luzpaz/argopy

A python library for Argo data beginners and experts

	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
Documentation
Usage
Release
Development
Data resources

Install

Install the last release with pip:

pip install argopy

But since this is a young library in active development, use direct install 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) and with python versions 3.7 and 3.8.

Usage

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')    

See the documentation page for more examples.

Development roadmap

Our next big steps:

• To provide Bio-geochemical variables (#22, #77, #81)
• To develop expert methods related to Quality Control of the data with other python softwares like:
  • pyowc: #33, #53
  • bgcArgoDMQC: #37

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 (local and online)
• Argovis
• Online GDAC ftp

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

• Map with trajectories
• Histograms for meta-data
• Waterfall plots
• T/S diagram