Guillaume Maze 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.])

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)

To run data mining algorithms on ocean's large datasets, we need to optimise access to datasets with possibly up to 6-dimensions.

A generalised 6-dimensional dataset is [X,Y,Z,T,V,E] where:

X,Y,Z,T are the space/time dimensions,
V is the variable dimension (eg: temperature, salinity, zonal velocity) and,
E the ensemble dimensions (list of realisations or members).

Running data mining algorithms on this dataset mostly implies to re-arrange the 6 dimensions into 2-dimensional arrays with, following the statistics vocabulary "sampling" vs "features" dimensions. The sampling dimension is along rows, the features along columns. A large dataset can have billions of rows and hundreds of columns.

Eg:

	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):

	#!/bin/env python
	# -coding: UTF-8 --

	import requests
	import time

	# Request full data:
	t0 = time.time()
	url = 'http://www.ifremer.fr/erddap/tabledap/ArgoFloats.csv?data_mode,latitude,longitude,position_qc,time,time_qc,direction,platform_number,cycle_number,pres,temp,psal,pres_qc,temp_qc,psal_qc,pres_adjusted,temp_adjusted,psal_adjusted,pres_adjusted_qc,temp_adjusted_qc,psal_adjusted_qc,pres_adjusted_error,temp_adjusted_error,psal_adjusted_error&platform_number=~"5900446"&distinct()&orderBy("time,pres")'
	requests.get(url)

	#!/usr/bin/env python
	# coding: utf-8
	#
	# $ time ./Parallel_images.py
	# Use 8 processes
	# 107.249u 2.444s 0:17.10 641.4% 0+0k 0+0io 1056pf+0w
	#

	import os
	import numpy as np

	#!/usr/bin/env bash
	#
	# Gerenate mp4 videos from a collection of image files
	#
	# Video files are saved into ./videos
	#

	# Folder with image files:
	src="/home/datawork-lops-oh/somovar/WP1/data/dashboard/img/monthly" # This is an example

	def base_fct(**kwargs):
	defaults = {'sharey':'row', 'dpi':80, 'facecolor':'w', 'edgecolor':'k'}
	options = {defaults, kwargs}
	return options

	def fct(**kwargs):
	defaults = {'sharey':'cols'}
	return base_fct({defaults, **kwargs})

	print("Default base options:\n", base_fct())

	#~/usr/bin/env python
	#
	# Useful functions for xarray time series analysis
	# (c) G. Maze, Ifremer
	#

	import numpy as np
	import xarray as xr
	import pandas as pd
	from statsmodels.tsa.seasonal import seasonal_decompose