MOC transports

Clean, modular loading of AMOC observing array datasets, with optional structured logging and metadata enrichment.

AMOCarray provides a unified system to access and process data from major Atlantic Meridional Overturning Circulation (AMOC) observing arrays: - MOVE (16°N) - RAPID (26°N) - OSNAP (Subpolar North Atlantic) - SAMBA (34.5°S)

The project emphasizes clarity, reproducibility, and modular design, with per-dataset logging, metadata handling, and testable utilities.

This is a work in progress, all contributions welcome!

Install

Install from PyPI (https://pypi.org/project/amocarray/) with sh python -m pip install amocarray

Documentation

Documentation is available at https://amoccommunity.github.io/amocarray.

Check out the demo notebook notebooks/demo.ipynb for example functionality.

As input, amocarray downloads data from the observing arrays.

Quickstart

Load a sample dataset

```python from amocarray import readers

Load RAPID sample dataset

ds = readers.loadsampledataset("rapid") print(ds) ```

Load a full dataset

```python from amocarray import readers

datasets = readers.load_dataset("osnap") for ds in datasets: print(ds) `` A*.logfile will be written tologs/` by default.

Data will be cached in ~/.amocarray_data/ unless you specify a custom location.

Project structure

amocarray/ │ ├── readers.py # Orchestrator for loading datasets ├── read_move.py # MOVE reader ├── read_rapid.py # RAPID reader ├── read_osnap.py # OSNAP reader ├── read_samba.py # SAMBA reader │ ├── utilities.py # Shared utilities (downloads, parsing, etc.) ├── logger.py # Structured logging setup │ └── tests/ # Unit tests

Roadmap

• [ ] Add test coverage for utilities and readers
• [ ] Add dataset summary output at end of load_dataset()
• [x] Optional global logging helpers (disablelogging(), enablelogging())
• [ ] Extend loadsampledataset() to support all arrays
• [x] Metadata enrichment (source paths, processing dates)
• [ ] Clarify separation between added metadata and original metadata

Contributing

All contributions are welcome! See contributing for more details.

To install a local, development version of amocarray, clone the repository, open a terminal in the root directory (next to this readme file) and run these commands:

sh git clone https://github.com/AMOCcommunity/amocarray.git cd amocarray pip install -r requirements-dev.txt pip install -e . This installs amocarray locally. The -e ensures that any edits you make in the files will be picked up by scripts that impport functions from glidertest.

You can run the example jupyter notebook by launching jupyterlab with jupyter-lab and navigating to the notebooks directory, or in VS Code or another python GUI.

All new functions should include tests. You can run tests locally and generate a coverage reporrt with: sh pytest --cov=amocarray --cov-report term-missing tests/

Try to ensure that all the lines of your contribution are covered in the tests.

 Initial plans

The initial plan for this repository is to simply load the volume transports as published by different AMOC observing arrays and replicate (update) the figure from Frajka-Williams et al. (2019) 10.3389/fmars.2019.00260.

Acknowledgements

• MOVE data: NOAA Climate Program Office and German Bundesministerium für Bildung und Forschung.
• OSNAP data: www.o-snap.org
• SAMBA data: NOAA AOML, Met Office, and associated research projects.
• RAPID data: RAPID-MOCHA-WBTS collaboration.

Dataset access and processing via AMOCarray.