Argo is an international program that collects information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents and move up and down between the surface and a mid-water level. Each instrument (float) spends almost all its life below the surface. The name Argo was chosen because the array of floats works in partnership with the Jason earth observing satellites that measure the shape of the ocean surface. (In Greek mythology Jason sailed on his ship the Argo in search of the golden fleece). To learn more about Argo, how it works, its data and technology, and its scientific and environmental impact, click here.
The argofloats tool builds on the argovis API and allows the user to perform basic operations like search for and export platform and profile data, parse metadata and so on and more functionality will be added to this tool in the future.
Disclaimer: This is an unofficial tool. It is created and maintained by Samapriya Roy.
Samapriya Roy. (2022). samapriya/argofloats: Simple CLI for ArgoVis and Argofloats (0.0.7).
Zenodo. https://doi.org/10.5281/zenodo.6187858
These data were collected and made freely available by the International Argo Program and the national programs that contribute to it. (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System.
The general Argo DOI is below.
Argo (2000). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. https://doi.org/10.17882/42182
If you used data from a particular month, please add the month key to the end of the DOI url to make it reproducible. The key is comprised of the hashtag symbol (#) and then numbers. For example, the key for August 2020 is 76230. The citation would look like:
Argo (2020). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC) – Snapshot of Argo GDAC of August 2020. SEANOE. https://doi.org/10.17882/42182#76230
Argovis API was used to parse through and get to the datasets, you can cite argovis using the following
Tucker, T., D. Giglio, M. Scanderbeg, and S.S. Shen, 2020: Argovis: A Web Application for Fast Delivery,
Visualization, and Analysis of Argo Data. J. Atmos. Oceanic Technol., 37 (3), 401-416
https://doi.org/10.1175/JTECH-D-19-0041.1
Readme Docs available online
This assumes that you have native python & pip installed in your system, you can test this by going to the terminal (or windows command prompt) and trying
python and then pip list
argofloats only supports Python v3.4 or higher
This command line tool is dependent on functionality from GDAL For installing GDAL in Ubuntu
sudo add-apt-repository ppa:ubuntugis/ppa && sudo apt-get update
sudo apt-get install gdal-bin
sudo apt-get install python-gdal
Shapely and a few other libraries are notoriously difficult to install on windows machines so follow the steps mentioned here before installing argofloats. You can download and install shapely and other libraries from the Unofficial Wheel files from here download depending on the python version you have. Do this only once you have install GDAL. I would recommend the steps mentioned above to get the GDAL properly installed. However I am including instructions to using a precompiled version of GDAL similar to the other libraries on windows. You can test to see if you have gdal by simply running
gdalinfo
in your command prompt. If you get a read out and not an error message you are good to go. If you don't have gdal try Option 1,2 or 3 in that order and that will install gdal along with the other libraries
Simply run argofloats -h after installation. This should go fetch the extra libraries you need and install them. Once installation is complete, the porder help page will show up. This should save you from the few steps below.
If this does not work or you get an unexpected error try the following commands. You can also use these commands if you simply want to update these libraries.
pipwin refresh
pipwin install gdal
For Windows I also found this guide from UCLA
Also for Ubuntu Linux I saw that this is necessary before the install
sudo apt install libcurl4-openssl-dev libssl-dev
To install argofloats: Simple CLI for ArgoVis and Argofloats you can install using two methods.
pip install argofloats
or you can also try
git clone https://github.com/samapriya/argofloats.git
cd argofloats
python setup.py install
For Linux use sudo or try pip install argofloats --user.
I recommend installation within a virtual environment. Find more information on creating virtual environments here.
As usual, to print help:
argofloats -h
usage: argofloats [-h] {readme,overview,pm,plm,global-search,platform-profiles,profile-export} ...
Simple CLI for ArgoVis & Argofloats
positional arguments:
{readme,overview,pm,plm,global-search,platform-profiles,profile-export}
readme Go the web based porder readme page
overview Get overview of platforms and profiles
pm Get Platform metadata
plm Get Platform Profile metadata
global-search Global search reports using platform, profile or BGC type
platform-profiles Export all profiles for a given platform
profile-export Export profile based on Platform Profile ID, Lat, Long or Geometry GeoJSON file
optional arguments:
-h, --help show this help message and exit
To obtain help for specific functionality, simply call it with help switch, e.g.: argofloats pm -h. If you didn't install argofloats, then you can run it just by going to argofloats directory and running python argofloats.py [arguments go here]
The tool is designed to interact with the Argofloats Argovis API
The overview tool fetches the latest information about platform and profile count, last updated as well as the distribution of datasets across the different data centes or DACs. There are no arguments for this tool
The tool fetches metadata about a platform as pretty prints the information as a JSON object. This does require a Platform ID also called WMO number for the argo float.
Each platform consists of profiles where each profile is attached to a platform and is a single cycle of data collection. So platforms can have multiple profiles and generally represented as PlatformID_ProfileNo. This is the argument used by the tool and it pulls the metadata for that specific profile for that platform/argo float.
The global search tool was created to look for quick snapshots of all platforms and platforms in a single month. The tool allows you to provide a start and end date and it generates monthly reports pertaining to profiles that are globally available for a specific month and year. The tool can further use filters like setting bgc to True to get only those profiles/platforms that has BGC data or providing a specific platform ID to get only those profiles pertaining to specific platform.
You can provide a filter for example here bgc is set to true to look for all those platforms & profiles with BGC data
You can also search with no arguments or filters to get all profiles in a month and year
Each platform consists of profiles where each profile is attached to a platform and is a single cycle of data collection. So platforms can have multiple profiles and generally represented as PlatformID_ProfileNo. This tool fetches all platforms linked to a specific platform and exports it as a single CSV including platform profile id and measurements. If no path is provided the profile CSV is saved onto the home folder.
This tool allows you to search the argo floats database using either a lat long and a buffer area , or a geometry.geojson file or a given profile id. This tool is also capable to running long time searches overcoming the 3 month limit constrained by the argovis API. All use cases are shown below. The outputs are written as CSV file with a prefix argoprofile_
This uses the pofile ID and simply exports the profile as a CSV
This uses a lat long and radius to search in kilometers along with start and end date and exports all matching profiles to individual CSV files
This makes uses of a geojson file along with start and end date and exports all matching profiles to individual CSV files
- added BGC export for profiles apart from core export
- added Global search capability for quick filter and export using keywords
- Improved error handling
- Better readout if search yields nothing
- General improvements & file skipping if exists
- Added readme tool to open in browser
- Fixed relative import of geopandas.
- Added platform profiles export tool.
- Minor general improvements.
- Added readme to the overall tool.
- Improved profile metadata parsing
- Added version check for all future pypi versions
- Added readme and created new branch.
