changes to animation buoy plot tools

scripts/plot_cci_buoy_error.py

@@ -127,18 +127,21 @@ for hour in hours_list_to_plot:
 
     for date in dates_list:
         buoy_data = df_buoy[date.strftime('%Y-%m-%d')][0]
-        xx = buoy_data.lon.values
-        yy = buoy_data.lat.values
-        s = 30 / buoy_data.age.values
-        temp_diff = buoy_data['cci_sst'] - buoy_data['cci_sst_1x1']
-        temp = temp_diff.values
-
-        cmap = plt.get_cmap('PiYG_r', 20)
-
-        img = ax.scatter(xx, yy, s=s,
-                         c=temp, cmap=cmap, vmin=-1, vmax=1, edgecolors='k',
-                         linewidths=0.5,
-                         transform=crs.PlateCarree())
+        if buoy_data.empty:
+            pass
+        else:
+            xx = buoy_data.lon.values
+            yy = buoy_data.lat.values
+            s = 30 / buoy_data.age.values
+            temp_diff = buoy_data['cci_sst'] - buoy_data['cci_sst_1x1']
+            temp = temp_diff.values
+
+            cmap = plt.get_cmap('PiYG_r', 20)
+
+            img = ax.scatter(xx, yy, s=s,
+                             c=temp, cmap=cmap, vmin=-1, vmax=1, edgecolors='k',
+                             linewidths=0.5,
+                             transform=crs.PlateCarree())
 
     # Now adding the colour-bar
     cax, kw = colorbar.make_axes(ax, location='top', pad=0.07, aspect=30)

sst_tools/workflow_sst.py

@@ -89,46 +89,54 @@ def process_hour_drift_from_start_to_end(df,
             lat_n = extent[3]
 
             day_df = day_df[day_df['lon'].between(lon_w, lon_e) & day_df['lat'].between(lat_s, lat_n)]
-
-        # It is important when using interp that you convert to xarray!
-        yy = xr.DataArray(day_df.lat.values, dims='z')
-        xx = xr.DataArray(day_df.lon.values, dims='z')
-
-        # Select time chunk from cci sst dask-xarray
-        time_chunk = cci_data.loc[dict(time=date.strftime('%Y-%m-%d'))]
-        time_chunk = xr.concat(time_chunk.load(), dim='time')
-
-        # Interpolate sst from time chunk to buoy coord and save it
-        cci_sst_in_buoy_cord = time_chunk.interp(lat=yy, lon=xx)
-        cci_sst_in_buoy_cord = cci_sst_in_buoy_cord.to_dataframe().analysed_sst.values
-        day_df = day_df.assign(cci_sst=cci_sst_in_buoy_cord)
-
-        # Calculate gridded averages
-        temp_2x2d = []
-        temp_1x1d = []
-        for x, y in zip(xx, yy):
-            chunk_2x2d = time_chunk.sel(lat=slice(y - 2, y + 2),
-                                        lon=slice(x - 2, x + 2))
-            chunk_1x1d = time_chunk.sel(lat=slice(y - 1, y + 1),
-                                        lon=slice(x - 1, x + 1))
-
-            temp_2x2d.append(np.float32(chunk_2x2d.mean().values))
-            temp_1x1d.append(np.float32(chunk_1x1d.mean().values))
-        # Save it
-        day_df['cci_sst_2x2'] = temp_2x2d
-        day_df['cci_sst_1x1'] = temp_1x1d
-
-        # Interpolate zonal mean from cci data to the buoy coord and save it
-        time_chunk_z_m = cci_zonal_mean.loc[dict(time=date.strftime('%Y-%m-%d'))]
-        time_chunk_z_m = xr.concat(time_chunk_z_m.load(), dim='time')
-        z_mean = time_chunk_z_m.interp(lat=yy)
-        z_mean = z_mean.to_dataframe().analysed_sst.values
-        day_df = day_df.assign(sst_z_mean=lambda x: x.sst - z_mean)
-
-        # Give each date an age
-        day_df['age'] = day_df['date'].apply(lambda x: age.get(x.strftime('%Y-%m-%d')))
-
-        # Save all in a dictionary lead by date
+            day_df = day_df.dropna(subset=['lon', 'lat'])
+
+        if day_df.empty:
+            day_df = day_df.assign(cci_sst=np.NaN)
+            day_df['cci_sst_2x2'] = np.NaN
+            day_df['cci_sst_1x1'] = np.NaN
+            day_df = day_df.assign(sst_z_mean=np.NaN)
+            day_df['age'] = day_df['date'].apply(lambda x: age.get(x.strftime('%Y-%m-%d')))
+        else:
+            # It is important when using interp that you convert to xarray!
+            yy = xr.DataArray(day_df.lat.values, dims='z')
+            xx = xr.DataArray(day_df.lon.values, dims='z')
+
+            # Select time chunk from cci sst dask-xarray
+            time_chunk = cci_data.loc[dict(time=date.strftime('%Y-%m-%d'))]
+            time_chunk = xr.concat(time_chunk.load(), dim='time')
+
+            # Interpolate sst from time chunk to buoy coord and save it
+            cci_sst_in_buoy_cord = time_chunk.interp(lat=yy, lon=xx)
+            cci_sst_in_buoy_cord = cci_sst_in_buoy_cord.to_dataframe().analysed_sst.values
+            day_df = day_df.assign(cci_sst=cci_sst_in_buoy_cord)
+
+            # Calculate gridded averages
+            temp_2x2d = []
+            temp_1x1d = []
+            for x, y in zip(xx, yy):
+                chunk_2x2d = time_chunk.sel(lat=slice(y - 2, y + 2),
+                                            lon=slice(x - 2, x + 2))
+                chunk_1x1d = time_chunk.sel(lat=slice(y - 1, y + 1),
+                                            lon=slice(x - 1, x + 1))
+
+                temp_2x2d.append(np.float32(chunk_2x2d.mean().values))
+                temp_1x1d.append(np.float32(chunk_1x1d.mean().values))
+            # Save it
+            day_df['cci_sst_2x2'] = temp_2x2d
+            day_df['cci_sst_1x1'] = temp_1x1d
+
+            # Interpolate zonal mean from cci data to the buoy coord and save it
+            time_chunk_z_m = cci_zonal_mean.loc[dict(time=date.strftime('%Y-%m-%d'))]
+            time_chunk_z_m = xr.concat(time_chunk_z_m.load(), dim='time')
+            z_mean = time_chunk_z_m.interp(lat=yy)
+            z_mean = z_mean.to_dataframe().analysed_sst.values
+            day_df = day_df.assign(sst_z_mean=lambda x: x.sst - z_mean)
+
+            # Give each date an age
+            day_df['age'] = day_df['date'].apply(lambda x: age.get(x.strftime('%Y-%m-%d')))
+
+            # Save all in a dictionary lead by date
         d[date.strftime('%Y-%m-%d')].append(day_df)
     return d