<jupyter_start><jupyter_text>Assignment: Plotting surface currentsThroughout the book, we will be using the [NCEP Global Ocean Data Assimilation System (GODAS) datasets](https://psl.noaa.gov/data/gridded/data.godas.html) to explore the ocean currents and properties ourselves. We will do that using the `xarray` and `dask` packages for analysing NetCDF data, in combination with `cartopy` for plottingFirst of all, make sure that you have `jupyter`, `xarray`, `dask`, `netcdf4` and `cartopy` installed in your conda environment.Then, download the ['U component of current'](https://psl.noaa.gov/cgi-bin/db_search/DBListFiles.pl?did=98&tid=83478&vid=1918) and ['V component of current'](https://psl.noaa.gov/cgi-bin/db_search/DBListFiles.pl?did=98&tid=83478&vid=1920) from the GODAS website at https://psl.noaa.gov/data/gridded/data.godas.html (via 'List of *.nc files'>'See list'>'Save Link as'). To limit download and storage, it is ok for now to only download the year 2020.Then, load the modules as<jupyter_code>import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature<jupyter_output><empty_output><jupyter_text>Now open the U file as (where of course `datadir` should point to the directory where you saved the GODAS data)<jupyter_code>datadir ='/Users/erik/Downloads/'
U = xr.open_dataset(datadir+'ucur.2020.nc')
U<jupyter_output><empty_output><jupyter_text>What does the list above tell you about the content of the file? Now plot the surface velocities with e.g.<jupyter_code>U.ucur.isel(level=0).mean('time').plot()<jupyter_output><empty_output><jupyter_text>As you see, it is very simple to plot datasets with `xarray`, where you can take averages along dimensions with `mean()` and subset dimensions with `isel` (using th indices) or `sel` (using the values). See http://xarray.pydata.org/en/stable/plotting.html for much more background on plotting in xarrayIf you want a somewhat nicer-looking figure, there are many ways in which you can tweak it. For example, for a different projection, use e.g.<jupyter_code>fig = plt.figure(figsize=(12, 5))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson(central_longitude=180))

ax.coastlines(resolution='50m')
ax.add_feature(cfeature.LAND)
U.ucur.isel(level=0).mean('time').plot(ax=ax, transform=ccrs.PlateCarree())<jupyter_output><empty_output>
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