# -----------------------------------
# We can use `Grid` tools to interpolate SST on the altimetry grid
lons, lats = meshgrid(g.x_c, g.y_c)
shape = lats.shape
# flat grid before interp
lons, lats = lons.reshape(-1), lats.reshape(-1)
# interp and reshape
ti = t.interp('analysed_sst', lons, lats).reshape(shape).T
ti = ma.masked_invalid(ti)
# %%
# and add it to `g`
g.add_grid('sst',ti)
# %%
ax = start_axes("SST")
m = g.display(ax, "sst", vmin=295, vmax=300)
u,v = g.grid("ugosa").T,g.grid("vgosa").T
ax.quiver(g.x_c, g.y_c, u, v, scale=10)
update_axes(ax, m, unit='[°K]')
# %%
# Now, with eddy contours, and displaying SST anomaly
# ! lazy patch since add_grid isn't currently completing g.variables_description
g.variables_description['sst'] = t.variables_description[var_name_sst]
g.copy("sst", "sst_high")
g.bessel_high_filter('sst_high',200)
plt.colorbar(mappable, cax=ax.figure.add_axes([0.95, 0.05, 0.01, 0.9]))
# %%
# Load detection files and data to interp
a = EddiesObservations.load_file(data.get_path("Anticyclonic_20160515.nc"))
c = EddiesObservations.load_file(data.get_path("Cyclonic_20160515.nc"))
aviso_map = RegularGridDataset(
data.get_path("dt_med_allsat_phy_l4_20160515_20190101.nc"), "longitude",
"latitude")
aviso_map.add_uv("adt")
# %%
# Compute and store eke in cm²/s²
aviso_map.add_grid("eke", (aviso_map.grid("u")**2 + aviso_map.grid("v")**2) *
0.5 * (100**2))
eke_kwargs = dict(vmin=1, vmax=1000, cmap="magma_r")
ax = start_axes("EKE (cm²/s²)")
m = aviso_map.display(ax, "eke", **eke_kwargs)
a.display(ax, color="r", linewidth=0.5, label="Anticyclonic", ref=-10)
c.display(ax, color="b", linewidth=0.5, label="Cyclonic", ref=-10)
update_axes(ax, m)
# %%
# Get mean of eke in each effective contour
ax = start_axes("EKE (cm²/s²)")
a.display(ax, color="r", linewidth=0.5, label="Anticyclonic", ref=-10)
c.display(ax, color="b", linewidth=0.5, label="Cyclonic", ref=-10)