Data visualisation

Although argopy is not focus on visualisation, it provides a few functions to get you started. Plotting functions are available for both the data and index fetchers.

Trajectories

from argopy import IndexFetcher as ArgoIndexFetcher
idx = ArgoIndexFetcher().float([6902745, 6902746]).load()
fig, ax = idx.plot('trajectory')
fig, ax = idx.plot()  # Trajectory is the default plot

Some options are available to customise the plot, for instance:

from argopy import DataFetcher as ArgoDataFetcher
idx = ArgoDataFetcher().float([6901020, 6902746, 2903359]).load()
fig, ax = idx.plot('trajectory', style='white', palette='hls', figsize=(10,6), set_global=True)

Histograms on properties

It is also possible to create bar plot for histograms on some data properties: ‘profiler’ and ‘dac’:

from argopy import IndexFetcher as ArgoIndexFetcher
idx = ArgoIndexFetcher().region([-80,-30,20,50,'2021-01','2021-08']).load()
fig, ax = idx.plot('dac')

fig, ax = idx.plot('profiler')

Dashboards

We provide a few shortcuts toward third-party online dashboards that can help you visualise float or profile data. When working in Jupyter notebook, you can insert a dashboard in a cell, or get the url toward the dashboard to open it elsewhere. You have access to the Euro-Argo ERIC, Ocean-OPS, Argovis and BGC dashboards with the option type. See argopy.dashboard() for all the options.

Summary of available dashboards:

Type	base	float	profile
“data”, “ea”	X	X	X
“meta”	X	X	X
“bgc”	X	X	X
“ocean-ops”, “op”	X	X
“coriolis”, “cor”		X
“argovis”	X	X	X

Note

Dashboards can be open at the package level or from data fetchers.

Open the default dashboard:

import argopy
argopy.dashboard()

for a specific float, just provide its WMO:

import argopy
argopy.dashboard(5904797)
# or
ArgoDataFetcher().float(5904797).dashboard()

or for specific float cycle:

import argopy
argopy.dashboard(5904797, 12)
# or
ArgoDataFetcher().profile(5904797, 12).dashboard()