A python library for Argo data beginners and experts


Keywords
argo, argo-data, argo-floats, oceanography, python
License
Apache-2.0
Install
pip install argopy==0.1.12

Documentation

argopy logo 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
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Install

Install the last release with conda:

conda install -c conda-forge argopy

or pip:

pip install argopy

But since this is a young library in active development, use direct installation from this repo to benefit from the latest version:

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

The argopy library is tested to work under most OS (Linux, Mac, Windows) and with python versions 3.7 and 3.8.

Usage

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Fetching Argo Data

Import the data fetcher:

from argopy import DataFetcher as ArgoDataFetcher

and then, set it up to request data for a specific space/time domain:

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

for profiles of a given float:

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

or for one or a collection of floats:

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

Once your fetcher is initialized you can trigger fetch/load data like this:

ds = argo_loader.to_xarray()  # or:
ds = argo_loader.load().data

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:

df = ArgoDataFetcher().profile(6902746, 34).load().data.to_dataframe()

or to export it to files:

ds = ArgoDataFetcher().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')

Fetching only Argo index

Argo index are returned as pandas dataframe. Index fetchers works similarly to data fetchers.

Load the Argo index fetcher:

    from argopy import IndexFetcher as ArgoIndexFetcher

then, set it up to request index for a specific space/time domain:

    index_loader = ArgoIndexFetcher().region([-85,-45,10.,20.])
    index_loader = ArgoIndexFetcher().region([-85,-45,10.,20.,'2012-01','2014-12'])

or for one or a collection of floats:

    index_loader = ArgoIndexFetcher().float(6902746)
    index_loader = ArgoIndexFetcher().float([6902746, 6902747, 6902757, 6902766])   

Once your fetcher is initialized you can trigger fetch/load index like this:

    df = index_loader.to_dataframe()  # or
    df = index_loader.load().index

Note that like the data fetcher, the index fetcher can use different sources, a local copy of the GDAC ftp for instance:

    index_fetcher = ArgoIndexFetcher(src='localftp', path_ftp='/path/to/your/argo/ftp/', index_file='ar_index_global_prof.txt')

Visualisation

For plottings methods, you'll need matplotlib and possibly cartopy and seaborn installed. Argo Data and Index fetchers provide direct plotting methods, for instance:

    ArgoDataFetcher().float([6902745, 6902746]).plot('trajectory')    

index_traj

See the documentation page for more examples.

Development roadmap

See milestone here: https://github.com/euroargodev/argopy/milestone/3