Guillaume Maze gmaze

To get a list of WMO,CYC matching some QC position, you can do something like:

https://erddap.ifremer.fr/erddap/tabledap/ArgoFloats.htmlTable?platform_number,cycle_number,position_qc&platform_number=~%226903075|6903076%22&position_qc=~%221%22&distinct()&orderBy(%22platform_number,cycle_number%22)

This is an example where I select profiles from floats 6903075 and 6903076 having a QC position of 1

If you want to allow for more allowed QC, list them with a | in the request:

https://erddap.ifremer.fr/erddap/tabledap/ArgoFloats.htmlTable?platform_number,cycle_number,position_qc&platform_number=~%226903075|6903076%22&position_qc=~%220|1%22&distinct()&orderBy(%22platform_number,cycle_number%22)

Import the library:

# Import the main fetcher:
from argopy import DataFetcher as ArgoDataFetcher

Define what you want to fetch:

# a region:
ArgoSet = ArgoDataFetcher().region([-85, -45, 10., 20., 0, 1000.])

"Is there already an interface that allows for subsetting of the data or do you imagine that additional tools will be developed on top of the structure as is done with the current https GDAC service? "
In short:

additional tools are required mostly for Matlab users, Python users are already very well equipped.
If there's only subsetting to address, the api/database approach is an alternative solution to changing format. But that's not the case, we also would like to improve raw data access/structure through an alternative file format to netcdf

With regard to better understanding how Argo data could be easily subset-ed from the "cloud", this is for me the opportunity to try to summarise some key points: see below and, sorry for some possible shortcomings and verbosity of the email !

Key points

In long:

	import urllib.request
	import json
	import numpy as np
	import pandas as pd

	def get_float_config(wmo, cyc=None):

	def id_mission(missionCycles, a_cyc):
	this_mission = None
	for im, mission in enumerate(missionCycles):

	import aiohttp
	import asyncio
	from aiohttp.helpers import URL
	from aiohttp.web import HTTPException

	import nest_asyncio
	nest_asyncio.apply() # To be used in jupyter notebooks

	class OceanOPS_Deployments:
	""" Use the OceanOPS API for metadata access to retrieve Argo floats deployment information

	# Must be installed in python env:
	# - motuclient (via pip, https://github.com/clstoulouse/motu-client-python)
	# - pydap (via conda, https://github.com/pydap/pydap)

	import os
	import requests
	import xarray as xr

	# Set-up a request session that will know how to authenticate on the CMEMS server
	MOTU_USERNAME, MOTU_PASSWORD = (

	#!/bin/sh
	#
	# Clone a conda env and add it to jupyter kernel list
	#
	# This is basicaly a nice shortcut for:
	# mamba create --name argopy-pull208 --clone argopy-py38-all-pinned
	# python -m ipykernel install --user --name=argopy-pull208
	#
	#
	# Created by G. Maze on 2023-06-21.

	import pandas as pd
	import copernicusmarine

	class Armor3d:
	"""Global Ocean 1/4° Multi Observation Product ARMOR3D

	Product description:
	https://data.marine.copernicus.eu/product/MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012

	If start_date + n_days <= 2022-12-28:

	import pandas as pd
	import urllib
	import urllib.parse
	import json

	class FrenchFleet:
	"""

	Examples
	--------