generalising animation

scripts/plot_cci_buoy_error.py

@@ -37,6 +37,12 @@ start_day = int(sys.argv[3])
 month = int(sys.argv[4])
 day_to_plot = int(sys.argv[5]) # From SLURM ARRAY
 
+lat_bnds = [-50, -25]
+lon_bnds = [100, 180]
+
+extent = np.append(lon_bnds, lat_bnds)
+print(extent)
+
 # Define date arrays to process buoy data
 start_date = datetime(year=year, month=start_month, day=start_day)
 end_date = datetime(year=year, month=month, day=day_to_plot,
@@ -105,14 +111,15 @@ for hour in hours_list_to_plot:
                                                                 plot_end_date,
                                                                 age_dic,
                                                                 sst,
-                                                                zonal_means)
+                                                                zonal_means,
+                                                                extent=extent)
 
     # Plotting
-    f = plt.figure(figsize=(10, 16))
+    f = plt.figure(figsize=(10, 7))
     sns.set_style("white")
 
     ax = plt.axes(projection=crs.Mercator())
-    ax.set_extent([-90, -30, -65, -25], crs=crs.PlateCarree())
+    ax.set_extent(extent, crs=crs.PlateCarree())
 
     # Plot cci sst day
     cci_to_plot = daily_sst_zonal_mean.isel(time=len(sst.time)-1)
@@ -143,7 +150,8 @@ for hour in hours_list_to_plot:
                   # \n ''cci-sst at buoy coords - cci-sst avg in a 2x2 gridbox \n '
                   weight='bold')
 
-    ax.set_title(plot_end_date)
+    f.suptitle(plot_end_date)
+    #ax.set_title()
 
     plot_name = str(int(year)) + '_' + f"{str(int(month)):0>2}" \
                 + f"{str(int(day_to_plot)):0>2}" + f"{str(int(hour)):0>2}"

sst_tools/workflow_sst.py

@@ -30,7 +30,8 @@ def process_hour_drift_from_start_to_end(df,
                                          end_date,
                                          age,
                                          cci_data,
-                                         cci_zonal_mean):
+                                         cci_zonal_mean,
+                                         extent=None):
     """ Gets buoy hourly data for selected dates, groups the data by date
     and prepares extra sst averages for plotting.
 
@@ -40,9 +41,11 @@ def process_hour_drift_from_start_to_end(df,
          start_date: start days to plot (datetime.obj)
          end_date: end days and hours to plot (datetime.obj)
          age: dictionary of dates to plot with the corresponding ages
-         (marker sizes) for the scatter plot.
+                (marker sizes) for the scatter plot.
          cci_data: xarray containing cci sst temperature
          cci_zonal_mean: xarray containing cci sst temperature zonal mean
+         extent: let and lat coordinates for the extension of the map array of four
+                [lon_w, lon_e, lat_s, lat_n]
 
          Returns
          -------
@@ -76,8 +79,16 @@ def process_hour_drift_from_start_to_end(df,
 
         # Crop to plot lat and lon extend, this is not needed but is good for
         # a speedy plot
-        day_df = day_df[day_df['lon'].between(-90, -30) &
-                        day_df['lat'].between(-65, -25)]
+        if extent is None:
+            day_df = day_df
+        else:
+            assert len(extent) == 4
+            lon_w = extent[0]
+            lon_e = extent[1]
+            lat_s = extent[2]
+            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')