more instructive error messages + some deprecation fixes

src/pypromice/postprocess/real_time_utilities.py

@@ -68,7 +68,7 @@ def get_latest_data(
 
     # Apply smoothing to z_boom_u
     # require at least 2 hourly obs? Sometimes seeing once/day data for z_boom_u
-    df_limited = rolling_window(df_limited, "z_boom_u", "72H", 2, 1)
+    df_limited = rolling_window(df_limited, "z_boom_u", "72h", 2, 1)
 
     # limit to single most recent valid row (convert to series)
     s_current = df_limited.loc[last_valid_index]

src/pypromice/process/L2toL3.py

@@ -100,20 +100,31 @@ def toL3(L2, station_config={}, T_0=273.15):
         else:
             logger.info('t_l, p_l or rh_l_cor missing, cannot calulate tubrulent heat fluxes')
 
-    # Smoothing and inter/extrapolation of GPS coordinates
+    if len(station_config)==0:
+        logger.warning('\n***\nThe station configuration file is missing or improperly passed to pypromice. Some processing steps might fail.\n***\n')
+    # Smoothing and inter/extrapolation of GPS coordinates       
     for var in ['gps_lat', 'gps_lon', 'gps_alt']:
         ds[var.replace('gps_','')] = ('time', gps_coordinate_postprocessing(ds, var, station_config))
 
     # processing continuous surface height, ice surface height, snow height
     try:
         ds = process_surface_height(ds, station_config)
     except Exception as e:
-        logger.error("Error processing surface height at %s"%station_config['stid'])
+        logger.error("Error processing surface height at %s"%L2.attrs['station_id'])
         logging.error(e, exc_info=True)
 
     # making sure dataset has the attributes contained in the config files
-    ds.attrs['project'] = station_config['project']
-    ds.attrs['location_type'] = station_config['location_type']
+    if 'project' in station_config.keys():
+        ds.attrs['project'] = station_config['project']
+    else:
+        logger.error('No project info in station_config. Using \"PROMICE\".')
+        ds.attrs['project'] = "PROMICE"
+
+    if 'location_type' in station_config.keys():
+        ds.attrs['location_type'] = station_config['location_type']
+    else:
+        logger.error('No project info in station_config. Using \"ice sheet\".')
+        ds.attrs['location_type'] = "ice sheet"
 
     return ds
 

tests/unit/qc/test_persistence.py

@@ -23,10 +23,10 @@ def _test_1_hour_repeat(self, index: int):
             start="2023-01-26", end="2023-01-27", freq="h", tz="utc", inclusive="left"
         )
         input_series = pd.Series(index=time_range, data=np.arange(0, len(time_range)))
-        input_series[index + 1] = input_series[index]
+        input_series.iloc[index + 1] = input_series.iloc[index]
         min_repeats = 1
         expected_output = input_series.map(lambda _: False)
-        expected_output[index + 1] = True
+        expected_output.iloc[index + 1] = True
 
         persistent_mask = find_persistent_regions(
             input_series, min_repeats=min_repeats, max_diff=0.001
@@ -62,7 +62,7 @@ def test_persistent_period_longer_than_period_threshold(self):
         expected_filter_start = 27
         expected_filter_end = 33
         input_series = pd.Series(index=time_range, data=np.arange(0, len(time_range)))
-        input_series[index_start:index_end] = input_series[index_start]
+        input_series.iloc[index_start:index_end] = input_series.iloc[index_start]
         expected_output = input_series.map(lambda _: False)
         expected_output[expected_filter_start:expected_filter_end] = True
 
@@ -82,7 +82,7 @@ def test_period_threshold_longer_than_persistent_period(self):
         index_end = 27
         min_repeats = 10
         input_series = pd.Series(index=time_range, data=np.arange(0, len(time_range)))
-        input_series[index_start:index_end] = input_series[index_start]
+        input_series.iloc[index_start:index_end] = input_series.iloc[index_start]
         expected_output = input_series.map(lambda _: False)
 
         persistent_mask = find_persistent_regions(
@@ -101,7 +101,7 @@ def test_persistent_period_at_the_end(self):
         min_repeats = 4
         expected_filter_start = 27
         input_series = pd.Series(index=time_range, data=np.arange(0, len(time_range)))
-        input_series[index_start:] = input_series[index_start]
+        input_series.iloc[index_start:] = input_series.iloc[index_start]
         expected_output = input_series.map(lambda _: False)
         expected_output[expected_filter_start:] = True
 
@@ -121,10 +121,10 @@ def test_dont_filter_nan_values(self):
             index=time_range, data=np.zeros_like(time_range, dtype="float")
         )
         min_repeats = 4
-        input_series[:] = np.nan
-        input_series[9] = -11
-        input_series[10:12] = -10
-        input_series[15] = -9
+        input_series.iloc[:] = np.nan
+        input_series.iloc[9] = -11
+        input_series.iloc[10:12] = -10
+        input_series.iloc[15] = -9
         # There are >=4 repeats if the nan values are forward filled. [10:15] == -10
         # The output mask shouldn't filter nan values.
         expected_output = input_series.map(lambda _: False)