Applied pydocstyle

Signed-off-by: Clara De Smet <clara.de.smet@alliander.com>

openstef/feature_engineering/apply_features.py

@@ -37,41 +37,40 @@ def apply_features(
 ) -> pd.DataFrame:
     """Applies the feature functions defined in ``feature_functions.py`` and returns the complete dataframe.
 
-        Features requiring more recent label-data are omitted.
-
-        .. note::
-            For the time deriven features only the onces in the features list will be added. But for the weather features all will be added at present.
-            These unrequested additional features have to be filtered out later.
-
-        Args:
-            data (pandas.DataFrame): a pandas dataframe with input data in the form:
-                                        pd.DataFrame(
-                                            index=datetime,
-                                            columns=[label, predictor_1,..., predictor_n]
-                                        )
-            pj (PredictionJobDataClass): Prediction job.
-            feature_names (list[str]): list of reuqested features
-            horizon (float): Forecast horizon limit in hours.
-
-        Returns:
-            pd.DataFrame(index = datetime, columns = [label, predictor_1,..., predictor_n, feature_1, ..., feature_m])
-
-        Example output:
-
-        .. code-block:: py
-
-            import pandas as pd
-            import numpy as np
-    from geopy.geocoders import Nominatim
-            index = pd.date_range(start = "2017-01-01 09:00:00",
-            freq = '15T', periods = 200)
-            data = pd.DataFrame(index = index,
-                                data = dict(load=
-                                np.sin(index.hour/24*np.pi)*
-                                np.random.uniform(0.7,1.7, 200)))
+    Features requiring more recent label-data are omitted.
+
+    .. note::
+        For the time deriven features only the onces in the features list will be added. But for the weather features all will be added at present.
+        These unrequested additional features have to be filtered out later.
+
+    Args:
+        data (pandas.DataFrame): a pandas dataframe with input data in the form:
+                                    pd.DataFrame(
+                                        index=datetime,
+                                        columns=[label, predictor_1,..., predictor_n]
+                                    )
+        pj (PredictionJobDataClass): Prediction job.
+        feature_names (list[str]): list of reuqested features
+        horizon (float): Forecast horizon limit in hours.
+
+    Returns:
+        pd.DataFrame(index = datetime, columns = [label, predictor_1,..., predictor_n, feature_1, ..., feature_m])
+
+    Example output:
+
+    .. code-block:: py
+
+        import pandas as pd
+        import numpy as np
+        from geopy.geocoders import Nominatim
+        index = pd.date_range(start = "2017-01-01 09:00:00",
+        freq = '15T', periods = 200)
+        data = pd.DataFrame(index = index,
+                            data = dict(load=
+                            np.sin(index.hour/24*np.pi)*
+                            np.random.uniform(0.7,1.7, 200)))
 
     """
-
     if pj is None:
         pj = {"electricity_bidding_zone": BiddingZone.NL}