A new version of argopy has been released!

argopy is a python library dedicated to Argo data access, manipulation, and visualisation for standard users as well as Argo experts. The library is collaboratively developed on the euroargodev github.

This new version (v0.1.14) of argopy is the first one with (early) support for BGC data.

The online documentation has been reorganised and a gallery of notebook examples has been added: https://argopy.readthedocs.io/en/v0.1.14/gallery.html

Here are the main new features available in v0.1.14, which you can install via pip or conda/mamba:

• The content of multi-profile synthetic BGC files is accessible via the DataFetcher class (in "expert" mode via Ifremer's erddap). As with the core dataset, you can retrieve data for a region, float(s) or profile(s). What's new compared with the core dataset is that you can restrict the search for data to certain BGC parameters and also impose no-NaNs on some of the parameters.
  https://argopy.readthedocs.io/en/v0.1.14/user-guide/fetching-argo-data/data_set.html#the-bgc-dataset

 

• The 2 Argo-BGC indexes are fully supported by the ArgoIndex class, with 2 new methods to searching the list of parameters and/or their data mode.
  https://argopy.readthedocs.io/en/v0.1.14/metadata_fetching.html#store-low-level-argo-index-access

 

• A new user mode “research” is available for deep and core Argo data. In this mode, you only get data recommended by the Argo Steering Team for research purposes (i.e. only Delayed Mode data with QC=1).
  https://argopy.readthedocs.io/en/v0.1.14/user-guide/fetching-argo-data/user_mode.html#user-mode

 

• Once argopy is installed in your Python environment a new xarray "engine='argo'" becomes available. It allows proper opening of NetCDF Argo files with xarray. All NetCDF Argo files are supported.    https://argopy.readthedocs.io/en/latest/generated/argopy.xarray.ArgoEngine.html#argopy.xarray.ArgoEngine

 

• Easier access to all ADMT Argo manuals thanks to the new ArgoDocs class. More than 20 Argo manuals documents (in .pdf) have been produced by the ADMT. Using the new ArgoDocs class, it is now easier to navigate this large database for Argo experts.    https://argopy.readthedocs.io/en/v0.1.14/metadata_fetching.html#admt-documentation

 

The complete list of improvements brought by this new argopy version is available in the documentation:
https://argopy.readthedocs.io/en/v0.1.14/whats-new.html#v0-1-14-29-sep-2023

And as always, do not hesitate to discuss, suggest, and report any bug on the argopy repository: https://github.com/euroargodev/argopy