In [3]:
lon = floats.lon.values
lat = floats.lat.values
figsize=np.array([18, 12]);
projection=ccrs.PlateCarree();
#some other projections we might try
#projection=ccrs.Robinson();
#projection=ccrs.LambertCylindrical();
#for future reference, define a function to set up our map
def map_setup(figsize,projection):
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(projection=projection)
ax.add_feature(cfeature.LAND, facecolor='grey')
ax.gridlines(draw_labels=True)
return fig, ax
fig, ax = map_setup(figsize,projection)
#find the head (a) and tail (b) of each nan-separated segment, excluding the nans
b = np.argwhere(np.isnan(lat)) - 1
a = np.roll(b,1)+2
#set lon to nan when we jump across the dateline, for plotting purposes
lon_nojumps = lon
lon_nojumps[np.abs(lon-np.roll(lon, -1))>10] = np.nan; #nan for lon jumps > 10 degrees
#plot each float trajectory with its own color ... this takes a minute
for n in range(0,np.size(floats.id)):
#index into values belonging to the nth trajectory
index = np.arange(a.item(n),b.item(n))
#note you can't use a[n],b[n] because those are length 1 arrays, not scalars
if index.size > 0:
plt.plot(lon_nojumps[index], lat[index],transform=ccrs.PlateCarree())
#The "transform = ccrs.PlateCarree()" specifies that the data are given as latitude and longitude
#By default, the data is assumed to be in the same projection as that of the axis object
