diff --git a/nextflow/configs/profiles/sumner2.config b/nextflow/configs/profiles/sumner2.config
index 49b11d7..7b1dfdd 100644
--- a/nextflow/configs/profiles/sumner2.config
+++ b/nextflow/configs/profiles/sumner2.config
@@ -29,7 +29,7 @@ params {
     tracking_code_dir = "/kumar_lab_models/mouse-tracking-runtime/"
     gait_code_dir = "/gait-analysis/"
     vfi_code_dir = "/vfi/Code/"
-    support_code_dir = "/mouse-tracking-runtime/support_code/"
+    support_code_dir = "/workspace/support_code/"
     heuristic_classifier_folder = "/opt/JABS-postprocess/src/jabs_postprocess/heuristic_classifiers/"
     filter_processed = false
     model_dir = "/projects/kumar-lab/multimouse-pipeline/nextflow-artifacts/neural_net_models/"
@@ -90,7 +90,7 @@ params {
         ],
     ]
 
-    heuristic_classifiers = ["corner", "corner_facing", "freeze", "locomotion", "periphery", "wall_facing"]
+    heuristic_classifiers = ["corner", "corner_facing", "freeze", "locomotion", "periphery", "wall_facing", "locomotion_corner", "locomotion_periphery"]
 
     // Number of 5-minute bins for transforming summary tables into bins
     // 1 = 5 minutes, 4 = 20 minutes, etc.
@@ -182,7 +182,7 @@ process {
         container = "/projects/kumar-lab/meta/images/JABS-behavior-classifier/headless/v0.36.1/latest.sif"
     }
     withLabel: "jabs_postprocess" {
-        container = "/projects/kumar-lab/meta/images/JABS-postprocess/jabs-postprocess/v0.4.2/latest.sif"
+        container = "/projects/kumar-lab/meta/images/JABS-postprocess/jabs-postprocess/v0.5.1/latest.sif"
     }
     withLabel: "jabs_table_convert" {
         container = "/projects/kumar-lab/meta/images/mouse-tracking-runtime/RBase/v0.1.4/latest.sif"
diff --git a/nextflow/modules/jabs_classifiers.nf b/nextflow/modules/jabs_classifiers.nf
index a08ae5f..d53520b 100644
--- a/nextflow/modules/jabs_classifiers.nf
+++ b/nextflow/modules/jabs_classifiers.nf
@@ -171,7 +171,7 @@ process PREDICT_HEURISTICS {
  * @return features The generated feature file.
  */
 process BEHAVIOR_TABLE_TO_FEATURES {
-    label "jabs_table_convert"
+    label "tracking"
     label "cpu"
     label "r_jabs_table_convert"
 
@@ -183,7 +183,7 @@ process BEHAVIOR_TABLE_TO_FEATURES {
 
     script:
     """
-    Rscript ${params.support_code_dir}behavior_summaries.R -f ${in_summary_table} -b ${bin_size} -o "${in_summary_table.baseName}_features_${bin_size}.csv"
+    python3 ${params.support_code_dir}/behavior_summaries.py -f ${in_summary_table} -b ${bin_size} -o "${in_summary_table.baseName}_features_${bin_size}.csv"
     """
 }
 
diff --git a/support_code/behavior_summaries.py b/support_code/behavior_summaries.py
index 6621767..a24a26f 100644
--- a/support_code/behavior_summaries.py
+++ b/support_code/behavior_summaries.py
@@ -5,8 +5,10 @@
 from JABS postprocessing summary tables, calculating metrics like time spent in
 behaviors and distances traveled.
 """
+
 import argparse
 
+import numpy as np
 import pandas as pd
 
 
@@ -100,6 +102,14 @@ def get_columns_to_exclude(behavior: str) -> list:
         "bout_behavior",
         "not_behavior_dist",
         "behavior_dist",
+        "behavior_dist_threshold",
+        "behavior_dist_seg",
+        "avg_bout_duration",
+        "_stats_sample_count",
+        "bout_duration_std",
+        "bout_duration_var",
+        "latency_to_first_prediction",
+        "latency_to_last_prediction",
     ]
     return [f"{behavior}_{suffix}" for suffix in suffixes]
 
@@ -129,10 +139,11 @@ def aggregate_data_by_bin_size(
     time_behavior_col = f"{behavior}_time_behavior"
     time_not_behavior_col = f"{behavior}_time_not_behavior"
     behavior_dist_col = f"{behavior}_behavior_dist"
+    behavior_bout_col = f"{behavior}_bout_behavior"
 
     # Calculate time spent in behavior
     # TODO: Do we need to make `5` a configurable parameter?
-    aggregated[f"bin_avg_{bin_size*5}.{behavior}_time_secs"] = (
+    aggregated[f"bin_sum_{bin_size * 5}.{behavior}_time_secs"] = (
         aggregated[time_behavior_col]
         / (aggregated[time_behavior_col] + aggregated[time_not_behavior_col])
         * bin_size
@@ -141,9 +152,40 @@ def aggregate_data_by_bin_size(
 
     # Calculate average distance (in cm)
     # TODO: Do we need to make `5` a configurable parameter?
-    aggregated[f"bin_avg_{bin_size*5}.{behavior}_distance_cm"] = aggregated[
+    aggregated[f"bin_avg_{bin_size * 5}.{behavior}_distance_cm"] = aggregated[
         behavior_dist_col
     ] / (bin_size * 5)
+    aggregated[f"bin_sum_{bin_size * 5}.{behavior}_distance_cm"] = aggregated[
+        behavior_dist_col
+    ]
+    aggregated[f"bin_sum_{bin_size * 5}.{behavior}_distance_cm_threshold"] = aggregated[
+        f"{behavior}_behavior_dist_threshold"
+    ]
+    aggregated[f"bin_sum_{bin_size * 5}.{behavior}_distance_cm_seg"] = aggregated[
+        f"{behavior}_behavior_dist_seg"
+    ]
+
+    # Sum up bout count
+    aggregated[f"bin_sum_{bin_size * 5}.{behavior}_bout_behavior"] = aggregated[
+        behavior_bout_col
+    ]
+
+    # Additional stats
+    if np.sum(aggregated[f"{behavior}__stats_sample_count"]) == 0:
+        aggregated[f"bin_avg_{bin_size * 5}.{behavior}_avg_bout_length"] = np.nan
+    else:
+        aggregated[f"bin_avg_{bin_size * 5}.{behavior}_avg_bout_length"] = np.average(
+            aggregated[f"{behavior}_avg_bout_duration"],
+            weights=aggregated[f"{behavior}__stats_sample_count"],
+        )
+    # TODO: var and std need to be aggregated across bins.
+    # This is non-trivial because of the partial bouts and their associated weights.
+    aggregated[f"bin_first_{bin_size * 5}.{behavior}_latency_first_prediction"] = (
+        aggregated[f"{behavior}_latency_to_first_prediction"].head(1)
+    )
+    aggregated[f"bin_last_{bin_size * 5}.{behavior}_latency_last_prediction"] = (
+        aggregated[f"{behavior}_latency_to_last_prediction"].tail(1)
+    )
 
     # Reset index to make MouseID a regular column
     return aggregated.reset_index()