<jupyter_start><jupyter_text>PlottingLet's perform a search for gliders with the keywords UW and Scripps to get a collection of deployments in the USA west coast.<jupyter_code>from gliderpy.fetchers import DatasetList

datasets = DatasetList()
search_terms = ["UW", "Scripps"]
ids = datasets.get_ids(search_terms=search_terms)

print(
    f"found {len(ids)} datasets matching the search terms {search_terms} on the server {datasets.e.server}"
)

ids<jupyter_output><empty_output><jupyter_text>Now that we have a list of dataset ids we can choose one at random to fetch its data and plot it.<jupyter_code>from gliderpy.fetchers import GliderDataFetcher

uw_grab = GliderDataFetcher()
uw_grab.fetcher.dataset_id = ids[42]

df = uw_grab.to_pandas()
df.head()<jupyter_output><empty_output><jupyter_text>At the moment `gliderpy` has two convenience functions for plotting the track and a data section.<jupyter_code>from gliderpy.plotters import plot_track, plot_transect

fig, ax = plot_track(df)
ax.set_title(f"glider {ids[42]}")
import palettable
from palettable.cmocean.sequential import Thermal_20

fig, ax = plot_transect(df, "temperature (Celsius)", cmap=Thermal_20.mpl_colormap)
from palettable.cmocean.sequential import Haline_20

fig, ax = plot_transect(df, "salinity (1)", cmap=Haline_20.mpl_colormap)<jupyter_output><empty_output>
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