml'

    stored = yaml.safe_load(stored_metadata)
    [(_, original)] = read_documents(ds_filename)
    assert len(stored['lineage']['source_datasets']) == 1
    assert next(iter(stored['lineage']['source_datasets'].values())) == original

View more ways to use datacube

Open Data Cube Core

%5D(https://discord.gg/4hhBQVas5U)

Overview

The Open Data Cube Core provides an integrated gridded data analysis environment for decades of analysis ready earth observation satellite and related data from multiple satellite and other acquisition systems.

Documentation

See the user guide for installation and usage of the datacube, and for documentation of the API.

Join our Discord if you need help setting up or using the Open Data Cube.

Please help us to keep the Open Data Cube community open and inclusive by reading and following our Code of Conduct.

Requirements

System

• PostgreSQL 10+
• Python 3.9+

Developer setup

1. Clone:

   • git clone https://github.com/opendatacube/datacube-core.git

2. Create a Python environment for using the ODC. We recommend Mambaforge as the easiest way to handle Python dependencies.

   mamba env create -f conda-environment.yml conda activate cubeenv

3. Install a develop version of datacube-core.

   cd datacube-core pip install --upgrade -e .

4. Install the pre-commit hooks to help follow ODC coding conventions when committing with git.

   pre-commit install

5. Run unit tests + PyLint

Install test dependencies using:

> pip install --upgrade -e '.[test]'

If install for these fails, please lodge them as issues.

Run unit tests with:

> ./check-code.sh > > (this script approximates what is run by GitHub Actions. You can > alternatively run pytest yourself).

1. (or) Run all tests, including integration tests.

   ./check-code.sh integration_tests

   • Assumes a password-less Postgres database running on localhost called

   pgintegration

   • Otherwise copy integration_tests/integration.conf to ~/.datacube_integration.conf and edit to customise.

   • For instructions on setting up a password-less Postgres database, see
     the developer setup instructions.

Alternatively one can use the opendatacube/datacube-tests docker image to run tests. This docker includes database server pre-configured for running integration tests. Add --with-docker command line option as a first argument to ./check-code.sh script.

./check-code.sh --with-docker integration_tests

To run individual tests in a docker container

docker build --tag=opendatacube/datacube-tests-local --no-cache --progress plain -f docker/Dockerfile .

docker run -ti -v $(pwd):/code opendatacube/datacube-tests-local:latest pytest integration_tests/test_filename.py::test_function_name

Developer setup on Ubuntu

Building a Python virtual environment on Ubuntu suitable for development work.

Install dependencies:

sudo apt-get update
sudo apt-get install -y \
    autoconf automake build-essential make cmake \
    graphviz \
    python3-venv \
    python3-dev \
    libpq-dev \
    libyaml-dev \
    libnetcdf-dev \
    libudunits2-dev

Build the python virtual environment:

pyenv="${HOME}/.envs/odc"  # Change to suit your needs
mkdir -p "${pyenv}"
python3 -m venv "${pyenv}"
source "${pyenv}/bin/activate"
pip install -U pip wheel cython numpy
pip install -e '.[dev]'
pip install flake8 mypy pylint autoflake black