Gallery example on data collocation

examples/12_external_data/SST_collocation.py

@@ -0,0 +1,143 @@
+"""
+Collocating external data
+==========================
+
+Script will use py-eddy-tracker methods to upload external data (sea surface temperature, SST) in a common structure with altimetry.
+
+Figures higlights the different steps.
+
+"""
+
+from matplotlib import pyplot as plt
+from py_eddy_tracker.dataset.grid import RegularGridDataset
+from py_eddy_tracker import data
+import cartopy.crs as ccrs
+from datetime import datetime
+from numpy import ma, meshgrid
+
+datest = '20160707'
+
+filename_alt = "../l4_cmems/dt_blacksea_allsat_phy_l4_"+datest+"_20200801.nc" 
+lon_name_alt = 'longitude'
+lat_name_alt = 'latitude'
+
+filename_sst = "../SST/"+datest+"000000-GOS-L4_GHRSST-SSTfnd-OISST_HR_REP-BLK-v02.0-fv01.0.nc4" 
+lon_name_sst = 'lon'
+lat_name_sst = 'lat'
+var_name_sst = 'analysed_sst'
+
+extent = [27, 42, 40.5, 47]
+
+# %%
+# Functions to initiate figure axes
+def start_axes(title, extent=extent, fig=None, sp=None):
+    if fig is None:
+        fig = plt.figure(figsize=(13, 5))
+        ax = fig.add_axes([0.03, 0.03, 0.90, 0.94],projection=ccrs.PlateCarree())
+    else:
+        ax = fig.add_subplot(sp,projection=ccrs.PlateCarree())
+
+    ax.set_extent(extent)
+    ax.gridlines()
+    ax.coastlines(resolution='50m')
+    ax.set_title(title)
+    return ax
+
+def update_axes(ax, mappable=None, unit=''):
+    ax.grid()
+    if mappable:  
+        plt.colorbar(mappable, cax=ax.figure.add_axes([0.95, 0.05, 0.01, 0.9],title=unit))
+
+# %%
+# Loading SLA and first display
+# -----------------------------
+g = RegularGridDataset(data.get_path(filename_alt), lon_name_alt,lat_name_alt)
+ax = start_axes("SLA", extent=extent)
+m = g.display(ax, "sla", vmin=0.05, vmax=0.35)
+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='[m]')
+
+# %%
+# Loading SST and first display
+# -----------------------------
+t = RegularGridDataset(filename=data.get_path(filename_sst),
+                       x_name=lon_name_sst,
+                       y_name=lat_name_sst)
+
+# The following now load the corresponding variables from the SST netcdf (it's not needed to load it beforehand, so not executed.)
+# t.grid(var_name_sst)
+
+# %% 
+# We can now plot SST from `t`
+ax = start_axes("SST title")
+m = t.display(ax, 'analysed_sst', vmin=295, vmax=300)
+update_axes(ax, m, unit='[°K]')
+
+# %% 
+# Including SST in the Altimetry grid
+# -----------------------------------
+# 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)
+
+# %% 
+# Eddy detection
+date = datetime.strptime(datest,'%Y%m%d')
+a, c = g.eddy_identification("sla", "ugosa", "vgosa", date, 0.002)
+
+# %%
+kwargs_a = dict(lw=2, label="Anticyclonic", ref=-10, color="b")
+kwargs_c = dict(lw=2, label="Cyclonic", ref=-10, color="r")
+ax = start_axes("SST anomaly")
+m = g.display(ax, "sst_high", vmin=-1, vmax=1)
+ax.quiver(g.x_c, g.y_c, u, v, scale=20)
+a.display(ax, **kwargs_a), c.display(ax, **kwargs_c)
+update_axes(ax, m, unit='[°K]')
+
+# %% 
+# Example of post-processing
+# --------------------------
+# Get mean of sst anomaly_high in each internal contour
+anom_a = a.interp_grid(g, "sst_high", method="mean", intern=True)
+anom_c = c.interp_grid(g, "sst_high", method="mean", intern=True)
+
+# %% 
+# Are cyclonic (resp. anticyclonic) eddies generally associated with positive (resp. negative) SST anomaly ? 
+fig = plt.figure(figsize=(5, 5))
+ax = fig.add_axes([0.03, 0.03, 0.90, 0.90])
+ax.set_xlabel("SST anomaly")
+ax.set_xlim([-1,1])
+ax.set_title('Histograms of SST anomalies')
+ax.hist(anom_a,5, alpha=0.5, label = 'Anticyclonic (mean:%s)'%(anom_a.mean()))
+ax.hist(anom_c,5, alpha=0.5, label = 'Cyclonic (mean:%s)'%(anom_c.mean()))
+ax.legend()
+
+# %% 
+# Not clearly so in that case ..