Better gestion of error for recalibration

src/xsar/config.yml

@@ -1,16 +1,4 @@
 # default data dir for tests and examples
 data_dir: /tmp
 auxiliary_dir:
-path_auxiliary_df: 
-
-auxiliary_names:    
-    S1B:     
-        v_IPF_36:
-            AUX_PP1: 'S1B_AUX_PP1_V20160422T000000_G20220323T140710.SAFE'
-            AUX_CAL: 'S1B_AUX_CAL_V20160422T000000_G20210104T140113.SAFE'
-            #AUX_INS: 'S1B_AUX_INS_V20160422T000000_G20211027T134314.SAFE'
-    S1A:
-        v_IPF_36:
-            AUX_PP1: 'S1A_AUX_PP1_V20190228T092500_G20220323T153041.SAFE'
-            AUX_CAL: 'S1A_AUX_CAL_V20190228T092500_G20210104T141310.SAFE'
-            #AUX_INS: 'S1A_AUX_INS_V20190228T092500_G20211103T111906.SAFE'}
+path_auxiliary_df:

src/xsar/raster_readers.py

@@ -73,7 +73,7 @@ def ecmwf_0100_1h(fname, **kwargs):
     xarray.Dataset
     """
     ecmwf_ds = xr.open_dataset(
-        fname, chunks={'Longitude': 1000, 'Latitude': 1000}).isel(time=0)
+        fname, chunks={'time': 1, 'latitude': 901, 'longitude': 1800}).isel(time=0)
     ecmwf_ds.attrs['time'] = datetime.datetime.fromtimestamp(
         ecmwf_ds.time.item() // 1000000000)
     if 'time' in ecmwf_ds:
@@ -149,7 +149,7 @@ def hwrf_0015_3h(fname, **kwargs):
     -------
     xarray.Dataset
     """
-    hwrf_ds = xr.open_dataset(fname)
+    hwrf_ds = xr.open_dataset(fname, chunks={'t': 1, 'LAT': 700, 'LON': 700})
     try:
         hwrf_ds = hwrf_ds[['U', 'V', 'LON', 'LAT']]
         hwrf_ds = hwrf_ds.squeeze('t', drop=True)
@@ -191,7 +191,8 @@ def era5_0250_1h(fname, **kwargs):
     xarray.Dataset
     """
 
-    ds_era5 = xr.open_dataset(fname, chunks={'time': 1})
+    ds_era5 = xr.open_dataset(
+        fname, chunks={'time': 6, 'latitude025': 721, 'longitude025': 1440})
     ds_era5 = ds_era5[['u10', 'v10', 'latitude025', 'longitude025']]
     ds_era5 = ds_era5.sel(time=str(kwargs['date']), method="nearest")
     ds_era5 = ds_era5.drop('time')

src/xsar/sentinel1_dataset.py

@@ -206,7 +206,7 @@ def __init__(self, dataset_id, resolution=None,
         if self.apply_recalibration and np.all(np.isnan(self.datatree.antenna_pattern['roll'].values)):
             self.apply_recalibration = False
             raise ValueError(
-                f"Recalibration can't be done without roll angle. You probably work with an old file for which roll angle is not auxiliary file.")
+                f"Recalibration can't be done without roll angle. You probably work with an old file for which roll angle is not in auxiliary file.")
 
         # self.datatree['measurement'].ds = .from_dict({'measurement':self._load_digital_number(resolution=resolution, resampling=resampling, chunks=chunks)
         # self._dataset = self.datatree['measurement'].ds #the two variables should be linked then.
@@ -528,14 +528,40 @@ def add_high_resolution_variables(self, luts=False, patch_variable=True, skip_va
                                                 (dataframe_aux.aux_type == "CAL") &
                                                 (dataframe_aux.icid == int(self.sar_meta.manifest_attrs['icid'])) &
                                                 (dataframe_aux.validation_date <= self.sar_meta.start_date)]
+                    # Check if sel_cal is empty
+                    if sel_cal.empty:
+                        sel_ = dataframe_aux.loc[(dataframe_aux.sat_name == self.sar_meta.manifest_attrs['satellite']) &
+                                                 (dataframe_aux.aux_type == "CAL") &
+                                                 (dataframe_aux.icid == int(self.sar_meta.manifest_attrs['icid']))]
+                        if sel_.empty:
+                            raise ValueError(
+                                f"Cannot recalibrate - No matching CAL data found for your data : start_date : {self.sar_meta.start_date}.")
+                        else:
+                            raise ValueError(f"Cannot recalibrate - No matching CAL data found for your data: start_date: {self.sar_meta.start_date} & \
+                                miniumum validation date in active aux files: {sel_.sort_values(by=['validation_date'], ascending=False).validation_date.values[0]}.")
+
                     sel_cal = sel_cal.sort_values(
                         by=["validation_date", "generation_date"], ascending=False)
+
                     path_new_cal = sel_cal.iloc[0].aux_path
 
                     sel_pp1 = dataframe_aux.loc[(dataframe_aux.sat_name == self.sar_meta.manifest_attrs['satellite']) &
                                                 (dataframe_aux.aux_type == "PP1") &
                                                 (dataframe_aux.icid == int(self.sar_meta.manifest_attrs['icid'])) &
                                                 (dataframe_aux.validation_date <= self.sar_meta.start_date)]
+
+                    # Check if sel_pp1 is empty
+                    if sel_pp1.empty:
+                        sel_ = dataframe_aux.loc[(dataframe_aux.sat_name == self.sar_meta.manifest_attrs['satellite']) &
+                                                 (dataframe_aux.aux_type == "PP1") &
+                                                 (dataframe_aux.icid == int(self.sar_meta.manifest_attrs['icid']))]
+                        if sel_.empty:
+                            raise ValueError(
+                                f"Cannot recalibrate - No matching PP1 data found for your data : start_date : {self.sar_meta.start_date}.")
+                        else:
+                            raise ValueError(f"Cannot recalibrate - No matching PP1 data found for your data: start_date: {self.sar_meta.start_date} & \
+                                miniumum validation date in active aux files: {sel_.sort_values(by=['validation_date'], ascending=False).validation_date.values[0]}.")
+
                     sel_pp1 = sel_pp1.sort_values(
                         by=["validation_date", "generation_date"], ascending=False)
                     path_new_pp1 = sel_pp1.iloc[0].aux_path