Data sources¶
Let’s start with standard import:
In [1]: import argopy
In [2]: from argopy import DataFetcher as ArgoDataFetcher
Selecting a source¶
argopy can get access to Argo data from different sources:
-
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
erddapand methods described below. The Ifremer erddap dataset is based on mono-profile files of the GDAC. 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 formatted and organised the Argo way.You can select this data source with the keywordlocalftpand methods described below.the Argovis server.
The Argovis server database is updated daily and provides access to curated Argo data (QC=1 only). You can select this data source with the keyword
argovisand methods described below.
You have several ways to specify which data source you want to use:
using argopy global options:
In [3]: argopy.set_options(src='erddap')
Out[3]: <argopy.options.set_options at 0x7fe1f1531310>
in a temporary context:
In [4]: with argopy.set_options(src='erddap'):
...: loader = ArgoDataFetcher().profile(6902746, 34)
...:
with an argument in the data fetcher:
In [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¶
Features¶
Each of the available data sources have their own features and capabilities. Here is a summary:
Data source: |
erddap |
localftp |
argovis |
|---|---|---|---|
Access Points |
|||
region |
X |
X |
X |
float |
X |
X |
X |
profile |
X |
X |
X |
User mode |
|||
standard |
X |
X |
X |
expert |
X |
X |
|
Dataset |
|||
core (T/S) |
X |
X |
X |
BGC |
|||
Reference data for DMQC |
X |
||
Parallel method |
|||
multi-threading |
X |
X |
X |
multi-processes |
X |
||
Dask client |
Fetched data and variables¶
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()):
# Download ftp sample and get the ftp local path:
In [6]: ftproot = argopy.tutorial.open_dataset('localftp')[0]
# then fetch data:
In [7]: with argopy.set_options(src='localftp', local_ftp=ftproot):
...: ds = ArgoDataFetcher().float(1900857).to_xarray()
...: print(ds)
...:
<xarray.Dataset>
Dimensions: (N_POINTS: 20966)
Coordinates:
* N_POINTS (N_POINTS) int64 0 1 2 3 ... 20962 20963 20964 20965
TIME (N_POINTS) datetime64[ns] 2008-02-25T04:03:00 ... ...
LATITUDE (N_POINTS) float64 -39.93 -39.93 ... -44.16 -44.16
LONGITUDE (N_POINTS) float64 10.81 10.81 10.81 ... 92.65 92.65
Data variables: (12/13)
CONFIG_MISSION_NUMBER (N_POINTS) int64 1 1 1 1 1 1 1 1 ... 2 2 2 2 2 2 2 2
CYCLE_NUMBER (N_POINTS) int64 0 0 0 0 0 0 ... 192 192 192 192 192
DATA_MODE (N_POINTS) <U1 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D'
DIRECTION (N_POINTS) <U1 'D' 'D' 'D' 'D' ... '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
... ...
PRES_QC (N_POINTS) int64 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 34.72
PSAL_QC (N_POINTS) int64 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 ... 2.422 2.413
TEMP_QC (N_POINTS) int64 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
Attributes:
DATA_ID: ARGO
DOI: http://doi.org/10.17882/42182
Fetched_from: /home/docs/.argopy_tutorial_data/ftp
Fetched_by: docs
Fetched_date: 2021/11/02
Fetched_constraints: phy;WMO1900857
Fetched_uri: /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 and argovis sources:
In [8]: 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 ... 20962 20963 20964 20965
LATITUDE (N_POINTS) float64 -39.93 -39.93 ... -44.16 -44.16
LONGITUDE (N_POINTS) float64 10.81 10.81 10.81 ... 92.65 92.65
TIME (N_POINTS) datetime64[ns] 2008-02-25T04:03:00 ... ...
Data variables: (12/13)
CONFIG_MISSION_NUMBER (N_POINTS) int64 1 1 1 1 1 1 1 1 ... 2 2 2 2 2 2 2 2
CYCLE_NUMBER (N_POINTS) int64 0 0 0 0 0 0 ... 192 192 192 192 192
DATA_MODE (N_POINTS) <U1 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D'
DIRECTION (N_POINTS) <U1 'D' 'D' 'D' 'D' ... '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
... ...
PRES_QC (N_POINTS) int64 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 34.72
PSAL_QC (N_POINTS) int64 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 ... 2.422 2.413
TEMP_QC (N_POINTS) int64 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
Attributes:
DATA_ID: ARGO
DOI: http://doi.org/10.17882/42182
Fetched_from: https://www.ifremer.fr/erddap
Fetched_by: docs
Fetched_date: 2021/11/02
Fetched_constraints: phy;WMO1900857
Fetched_uri: ['https://www.ifremer.fr/erddap/tabledap/ArgoFloats...
history: Variables filtered according to DATA_MODE; Variable...
In [9]: with argopy.set_options(src='argovis'):
...: ds = ArgoDataFetcher().float(1900857).to_xarray()
...: print(ds)
...:
<xarray.Dataset>
Dimensions: (N_POINTS: 21029)
Coordinates:
* N_POINTS (N_POINTS) int64 0 1 2 3 4 ... 21025 21026 21027 21028
TIME (N_POINTS) object '2008-02-28T01:23:00.000Z' ... '2013-0...
LATITUDE (N_POINTS) float64 -40.02 -40.02 -40.02 ... -44.16 -44.16
LONGITUDE (N_POINTS) float64 10.54 10.54 10.54 ... 92.65 92.65 92.65
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 'A' 'A' 'A' 'A' 'A' ... '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) int64 16 26 37 45 55 ... 1913 1938 1964 1987
PSAL (N_POINTS) float64 34.74 34.73 34.67 ... 34.71 34.71 34.72
TEMP (N_POINTS) float64 16.69 16.59 15.92 ... 2.431 2.422 2.413
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: https://argovis.colorado.edu
Fetched_by: docs
Fetched_date: 2021/11/02
Fetched_constraints: phy;WMO1900857
Fetched_uri: ['https://argovis.colorado.edu/catalog/platforms/19...
We can see some minor differences between localftp/erddap vs the
argovis response: this later data source does not include the
descending part of the first profile, this explains why argovis
returns slightly less data.
Status of sources¶
With remote, online data sources, it may happens that the data server is experiencing down time. With local data sources, the availability of the path is checked when it is set. But it may happens that the path points to a disk that get unmounted or unplugged after the option setting.
If you’re running your analysis on a Jupyter notebook, you can use the argopy.status() method to insert a data status monitor on a cell output. All available data sources will be monitored continuously.
argopy.status()
If one of the data source become unavailable, you will see the status bar changing to something like:
Note that the argopy.status() method has a refresh option to let you specify the refresh rate in seconds of the monitoring.
Last, you can check out the following argopy status webpage that monitors all important resources to the software.