Re-factoring

menouarazib · Jul 10, 2023 · b92bb3a · b92bb3a
1 parent e3c0f76
commit b92bb3a
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Showing 2 changed files with 7 additions and 8 deletions.
diff --git a/eventdetector/data/helpers.py b/eventdetector/data/helpers.py
@@ -355,13 +355,12 @@ def get_dataset_within_events_times(data_set: pd.DataFrame, events_times: pd.Dat
 def op(dataset_as_overlapping_partitions: np.ndarray, events_as_intervals: list[Interval]) -> \
         tuple[np.ndarray, np.ndarray]:
     """
-    Calculates the "op" value for each overlapping partition in the dataset, based on the overlapping parameter between the
-    partition and a set of events.
+    Calculates the "op" value for each overlapping partition in the dataset, based on the overlapping parameter 
+    between the partition and a set of events.
 
-    Args:
-        dataset_as_overlapping_partitions: A numpy ndarray containing the overlapping partitions for the dataset, where each overlapping
-            partition is a 2D numpy ndarray containing the data points for the partition and their timestamps.
-        events_as_intervals: A list of Interval objects representing the events in the dataset.
+    Args: dataset_as_overlapping_partitions: A numpy ndarray containing the overlapping partitions for the dataset, 
+    where each overlapping partition is a 2D numpy ndarray containing the data points for the partition and their 
+    timestamps. events_as_intervals: A list of Interval objects representing the events in the dataset.
 
     Returns:
         A tuple containing two values:

diff --git a/eventdetector/metamodel/meta_model.py b/eventdetector/metamodel/meta_model.py
@@ -275,8 +275,8 @@ def prepare_data_and_computing_op(self) -> None:
             and convert the remaining events to intervals. If a time partition is specified, it will get the union of
             event times and extract the corresponding portion of the dataset.
 
-        The dataset will then be converted to overlapping partitions using the specified width and step size, and the $op$
-            (overlapping parameter) values will be computed for each partition based on the given intervals.
+        The dataset will then be converted to overlapping partitions using the specified width and step size, 
+        and the $op$ (overlapping parameter) values will be computed for each partition based on the given intervals.
 
         Finally, the learning data (overlapping partitions and corresponding $op$ values) will be stored in
             the instance variables x and y.