[2]:

import argopy
from argopy import DataFetcher as ArgoDataFetcher

/home/docs/checkouts/readthedocs.org/user_builds/argopy/checkouts/v0.1.3/argopy/plotters.py:31: UserWarning: argopy requires cartopy installed for full map plotting functionality
  warnings.warn("argopy requires cartopy installed for full map plotting functionality")

Data sources¶

Selecting a source¶

argopy can get access to Argo data from different sources:

the Ifremer erddap server.

The erddap server database is updated daily and doesn’t require you to download anymore data than what you need.

You can select this data source with the keyword errdap and methods described below.
your local collection of Argo files, organised as in the GDAC ftp.

This is how you would use argopy with your data, as long as they are formated and organised the Argo way.

You can select this data source with the keyword localftp and methods described below.

You have several ways to specify which data source you want to use:

using argopy global options:

[3]:

argopy.set_options(src='erddap')

[3]:

<argopy.options.set_options at 0x7f989a046f98>

in a temporary context:

[4]:

with argopy.set_options(src='erddap'):
    loader = ArgoDataFetcher().profile(6902746, 34)

with an argument in the data fetcher:

[5]:

loader = ArgoDataFetcher(src='erddap').profile(6902746, 34)

Setting a local copy of the GDAC ftp¶

Data fetching with the localftp data source will require you to specify the path toward your local copy of the GDAC ftp server with the local_ftp option.

This is not an issue for expert users, but standard users may wonder how to set this up. The primary distribution point for Argo data, the only one with full support from data centers and with nearly a 100% time availability, is the GDAC ftp. Two mirror servers are available:

France Coriolis: ftp://ftp.ifremer.fr/ifremer/argo
US GODAE: ftp://usgodae.org/pub/outgoing/argo

If you want to get your own copy of the ftp server content, Ifremer provides a nice rsync service. The rsync server “vdmzrs.ifremer.fr” provides a synchronization service between the “dac” directory of the GDAC and a user mirror. The “dac” index files are also available from “argo-index”.

From the user side, the rsync service:

Downloads the new files
Downloads the updated files
Removes the files that have been removed from the GDAC
Compresses/uncompresses the files during the transfer
Preserves the files creation/update dates
Lists all the files that have been transferred (easy to use for a user side post-processing)

To synchronize the whole dac directory of the Argo GDAC:

rsync -avzh --delete vdmzrs.ifremer.fr::argo/ /home/mydirectory/...

To synchronize the index:

rsync -avzh --delete vdmzrs.ifremer.fr::argo-index/ /home/mydirectory/...

Note

The first synchronisation of the whole dac directory of the Argo GDAC (365Gb) can take quite a long time (several hours).

Comparing data sources¶

You may wonder if the fetched data are different from the available data sources.

This will depend on the last update of your local data.

Let’s retrieve one float data from a local sample of the GDAC ftp (a sample GDAC ftp is downloaded automatically with the method argopy.tutorial.open_dataset()):

[6]:

# Download ftp sample and get the ftp local path:
ftproot, flist = argopy.tutorial.open_dataset('localftp')

# then fetch data:
with argopy.set_options(src='localftp', local_ftp=os.path.join(ftproot, 'dac')):
    ds = ArgoDataFetcher().float(1900857).to_xarray()
    print(ds)

<xarray.Dataset>
Dimensions:          (N_POINTS: 20966)
Coordinates:
  * N_POINTS         (N_POINTS) int64 0 1 2 3 4 ... 20962 20963 20964 20965
    LONGITUDE        (N_POINTS) float64 10.81 10.81 10.81 ... 92.65 92.65 92.65
    TIME             (N_POINTS) datetime64[ns] 2008-02-25T04:03:00 ... 2013-06-01T05:34:00
    LATITUDE         (N_POINTS) float64 -39.93 -39.93 -39.93 ... -44.16 -44.16
Data variables:
    CYCLE_NUMBER     (N_POINTS) int64 0 0 0 0 0 0 0 ... 192 192 192 192 192 192
    DATA_MODE        (N_POINTS) <U1 'D' 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D' 'D'
    DIRECTION        (N_POINTS) <U1 'D' 'D' 'D' 'D' 'D' ... 'A' 'A' 'A' 'A' 'A'
    PLATFORM_NUMBER  (N_POINTS) int64 1900857 1900857 ... 1900857 1900857
    POSITION_QC      (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    PRES             (N_POINTS) float64 17.0 25.0 35.0 ... 1.964e+03 1.987e+03
    PRES_QC          (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    PSAL             (N_POINTS) float64 34.68 34.68 34.69 ... 34.71 34.71 34.72
    PSAL_QC          (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    TEMP             (N_POINTS) float64 16.14 16.14 16.03 ... 2.431 2.422 2.413
    TEMP_QC          (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    TIME_QC          (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
Attributes:
    DATA_ID:              ARGO
    DOI:                  http://doi.org/10.17882/42182
    Fetched_from:         /home/docs/.argopy_tutorial_data/ftp/dac
    Fetched_by:           docs
    Fetched_date:         2020/05/15
    Fetched_constraints:  WMO1900857
    Fetched_url:          /home/docs/.argopy_tutorial_data/ftp/dac/coriolis/1...
    history:              Variables filtered according to DATA_MODE; Variable...

Let’s now retrieve the latest data for this float from the erddap:

[7]:

with argopy.set_options(src='erddap'):
    ds = ArgoDataFetcher().float(1900857).to_xarray()
    print(ds)

<xarray.Dataset>
Dimensions:          (N_POINTS: 20966)
Coordinates:
  * N_POINTS         (N_POINTS) int64 0 1 2 3 4 ... 20962 20963 20964 20965
    LONGITUDE        (N_POINTS) float64 10.81 10.81 10.81 ... 92.65 92.65 92.65
    TIME             (N_POINTS) datetime64[ns] 2008-02-25T04:03:00 ... 2013-06-01T05:34:00
    LATITUDE         (N_POINTS) float64 -39.93 -39.93 -39.93 ... -44.16 -44.16
Data variables:
    CYCLE_NUMBER     (N_POINTS) int64 0 0 0 0 0 0 0 ... 192 192 192 192 192 192
    DATA_MODE        (N_POINTS) <U1 'D' 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D' 'D'
    DIRECTION        (N_POINTS) <U1 'D' 'D' 'D' 'D' 'D' ... 'A' 'A' 'A' 'A' 'A'
    PLATFORM_NUMBER  (N_POINTS) int64 1900857 1900857 ... 1900857 1900857
    POSITION_QC      (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    PRES             (N_POINTS) float64 17.0 25.0 35.0 ... 1.964e+03 1.987e+03
    PRES_QC          (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    PSAL             (N_POINTS) float64 34.68 34.68 34.69 ... 34.71 34.71 34.72
    PSAL_QC          (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    TEMP             (N_POINTS) float64 16.14 16.14 16.03 ... 2.431 2.422 2.413
    TEMP_QC          (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    TIME_QC          (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
Attributes:
    DATA_ID:              ARGO
    DOI:                  http://doi.org/10.17882/42182
    Fetched_from:         http://www.ifremer.fr/erddap
    Fetched_by:           docs
    Fetched_date:         2020/05/15
    Fetched_constraints:  phy_WMO1900857
    Fetched_url:          http://www.ifremer.fr/erddap/tabledap/ArgoFloats.cs...
    history:              Variables filtered according to DATA_MODE; Variable...