argopy 0.1.1

A python library for Argo data beginners and experts

Argo data python library

argopy is a python library that aims to ease Argo data access, visualisation and manipulation for regular users as well as Argo experts and operators.

Several python packages exist: we are currently in the process of analysing how to merge these libraries toward a single powerfull tool.
List your tool here !

Click here to and play with argopy before you even install it (thanks Pangeo).

Install

Since this is a library in active development, use direct install from this repo to benefit from the last version:

pip install git+http://github.com/euroargodev/argopy.git@master

The argopy library should work under all OS (Linux, Mac and Windows) and with python versions 3.6, 3.7 and 3.8.

Usage

Fetching Argo Data

Init the default data fetcher like:

from argopy import DataFetcher as ArgoDataFetcher
argo_loader = ArgoDataFetcher()

and then, request data for a specific space/time domain:

ds = argo_loader.region([-85,-45,10.,20.,0,10.]).to_xarray()
ds = argo_loader.region([-85,-45,10.,20.,0,1000.,'2012-01','2012-12']).to_xarray()

for profiles of a given float:

ds = argo_loader.profile(6902746, 34).to_xarray()
ds = argo_loader.profile(6902746, np.arange(12,45)).to_xarray()
ds = argo_loader.profile(6902746, [1,12]).to_xarray()

or for one or a collection of floats:

ds = argo_loader.float(6902746).to_xarray()
ds = argo_loader.float([6902746, 6902747, 6902757, 6902766]).to_xarray()

Two Argo data fetchers are available.

1. The Ifremer erddap (recommended, but requires internet connection):

   argo_loader = ArgoDataFetcher(backend='erddap')
   ds = argo_loader.profile(6902746, 34).to_xarray()
   

2. your own local copy of the GDAC ftp (offline access possible, but more limited than the erddap).

   argo_loader = ArgoDataFetcher(backend='localftp', path_ftp='/path/to/your/copy/of/Argo/ftp/dac')
   ds = argo_loader.float(6902746).to_xarray()
   

Data manipulation

Data are returned as a collection of measurements. If you want to convert them into a collection of profiles, you can use the xarray accessor named argo:

from argopy import DataFetcher as ArgoDataFetcher
ds = ArgoDataFetcher().float(5903248).to_xarray() # Dimensions: (N_POINTS: 25656)
ds = ds.argo.point2profile() # Dimensions: (N_LEVELS: 71, N_PROF: 368)

By default fetched data are returned in memory as xarray.DataSet. From there, it is easy to convert it to other formats like a Pandas dataframe:

ds = ArgoDataFetcher().profile(6902746, 34).to_xarray()
df = ds.to_dataframe()

or to export it to files:

ds = argo_loader.region([-85,-45,10.,20.,0,100.]).to_xarray()
ds.to_netcdf('my_selection.nc')
# or by profiles:
ds.argo.point2profile().to_netcdf('my_selection.nc')

Development roadmap

We aim to provide high level helper methods to load Argo data and meta-data from:

• Ifremer erddap
• local copy of the GDAC ftp folder
• Index files (ongoing work here)
• the argovis dataset (help wanted here)
• any other usefull access point to Argo data ?

We also aim to provide high level helper methods to visualise and plot Argo data and meta-data:

• Map with trajectories
• Waterfall plots
• T/S diagram
• etc !