Usage

Most workflows run through the Jupyter notebooks under notebooks/production/. The src/ package provides the underlying library (data download, processing, forecasting, and output checks) documented in the API reference.

Running Jupyter Lab

After setting up and activating the nbs_env environment:

jupyter lab

Working with the notebooks

1. In Jupyter Lab, navigate to notebooks/production/.

2. Open the appropriate notebook. There are separate notebooks for:

   • Training a forecast model (not needed for most users),

   • Downloading and preprocessing input data from NOAA CFS and other sources,

   • Generating forecasts (e.g. 2_LEF_forecast_model.ipynb).

3. Set your directory paths in the User Input section near the top.

4. Run the notebook to generate forecasts.

Inputs and data sources

Forecasts require NOAA CFS atmospheric forecast data and GLSEA sea-surface temperatures as initial conditions. The download/preprocessing notebooks handle acquiring and shaping both. See the README “Data Sources” table for the full list of datasets and how they are used (forecasting vs. training).

Running the tests

The library has an automated test suite. To run the fast, offline tests:

pytest -m "not network"

Tests that reach live NOAA/AWS endpoints are marked network and can be run explicitly with pytest -m network. On pull requests, continuous integration runs the offline suite automatically. See CONTRIBUTING.md for details.