Skip to content

Repository files navigation

OceanArray

PyPI Python versions License: MIT Tests Documentation DOI

Python tools for processing moored oceanographic array observations from raw instrument files to quality-controlled, CF-compliant NetCDF.

Documentation: https://ocean-uhh.github.io/oceanarray/

Installation

pip install oceanarray

This pulls in seasenselib (used to read raw instrument files in stage 1) automatically.

Python 3.10–3.12 is supported.

Quick start

Create a skeleton YAML, fill in the deployment metadata, then process:

oceanarray init dsG3_1_2026 --proc-dir /data/proc
# edit /data/proc/dsG3_1_2026/dsG3_1_2026.mooring.yaml
oceanarray validate /data/proc/dsG3_1_2026/dsG3_1_2026.mooring.yaml
oceanarray process dsG3_1_2026 --raw-dir /data/raw --proc-dir /data/proc --stage 1 2 3 stack grid
oceanarray report  dsG3_1_2026 --raw-dir /data/raw --proc-dir /data/proc --all

See the documentation for the recommended first-processing workflow, YAML reference, and CLI reference.

Python API

import oceanarray

oceanarray.process("dsG3_1_2026", raw_dir="/data/raw", proc_dir="/data/proc")

See the API reference for the full interface.

Acknowledgements

Development was assisted by Claude Code (Anthropic) and GitHub Copilot code review.

The data-processing approach draws on methods developed for the RAPID mooring array programme (UK/US funded). oceanarray is developed toward the DFG Ocean Array infrastructure project (DFG project number 571027118), from which it takes its name, and was first applied in the AEI–DFG MIXSED project (DFG project number 541914507).

License

MIT License

About

Processing pipeline for moored oceanographic instruments. Converts raw Sea-Bird, RBR, Nortek, and RDI files to CF-NetCDF with QARTOD QC, clock-drift correction, and self-contained HTML reports (no internet needed at view time).

Topics

Resources

Contributing

Stars

0 stars

Watchers

0 watching

Forks

Releases

Contributors

Languages