Demonstrating basic plotting

import cartopy.crs as ccrs
import lzma
import matplotlib.pyplot as plt
import xarray as xr
import trajan as ta
import coloredlogs

Demonstrating how a trajectory dataset (from OpenDrift)

can be analysed and plotted with Trajan

---

coloredlogs.install(level='debug')

Importing a trajectory dataset from a simulation with OpenDrift. decode_coords is needed so that lon and lat are not interpreted as coordinate variables.

with lzma.open('openoil.nc.xz') as oil:
    d = xr.open_dataset(oil, decode_coords=False)
    d.load()
    # Requirement that status>=0 is needed since non-valid points are not masked in OpenDrift output
    d = d.where(d.status>=0)  # only active particles

# Displaying a basic plot of trajectories
d.traj.plot()
plt.title('Basic trajectory plot')
plt.show()

Demonstrating how the Xarray Dataset can be modified, allowing for

more flexibility than can be provided through the plotting method of OpenDrift

---

# Extracting only the first 10 elements, and every 4th output time steps:
d.isel(trajectory=range(0, 10), time=range(0, len(d.time), 4)).traj.plot()
plt.title('First 10 elements, and every 4th time steps')
plt.show()

# Plotting a "mean" trajectory on top
d.traj.plot(color='red', alpha=0.01, land='fast')  # Plotting trajectories in red, and with landmask as land.
dmean = d.mean('trajectory', skipna=True)
dmean.traj.plot.lines(color='black', linewidth=5)  # Plotting mean trajectory in black
plt.show()

# Calling set_up_map explicitly
d.traj.plot.set_up_map(margin=0)
d.traj.plot(color='red', alpha=0.01)  # Plotting trajectories in red
dmean.traj.plot(color='black', alpha=1, linewidth=5)  # Plotting mean trajectory in black
# Plotting the mean trajectory for a sub period in yellow
dmean17nov = d.sel(time=slice('2015-11-17', '2015-11-17 12')).mean('trajectory', skipna=True)
dmean17nov.traj.plot(color='yellow', alpha=1, linewidth=5)
plt.tight_layout()
plt.savefig('testplot.png')  # TODO: this produces a figure, but with title from previous