diff --git a/src/graphnet/models/graphs/utils.py b/src/graphnet/models/graphs/utils.py
index 9385bd33b..77669eaeb 100644
--- a/src/graphnet/models/graphs/utils.py
+++ b/src/graphnet/models/graphs/utils.py
@@ -51,7 +51,7 @@ def gather_cluster_sequence(
     Args:
         x:  Array for clustering
         feature_idx: Index of the feature in `x` to
-                     be gathered for each cluster.
+        be gathered for each cluster.
         cluster_columns: Index in `x` from which to build clusters.
 
     Returns:
@@ -66,10 +66,16 @@ def gather_cluster_sequence(
         x[:, cluster_columns], return_counts=True, axis=0
     )
     # sort DOMs and pulse-counts
-    sort_this = np.concatenate([unique_sensors, counts.reshape(-1, 1)], axis=1)
-    sort_this = lex_sort(x=sort_this, cluster_columns=cluster_columns)
-    unique_sensors = sort_this[:, 0 : unique_sensors.shape[1]]
-    counts = sort_this[:, unique_sensors.shape[1] :].flatten().astype(int)
+    sensor_counts = counts.reshape(-1, 1)
+    contingency_table = np.concatenate([unique_sensors, sensor_counts], axis=1)
+    sensors_in_contingency_table = np.arange(0, unique_sensors.shape[1], 1)
+    contingency_table = lex_sort(
+        x=contingency_table, cluster_columns=sensors_in_contingency_table
+    )
+    unique_sensors = contingency_table[:, 0 : unique_sensors.shape[1]]
+    count_part = contingency_table[:, unique_sensors.shape[1] :]
+    flattened_counts = count_part.flatten()
+    counts = flattened_counts.astype(int)
 
     # Pad unique sensor columns with NaN's up until the maximum number of
     # Same pmt-pulses. Each of padded columns represents a pulse.
@@ -129,8 +135,8 @@ def cluster_summarize_with_percentiles(
     then each row in the returned array will correspond to a DOM,
     and the time and charge for each DOM will be summarized by percentiles.
     Returned output array has dimensions
-    `[n_clusters, len(percentiles)*len(summarization_indices)
-    + len(cluster_indices)]`
+    `[n_clusters,
+    len(percentiles)*len(summarization_indices) + len(cluster_indices)]`
 
     Args:
         x: Array to be clustered
@@ -181,9 +187,9 @@ def ice_transparency(
 
     Returns:
         f_scattering: Function that takes a normalized depth and returns the
-                      corresponding normalized scattering length.
+        corresponding normalized scattering length.
         f_absorption: Function that takes a normalized depth and returns the
-                      corresponding normalized absorption length.
+        corresponding normalized absorption length.
     """
     # Data from page 31 of https://arxiv.org/pdf/1301.5361.pdf
     df = pd.read_parquet(