update to fix temporal subsetting

podaac/subsetter/subset.py

@@ -791,7 +791,12 @@ def build_cond(str_timestamp, compare):
                 epoch_datetime = dataset[epoch_time_var_name].values[0]
                 timestamp = np.datetime64(timestamp) - epoch_datetime
 
-        return compare(dataset[time_var_name], timestamp)
+        time_data = dataset[time_var_name]
+        if dataset[time_var_name].long_name == "reference time of sst file":
+            timedelta_seconds = dataset['sst_dtime'].astype('timedelta64[s]')
+            time_data = dataset[time_var_name] + timedelta_seconds
+
+        return compare(time_data, timestamp)
 
     temporal_conds = []
     if min_time:
@@ -1094,7 +1099,6 @@ def open_as_nc_dataset(filepath: str) -> Tuple[nc.Dataset, bool]:
     try:
         nc_dataset = nc.Dataset(filepath, mode='r')
         has_groups = bool(nc_dataset.groups)
-
         # If dataset has groups, transform to work with xarray
         if has_groups:
             nc_dataset = transform_grouped_dataset(nc_dataset, filepath)
@@ -1212,7 +1216,6 @@ def subset(file_to_subset: str, bbox: np.ndarray, output_file: str,
         if 'ScanTime' in [var.split('__')[-2] for var in list(nc_dataset.variables.keys())]:
             gc.change_var_dims(nc_dataset, variables)
             hdf_type = 'GPM'
-
     args = {
         'decode_coords': False,
         'mask_and_scale': False,
@@ -1236,6 +1239,12 @@ def subset(file_to_subset: str, bbox: np.ndarray, output_file: str,
             except AttributeError:
                 pass
 
+            try:
+                if nc_dataset[time_variable].long_name == "reference time of sst file":
+                    args['mask_and_scale'] = True
+            except AttributeError:
+                pass
+
     if hdf_type == 'GPM':
         args['decode_times'] = False
 

tests/test_subset.py

@@ -350,13 +350,15 @@ def test_subset_empty_bbox(test_file, data_dir, subset_output_dir, request):
 
     # Ensure all variables are present but empty.
     for _, variable in empty_dataset.data_vars.items():
-        try:
-            assert np.all(variable.data == variable.attrs.get('_FillValue', np.nan) or np.isnan(variable.data))
-        except Exception as ex:
-            # if there is no fill value and type is integer then don't raise exception
-            fill_value = variable.attrs.get('_FillValue', np.nan)
-            if not (np.isnan(fill_value) and np.issubdtype(variable.dtype, np.integer)):
-                raise(ex)
+        fill_value = variable.attrs.get('_FillValue', np.nan)
+        data = variable.data
+
+        # Perform the main check
+        condition = np.all(data == fill_value) or np.all(np.isnan(data))
+
+        # Handle the specific integer dtype case
+        if not condition and not (np.isnan(fill_value) and np.issubdtype(variable.dtype, np.integer)):
+            assert condition, f"Data does not match fill value for variable: {variable}"
 
     assert test_input_dataset.dims.keys() == empty_dataset.dims.keys()