Plotting Intro

Gliderpy has a plotting interface for quick simple diagnostic figures they are: plot_ track, plot_ctd, and plot_transect for plotting the glider track, a vertical transect for a specific variable, or a single cast (glider dive). Let’s take a look on how to use them. First we will load a glider dataset as a pandas DataFrame.

[1]:

from gliderpy.fetchers import GliderDataFetcher

glider_grab = GliderDataFetcher()

glider_grab.dataset_ids = ["whoi_406-20160902T1700"]
dfs = glider_grab.to_pandas()

plot_track

The plot_track method will returns a map with the glider’s track.

[2]:

df = dfs["whoi_406-20160902T1700"]

fig, ax = df.plot_track()

/home/runner/micromamba/envs/TEST/lib/python3.13/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_coastline.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)
_images/01-plotting_intro-output_3_1.png

plot_cast

This method groups all the casts by their position (latitude and longitude) giving the user access to each individual cast using the index (profile_number) of the grouped DataFrame.

[3]:

fig, ax = df.plot_cast(profile_number=0, var="temperature", color="blue")
_images/01-plotting_intro-output_5_0.png

All the methods accept and ax argument and more complex figures can be create. For example, let’s add a second variable to the cast above.

[4]:

fig, ax0 = df.plot_cast(profile_number=0, var="temperature", color="blue")

ax1 = ax0.twiny()
df.plot_cast(profile_number=0, var="salinity", color="red", ax=ax1)

ax0.legend()
ax1.legend()

[4]:

<matplotlib.legend.Legend at 0x7f270befad50>
_images/01-plotting_intro-output_7_1.png

We can check a whole transec with the plot_transect method.

[5]:

fig, ax = df.plot_transect(var="temperature", cmap="viridis")
_images/01-plotting_intro-output_9_0.png

Using a given matplotlib we can create a fancier version with two variables in a subplot.

[6]:

import matplotlib.pyplot as plt

fig, (ax0, ax1) = plt.subplots(
    figsize=(15, 9),
    nrows=2,
    sharex=True,
    sharey=True,
)

df.plot_transect(var="temperature", ax=ax0, cmap="viridis")
df.plot_transect(var="salinity", ax=ax1, cmap="cividis")

[6]:

(<Figure size 1500x900 with 4 Axes>, <Axes: ylabel='pressure'>)
_images/01-plotting_intro-output_11_1.png