CONTRIBUTING.md

Contributing

Thanks for helping with the ARCEME Data Cube Pipeline. This guide describes how to set up the environment, run the pipeline, and propose changes.

Scope

• Main code lives in src/processor.
• Keep large outputs (Zarr, logs, data cubes) outside the repo in the configured output directory.
• Avoid committing credentials or generated artifacts.

Requirements

• Python 3.11+
• uv (https://astral.sh/uv/)

Setup

cd /home/eouser/datacubes/data-cubes-arceme
uv sync

Configuration

• Default config: src/processor/pipeline_config.yaml
• Quick local run: src/processor/test_config.yaml
• Custom config: pass --config /path/to/file.yaml

Create a local .env with S3 credentials (do not commit real secrets). See README.md for the full template and endpoint notes.

Running

uv run python src/processor/pipeline_orchestrator.py

Custom config:

uv run python src/processor/pipeline_orchestrator.py --config src/processor/test_config.yaml

Tests / checks

There is a simple cloud-mask smoke script:

uv run python test/senselv_tests.py

For a pipeline smoke run, use src/processor/test_config.yaml to keep runtime short.

Dependencies

Dependencies are managed with uv.

• Add/update: uv add <package>
• Sync lockfile: uv sync
• Commit changes to pyproject.toml and uv.lock together.

Submitting changes

• Keep changes focused and describe how to reproduce or validate.
• Update README.md when adding new options or workflow steps.
• If you touch configs or outputs, note the config used and the expected output location.