Top 3 Python oceanography Projects

iris

1 615 9.5 Python

A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data (by SciTools)
VIAME

1 264 9.4 Python

Video and Image Analytics for Multiple Environments
WorkOS

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The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
clouddrift

1 32 8.8 Python

CloudDrift accelerates the use of Lagrangian data for atmospheric, oceanic, and climate sciences.

Project mention: Show HN: Clouddrift (Python lib) – 170 years of Cyclone Track Data | news.ycombinator.com | 2024-04-16

Hey HN! I've recently taken on the role of lead developer for the clouddrift Python library, and I'd like to share a new dataset integration we've recently added. The purpose of the clouddrift library is to ease the process of working with ragged array data structures, particularly the ones encountered with Lagrangian datasets in atmospheric, oceanic, and climate sciences.
For those who might not be familiar, Lagrangian datasets track the movement of sensing platforms or particles (or in the case below, atmospheric phenomena like cyclones) through space and time, providing a detailed path of their journey. This is in contrast to Eulerian datasets, which focus on changes in a fixed location.
The new dataset we've incorporated is the HURDAT2 dataset [0] , offering access to cyclone track data from 1852 to 2022 for both the Atlantic and Pacific Ocean basins. This dataset is provided as an xarray [1] Dataset utilizing CF (Climate and Forecast) conventions [2] for data variables that are organized as a Ragged Arrays [3].
To get started with `clouddrift` and the HURDAT2 dataset, check out our example Jupyter notebooks [4].
If you'd like to receive regular updates on each release or just want to get an idea of features we've been working on checkout our latest [5].
I'd love to hear from HN. How do you see clouddrift working with ragged array datasets of any kind you may be working with? Are there any challenges created by working with ragged arrays and/or Lagrangian datasets you have faced which clouddrift may not be accounting for? Any and all feedback is welcome as we look to make this tool as useful as possible for the scientific community!
[0] https://github.com/Cloud-Drift/clouddrift/blob/main/clouddri...

NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).