gmaze

import tarfile
import gzip
import xarray as xr
import pandas as pd

# Work with a BGC-S doi snapshot
# Snapshot was downloaded from: https://www.seanoe.org/data/00311/42182/data/110195.tar.gz
file = '/Users/gmaze/Downloads/110195.tar.gz'

# Read one of the index file as a pandas dataframe:

gmaze

#!/usr/bin/env python
# coding: utf-8

import xarray as xr
import argopy
from argopy.stores import httpstore

# Convert an Argo multi-prof netcdf into zarr

# 1st we get the url toward the netcdf file:

gmaze

"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:

1. additional tools are required mostly for Matlab users, Python users are already very well equipped.
2. 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:

gmaze

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

class FrenchFleet:
    """

    Examples
    --------

gmaze

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:

gmaze

#!/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.

gmaze

# 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 = (

gmaze

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

gmaze

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):

gmaze

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.])