diff --git a/CI/unit_tests/distance_metrics/test_mahalanobis_distance.py b/CI/unit_tests/distance_metrics/test_mahalanobis_distance.py
index d156d03..d91687c 100644
--- a/CI/unit_tests/distance_metrics/test_mahalanobis_distance.py
+++ b/CI/unit_tests/distance_metrics/test_mahalanobis_distance.py
@@ -135,12 +135,12 @@ def create_sample_set():
         -------
         Creates a random normal distributed sample set
         """
-        point_1 = np.array([
-            onp.random.normal(0, 10, 100), onp.random.normal(0, 20, 100)
-        ]).T
-        point_2 = np.array([
-            onp.random.normal(0, 10, 100), onp.random.normal(0, 20, 100)
-        ]).T
+        point_1 = np.array(
+            [onp.random.normal(0, 10, 100), onp.random.normal(0, 20, 100)]
+        ).T
+        point_2 = np.array(
+            [onp.random.normal(0, 10, 100), onp.random.normal(0, 20, 100)]
+        ).T
         return point_1, point_2
 
     @staticmethod
diff --git a/znnl/training_recording/jax_recording.py b/znnl/training_recording/jax_recording.py
index 862b7d4..9ba92f8 100644
--- a/znnl/training_recording/jax_recording.py
+++ b/znnl/training_recording/jax_recording.py
@@ -254,17 +254,19 @@ def instantiate_recorder(self, data_set: dict = None, overwrite: bool = False):
             self._index_count = 0
 
         # Check if we need an NTK computation and update the class accordingly
-        if any([
-            "ntk" in self._selected_properties,
-            "covariance_ntk" in self._selected_properties,
-            "magnitude_ntk" in self._selected_properties,
-            "entropy" in self._selected_properties,
-            "magnitude_entropy" in self._selected_properties,
-            "magnitude_variance" in self._selected_properties,
-            "covariance_entropy" in self._selected_properties,
-            "eigenvalues" in self._selected_properties,
-            "trace" in self._selected_properties,
-        ]):
+        if any(
+            [
+                "ntk" in self._selected_properties,
+                "covariance_ntk" in self._selected_properties,
+                "magnitude_ntk" in self._selected_properties,
+                "entropy" in self._selected_properties,
+                "magnitude_entropy" in self._selected_properties,
+                "magnitude_variance" in self._selected_properties,
+                "covariance_entropy" in self._selected_properties,
+                "eigenvalues" in self._selected_properties,
+                "trace" in self._selected_properties,
+            ]
+        ):
             self._compute_ntk = True
 
         if "loss_derivative" in self._selected_properties:
diff --git a/znnl/visualization/tsne_visualizer.py b/znnl/visualization/tsne_visualizer.py
index 40618e0..d0d613a 100644
--- a/znnl/visualization/tsne_visualizer.py
+++ b/znnl/visualization/tsne_visualizer.py
@@ -165,20 +165,24 @@ def run_visualization(self):
         }
 
         # Add initial data
-        fig_dict["data"].append({
-            "x": self.dynamic[0][:, 0],
-            "y": self.dynamic[0][:, 1],
-            "mode": "markers",
-            "name": "Predictor",
-        })
-        fig_dict["data"].append({
-            "x": self.reference[0][:, 0],
-            "y": self.reference[0][:, 1],
-            "mode": "markers",
-            "xaxis": "x2",
-            "yaxis": "y2",
-            "name": "Target",
-        })
+        fig_dict["data"].append(
+            {
+                "x": self.dynamic[0][:, 0],
+                "y": self.dynamic[0][:, 1],
+                "mode": "markers",
+                "name": "Predictor",
+            }
+        )
+        fig_dict["data"].append(
+            {
+                "x": self.reference[0][:, 0],
+                "y": self.reference[0][:, 1],
+                "mode": "markers",
+                "xaxis": "x2",
+                "yaxis": "y2",
+                "name": "Target",
+            }
+        )
 
         # Make the figure frames.
         for i, item in enumerate(self.dynamic):