Linting.

src/adler/adler.py

@@ -9,18 +9,18 @@
 
 logger = logging.getLogger(__name__)
 
-def runAdler(cli_args):
 
+def runAdler(cli_args):
     logger.info("Beginning Adler.")
     logger.info("Ingesting all data for object {} from RSP...".format(cli_args.ssObjectId))
-  
+
     planetoid = AdlerPlanetoid.construct_from_RSP(
         cli_args.ssObjectId, cli_args.filter_list, cli_args.date_range
     )
 
     logger.info("Data successfully ingested.")
     logger.info("Calculating phase curves...")
-    
+
     # now let's do some phase curves!
 
     # get the r filter SSObject metadata

src/adler/dataclasses/AdlerData.py

@@ -185,7 +185,8 @@ def get_phase_parameters_in_filter(self, filter_name, model_name=None):
             try:
                 model_index = self.filter_dependent_values[filter_index].model_list.index(model_name)
             except ValueError:
-                logger.error("ValueError: Model {} does not exist for filter {} in AdlerData.model_lists.".format(
+                logger.error(
+                    "ValueError: Model {} does not exist for filter {} in AdlerData.model_lists.".format(
                         model_name, filter_name
                     )
                 )

src/adler/dataclasses/AdlerPlanetoid.py

@@ -91,13 +91,17 @@ def construct_from_SQL(
         )
 
         if len(observations_by_filter) == 0:
-            logger.error("No observations found for this object in the given filter(s). Check SSOID and try again.")
+            logger.error(
+                "No observations found for this object in the given filter(s). Check SSOID and try again."
+            )
             raise Exception(
                 "No observations found for this object in the given filter(s). Check SSOID and try again."
             )
 
         if len(filter_list) > len(observations_by_filter):
-            logger.info("Not all specified filters have observations. Recalculating filter list based on past observations.")
+            logger.info(
+                "Not all specified filters have observations. Recalculating filter list based on past observations."
+            )
             filter_list = [obs_object.filter_name for obs_object in observations_by_filter]
             logger.info("New filter list is: {}".format(filter_list))
 
@@ -140,13 +144,17 @@ def construct_from_RSP(
         )
 
         if len(observations_by_filter) == 0:
-            logger.error("No observations found for this object in the given filter(s). Check SSOID and try again.")
+            logger.error(
+                "No observations found for this object in the given filter(s). Check SSOID and try again."
+            )
             raise Exception(
                 "No observations found for this object in the given filter(s). Check SSOID and try again."
             )
 
         if len(filter_list) > len(observations_by_filter):
-            logger.info("Not all specified filters have observations. Recalculating filter list based on past observations.")
+            logger.info(
+                "Not all specified filters have observations. Recalculating filter list based on past observations."
+            )
             filter_list = [obs_object.filter_name for obs_object in observations_by_filter]
             logger.info("New filter list is: {}".format(filter_list))
 
@@ -216,7 +224,11 @@ def populate_observations(
             data_table = get_data_table(observations_sql_query, service=service, sql_filename=sql_filename)
 
             if len(data_table) == 0:
-                logger.warning("No observations found in {} filter for this object. Skipping this filter.".format(filter_name))
+                logger.warning(
+                    "No observations found in {} filter for this object. Skipping this filter.".format(
+                        filter_name
+                    )
+                )
                 print(
                     "WARNING: No observations found in {} filter for this object. Skipping this filter.".format(
                         filter_name

src/adler/dataclasses/dataclass_utilities.py

@@ -88,7 +88,11 @@ def get_from_table(data_table, column_name, data_type, table_name="default"):
             elif data_type == np.ndarray:
                 data_val = np.array(data_table[column_name])
             else:
-                logger.error("TypeError: Type for argument data_type not recognised for column {} in table {}: must be str, float, int or np.ndarray.".format(column_name, table_name))
+                logger.error(
+                    "TypeError: Type for argument data_type not recognised for column {} in table {}: must be str, float, int or np.ndarray.".format(
+                        column_name, table_name
+                    )
+                )
                 raise TypeError(
                     "Type for argument data_type not recognised for column {} in table {}: must be str, float, int or np.ndarray.".format(
                         column_name, table_name
@@ -134,7 +138,9 @@ def check_value_populated(data_val, data_type, column_name, table_name):
     str_is_empty = data_type == str and len(data_val) == 0
 
     if array_length_zero or number_is_nan or str_is_empty:
-        logger.warning("{} unpopulated in {} table for this object. Storing NaN instead.".format(column_name, table_name))
+        logger.warning(
+            "{} unpopulated in {} table for this object. Storing NaN instead.".format(column_name, table_name)
+        )
         print(
             "WARNING: {} unpopulated in {} table for this object. Storing NaN instead.".format(
                 column_name, table_name