Data visualisation

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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

_images/trajectory_sample.png

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)

_images/trajectory_sample_white.png

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')

_images/bar_dac.png

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

_images/bar_profiler.png

Float dashboard¶

When working in Jupyter notebook, you can insert the EuroArgo dashboard in a cell with:

import argopy
argopy.dashboard()

_images/dashboard.png

and for a specific float, just provide its WMO:

import argopy
argopy.dashboard(wmo=6902746)

_images/dashboard_float.png

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Manipulating data

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Data quality control