clouddrift.plotting.plot_ragged#

Plot individually the rows of a ragged array dataset on a Matplotlib Axes or a Cartopy GeoAxes object ax.

This function wraps Matplotlib’s plot function (plt.plot) and LineCollection (matplotlib.collections) to efficiently plot the rows of a ragged array dataset.

Parameters#

ax: matplotlib.axes.Axes or cartopy.mpl.geoaxes.GeoAxes: Axis to plot on.
longitudearray-like: Longitude sequence. Unidimensional array input.
latitudearray-like: Latitude sequence. Unidimensional array input.
rowsizelist: List of integers specifying the number of data points in each row.
*argstuple: Additional arguments to pass to ax.plot.
colorsarray-like: Values to map on the current colormap. If colors is the shape of rowsize, the data points of each row are uniformly colored according to the color value for the row. If colors is the shape of longitude and latitude, the data points are colored according to the color value for each data point.
tolerancefloat: Longitude tolerance gap between data points (in degrees) for segmenting rows. For periodic domains, the tolerance parameter should be set to the maximum allowed gap between data points. Defaults to 180.
**kwargsdict: Additional keyword arguments to pass to ax.plot.

Returns#

list of matplotlib.lines.Line2D or matplotlib.collections.LineCollection: The plotted lines or line collection. Can be used to set a colorbar after plotting or extract information from the lines.

Examples#

Load 100 trajectories from the gdp1h dataset for the examples.

>>> from clouddrift import datasets
>>> from clouddrift.ragged import subset
>>> from clouddrift.plotting import plot_ragged
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from mpl_toolkits.axes_grid1 import make_axes_locatable
>>> ds = datasets.gdp1h()
>>> ds = subset(ds, {"id": ds.id[:100].values}).load()

Plot the trajectories, assigning a different color to each trajectory:

>>> fig = plt.figure()
>>> ax = fig.add_subplot(1, 1, 1)
>>> l = plot_ragged(
>>>     ax,
>>>     ds.lon,
>>>     ds.lat,
>>>     ds.rowsize,
>>>     colors=np.arange(len(ds.rowsize))
>>> )
>>> divider = make_axes_locatable(ax)
>>> cax = divider.append_axes('right', size='3%', pad=0.05)
>>> fig.colorbar(l, cax=cax)

To plot the same trajectories, but assigning a different color to each data point based on a transformation of the time variable mapped onto the inferno colormap:

>>> fig = plt.figure()
>>> ax = fig.add_subplot(1, 1, 1)
>>> time = [v.astype(np.int64) / 86400 / 1e9 for v in ds.time.values]
>>> l = plot_ragged(
>>>     ax,
>>>     ds.lon,
>>>     ds.lat,
>>>     ds.rowsize,
>>>     colors=np.floor(time),
>>>     cmap="inferno"
>>> )
>>> divider = make_axes_locatable(ax)
>>> cax = divider.append_axes('right', size="3%", pad=0.05)
>>> fig.colorbar(l, cax=cax)

Finally, to plot the same trajectories, but using a cartopy projection:

>>> import cartopy.crs as ccrs
>>> fig = plt.figure()
>>> ax = fig.add_subplot(1, 1, 1, projection=ccrs.Mollweide())
>>> l = plot_ragged(
>>>     ax,
>>>     ds.lon,
>>>     ds.lat,
>>>     ds.rowsize,
>>>     colors=np.arange(len(ds.rowsize)),
>>>     transform=ccrs.PlateCarree(),
>>>     cmap="Blues",
>>> )
>>> ax.set_extent([-180, 180, -90, 90])
>>> ax.coastlines()
>>> ax.gridlines(draw_labels=True)
>>> divider = make_axes_locatable(ax)
>>> cax = divider.append_axes('right', size="3%", pad=0.25, axes_class=plt.Axes)
>>> fig.colorbar(l, cax=cax)

Raises#

ValueError: If longitude and latitude arrays do not have the same shape. If colors do not have the same shape as longitude and latitude arrays or rowsize. If ax is not a matplotlib Axes or GeoAxes object. If ax is a GeoAxes object and the transform keyword argument is not provided.
ImportError: If matplotlib is not installed. If the axis is a GeoAxes object and cartopy is not installed.

clouddrift.plotting.plot_ragged

Contents

clouddrift.plotting.plot_ragged#

Parameters#

Returns#

Examples#

Raises#