DestinE Jupyter Notebook Tutorials for Insula Code Lab

Welcome to the repository for DestinE Platform Jupyter Notebook Tutorials!

This repository is designed to guide users through the DestinE Platform Services, exploiting DestinE Digital Twin data as well as data from many other data sources within the Insula Code Lab Service interactive Jupyter environment.

➡️ Register on the Destination Earth Platform and start using Jupyter Notebooks on Insula Code Lab! Example notebooks are seamlessly integrated into the default Python environment for all registered users.

⚠️ To start exploiting DestinE Digital Twin data please make sure to request the upgraded access permission by visiting this link.

Jupyter Notebooks Examples

All examples are written in Python and are designed to work seamlessly with any DestinE Platform Data Access service.

• Access DestinE Climate Adaptation Digital Twin data on the cacheb ➡️ upgraded access required.
• Discover DestinE Climate Adaptation Digital Twin Data Streams on DestinEStreamer ➡️ upgraded access required.
• Discover and access Copernicus ERA5 data with Earth Data Hub Service examples.
• Access data on the data lake via the EDEN service example.
• Try out DestinE Climate Adaptation Digital Twin scenarios in Insula ➡️ upgraded access required.
• Compute the Standard Evapotranspiration variable from ERA5 data using the Drought Assessment example.
• Create a DEA data story using the service example notebook.
• Try out destinepyauth for harmonized authentication against DestinE platform services.

Notebook templates are all a quickstart to DestinE Platform services. Insula Code Lab includes also ECMWF's Polytope and EUMETSAT's HDA examples in the user's own environment.

Stay tuned for more contents and feel free to contribute!

Credits

• Earth Data Hub tutorials and Cache-B original content created by nicolamasotti from B-Open.
• EDEN notebooks original content created by MEEO.
• DestineStreamer examples created by SamCarraro from GeoVille.
• DEA tutorials created by Alia Space Systems.
• Insula contributions by Bea07, albeCGI, and crossi202 from CGI.
• destinepyauth tutorial created by purnelldj from Serco.

The CodeLab Environment

The CodeLab environment includes some Python packages pre-installed in the user's environment. The full list of dependencies is provided in the file requirements.txt.

Note: Pre-installed Python packages listed in this file provide a snapshot of dependencies needed to run the example notebooks provided in this repository.

Install new packages

To install new packages persistently in the coding environment, users can create their own virtual environment in Insula Code Lab by following the guidelines below.

Create a new environment

python -m venv /home/jovyan/my_env

Alternatively, if you also want access to the platform’s pre-installed packages from within the environment, add the --system-site-packages flag. Omit it to start from a clean environment.

python -m venv /home/jovyan/my_env --system-site-packages

Activate the new environment

source /home/jovyan/my_env/bin/activate

Install a new package

Install ipykernel together with your package so the environment can register its own notebook kernel. Always invoke pip as python -m pip, so packages install into the active environment.

python -m pip install ipykernel dvc # dvc is an arbitrary package in this example.

Note: If you install a package while a notebook is already running on that environment’s kernel, restart the kernel before you can import and use the new package. A running kernel does not pick up packages added after it started.

Enable the environment for usage in a notebook’s kernel

python -m ipykernel install --user --name=my_env

then refresh the browser.

Note: Register the kernel with python -m ipykernel, not ipython kernel install. The latter may register the Conda ipython instead of the environment’s Python: the package would be installed into my_env, while the notebook kernel would still start with /opt/conda/bin/python, so the environment is ignored and import raises a ModuleNotFoundError. Using python -m ipykernel binds the kernel to the environment’s interpreter and avoids this mismatch.

Contributing

Contributions are welcome! Because this repository is made of Jupyter notebooks, we use pre-commit hooks to keep diffs reviewable (stripping volatile notebook metadata while keeping figures), to lint code cells, and to scan for accidentally committed secrets.

Before opening a pull request:

1. Install and enable the hooks (one-time, per clone):

   pip install pre-commit
   pre-commit install
   

2. Run the hooks against everything and fix anything they flag:

   pre-commit run --all-files
   

   Some hooks (e.g. notebook metadata stripping) edit files in place — just re-stage the changes and commit again.

The same checks run automatically in CI on every pull request, so running them locally first avoids a failed build.

Contact

If you have questions or need support with these examples contact the ➡️ DestinE support.