diff --git a/src/sdk/python/rtdip_sdk/pipelines/data_quality/data_manipulation/spark/gaussian_smoothing.py b/src/sdk/python/rtdip_sdk/pipelines/data_quality/data_manipulation/spark/gaussian_smoothing.py
index 156acf387..fa95350c4 100644
--- a/src/sdk/python/rtdip_sdk/pipelines/data_quality/data_manipulation/spark/gaussian_smoothing.py
+++ b/src/sdk/python/rtdip_sdk/pipelines/data_quality/data_manipulation/spark/gaussian_smoothing.py
@@ -10,6 +10,7 @@
 )
 from ..interfaces import DataManipulationBaseInterface
 
+
 class GaussianSmoothing(DataManipulationBaseInterface):
     """
     Applies Gaussian smoothing to specified columns of a PySpark DataFrame.
@@ -59,13 +60,13 @@ class GaussianSmoothing(DataManipulationBaseInterface):
     """
 
     def __init__(
-            self,
-            df: PySparkDataFrame,
-            sigma: float,
-            mode: str = "temporal",
-            id_col: str = "id",
-            timestamp_col: str = "timestamp",
-            value_col: str = "value",
+        self,
+        df: PySparkDataFrame,
+        sigma: float,
+        mode: str = "temporal",
+        id_col: str = "id",
+        timestamp_col: str = "timestamp",
+        value_col: str = "value",
     ) -> None:
 
         if not isinstance(df, PySparkDataFrame):
@@ -129,11 +130,10 @@ def filter(self) -> PySparkDataFrame:
 
                 smoothed_values = gaussian_filter1d(values_sorted, sigma=self.sigma)
 
-
                 reverse_indices = np.argsort(sorted_indices)
                 pdf.loc[mask, self.value_col] = smoothed_values[reverse_indices]
 
         spark = self.df.sparkSession
         result_df = spark.createDataFrame(pdf)
 
-        return result_df
\ No newline at end of file
+        return result_df
diff --git a/tests/sdk/python/rtdip_sdk/pipelines/data_quality/data_manipulation/spark/test_gaussian_smoothing.py b/tests/sdk/python/rtdip_sdk/pipelines/data_quality/data_manipulation/spark/test_gaussian_smoothing.py
index 607f91762..ff0f8d50a 100644
--- a/tests/sdk/python/rtdip_sdk/pipelines/data_quality/data_manipulation/spark/test_gaussian_smoothing.py
+++ b/tests/sdk/python/rtdip_sdk/pipelines/data_quality/data_manipulation/spark/test_gaussian_smoothing.py
@@ -16,7 +16,7 @@
 from pyspark.sql import SparkSession
 
 from src.sdk.python.rtdip_sdk.pipelines.data_quality.data_manipulation.spark.gaussian_smoothing import (
-    GaussianSmoothing
+    GaussianSmoothing,
 )
 
 
@@ -40,7 +40,7 @@ def test_gaussian_smoothing_temporal(spark_session: SparkSession):
             ("A2PS64V0J.:ZUX09R", "2024-01-02 16:00:12.000", "Good", "0.150000006"),
             ("A2PS64V0J.:ZUX09R", "2024-01-02 20:03:46.000", "Good", "0.340000004"),
         ],
-        ["TagName", "EventTime", "Status", "Value"]
+        ["TagName", "EventTime", "Status", "Value"],
     )
 
     # Apply smoothing
@@ -50,13 +50,15 @@ def test_gaussian_smoothing_temporal(spark_session: SparkSession):
         id_col="TagName",
         mode="temporal",
         timestamp_col="EventTime",
-        value_col="Value"
+        value_col="Value",
     )
     result_df = smoother.filter()
     result_pdf = result_df.toPandas()
     original_pdf = df.toPandas()
 
-    assert not result_pdf["Value"].equals(original_pdf["Value"]), "Values should be smoothed and not identical"
+    assert not result_pdf["Value"].equals(
+        original_pdf["Value"]
+    ), "Values should be smoothed and not identical"
 
 
 def test_gaussian_smoothing_spatial(spark_session: SparkSession):
@@ -68,7 +70,7 @@ def test_gaussian_smoothing_spatial(spark_session: SparkSession):
             ("A2PS64V0J.:ZUX09R", "2024-01-02 16:00:12.000", "Good", "0.150000006"),
             ("A2PS64V0J.:ZUX09R", "2024-01-02 20:03:46.000", "Good", "0.340000004"),
         ],
-        ["TagName", "EventTime", "Status", "Value"]
+        ["TagName", "EventTime", "Status", "Value"],
     )
 
     # Apply smoothing
@@ -78,13 +80,15 @@ def test_gaussian_smoothing_spatial(spark_session: SparkSession):
         id_col="TagName",
         mode="spatial",
         timestamp_col="EventTime",
-        value_col="Value"
+        value_col="Value",
     )
     result_df = smoother.filter()
     result_pdf = result_df.toPandas()
     original_pdf = df.toPandas()
 
-    assert not result_pdf["Value"].equals(original_pdf["Value"]), "Values should be smoothed and not identical"
+    assert not result_pdf["Value"].equals(
+        original_pdf["Value"]
+    ), "Values should be smoothed and not identical"
 
 
 def test_interval_detection_large_data_set(spark_session: SparkSession):
@@ -94,18 +98,18 @@ def test_interval_detection_large_data_set(spark_session: SparkSession):
 
     df = spark_session.read.option("header", "true").csv(file_path)
 
-
     smoother = GaussianSmoothing(
         df=df,
         sigma=1,
         id_col="TagName",
         mode="temporal",
         timestamp_col="EventTime",
-        value_col="Value"
+        value_col="Value",
     )
 
     actual_df = smoother.filter()
 
+
 def test_gaussian_smoothing_invalid_mode(spark_session: SparkSession):
     # Create test data
     df = spark_session.createDataFrame(
@@ -116,7 +120,7 @@ def test_gaussian_smoothing_invalid_mode(spark_session: SparkSession):
             ("A2PS64V0J.:ZUX09R", "2024-01-02 16:00:12.000", "Good", "0.150000006"),
             ("A2PS64V0J.:ZUX09R", "2024-01-02 20:03:46.000", "Good", "0.340000004"),
         ],
-        ["TagName", "EventTime", "Status", "Value"]
+        ["TagName", "EventTime", "Status", "Value"],
     )
 
     # Attempt to initialize with an invalid mode
@@ -127,8 +131,5 @@ def test_gaussian_smoothing_invalid_mode(spark_session: SparkSession):
             id_col="TagName",
             mode="invalid_mode",  # Invalid mode
             timestamp_col="EventTime",
-            value_col="Value"
+            value_col="Value",
         )
-
-
-