diff --git a/aeon/dj_pipeline/__init__.py b/aeon/dj_pipeline/__init__.py
index 72e57718..d8f201c0 100644
--- a/aeon/dj_pipeline/__init__.py
+++ b/aeon/dj_pipeline/__init__.py
@@ -45,6 +45,7 @@ def fetch_stream(query, drop_pk=True):
     df.rename(columns={"timestamps": "time"}, inplace=True)
     df.set_index("time", inplace=True)
     df.sort_index(inplace=True)
+    df = df.convert_dtypes(convert_string=False, convert_integer=False, convert_boolean=False, convert_floating=False)
     return df
 
 
diff --git a/aeon/dj_pipeline/analysis/block_analysis.py b/aeon/dj_pipeline/analysis/block_analysis.py
index 4d5b0943..27fe4812 100644
--- a/aeon/dj_pipeline/analysis/block_analysis.py
+++ b/aeon/dj_pipeline/analysis/block_analysis.py
@@ -189,18 +189,42 @@ def make(self, key):
         patch_keys, patch_names = patch_query.fetch("KEY", "underground_feeder_name")
 
         for patch_key, patch_name in zip(patch_keys, patch_names):
-            delivered_pellet_df = fetch_stream(
+            # pellet delivery and patch threshold data
+            beam_break_df = fetch_stream(
                 streams.UndergroundFeederBeamBreak & patch_key & chunk_restriction
             )[block_start:block_end]
             depletion_state_df = fetch_stream(
                 streams.UndergroundFeederDepletionState & patch_key & chunk_restriction
             )[block_start:block_end]
+            # remove NaNs from threshold column
+            depletion_state_df = depletion_state_df.dropna(subset=["threshold"])
+            # identify & remove invalid indices where the time difference is less than 1 second
+            invalid_indices = np.where(depletion_state_df.index.to_series().diff().dt.total_seconds() < 1)[0]
+            depletion_state_df = depletion_state_df.drop(depletion_state_df.index[invalid_indices])
+
+            # find pellet times associated with each threshold update
+            #   for each threshold, find the time of the next threshold update,
+            #   find the closest beam break after this update time,
+            #   and use this beam break time as the delivery time for the initial threshold
+            pellet_ts_threshold_df = depletion_state_df.copy()
+            pellet_ts_threshold_df["pellet_timestamp"] = pd.NaT
+            for threshold_idx in range(len(pellet_ts_threshold_df) - 1):
+                if np.isnan(pellet_ts_threshold_df.threshold.iloc[threshold_idx]):
+                    continue
+                next_threshold_time = pellet_ts_threshold_df.index[threshold_idx + 1]
+                post_thresh_pellet_ts = beam_break_df.index[beam_break_df.index > next_threshold_time]
+                next_beam_break = post_thresh_pellet_ts[np.searchsorted(post_thresh_pellet_ts, next_threshold_time)]
+                pellet_ts_threshold_df.pellet_timestamp.iloc[threshold_idx] = next_beam_break
+            # remove NaNs from pellet_timestamp column (last row)
+            pellet_ts_threshold_df = pellet_ts_threshold_df.dropna(subset=["pellet_timestamp"])
+
+            # wheel encoder data
             encoder_df = fetch_stream(streams.UndergroundFeederEncoder & patch_key & chunk_restriction)[
                 block_start:block_end
             ]
             # filter out maintenance period based on logs
-            pellet_df = filter_out_maintenance_periods(
-                delivered_pellet_df,
+            pellet_ts_threshold_df = filter_out_maintenance_periods(
+                pellet_ts_threshold_df,
                 maintenance_period,
                 block_end,
                 dropna=True,
@@ -229,7 +253,6 @@ def make(self, key):
 
             patch_rate = depletion_state_df.rate.iloc[0]
             patch_offset = depletion_state_df.offset.iloc[0]
-
             # handles patch rate value being INF
             patch_rate = 999999999 if np.isinf(patch_rate) else patch_rate
 
@@ -237,14 +260,14 @@ def make(self, key):
                 {
                     **key,
                     "patch_name": patch_name,
-                    "pellet_count": len(pellet_df),
-                    "pellet_timestamps": pellet_df.index.values,
+                    "pellet_count": len(pellet_ts_threshold_df),
+                    "pellet_timestamps": pellet_ts_threshold_df.pellet_timestamp.values,
                     "wheel_cumsum_distance_travelled": encoder_df.distance_travelled.values[
                         ::wheel_downsampling_factor
                     ],
                     "wheel_timestamps": encoder_df.index.values[::wheel_downsampling_factor],
-                    "patch_threshold": depletion_state_df.threshold.values,
-                    "patch_threshold_timestamps": depletion_state_df.index.values,
+                    "patch_threshold": pellet_ts_threshold_df.threshold.values,
+                    "patch_threshold_timestamps": pellet_ts_threshold_df.index.values,
                     "patch_rate": patch_rate,
                     "patch_offset": patch_offset,
                 }
@@ -267,7 +290,7 @@ def make(self, key):
         subject_names = []
         for subject_name in set(subject_visits_df.id):
             _df = subject_visits_df[subject_visits_df.id == subject_name]
-            if _df.type[-1] != "Exit":
+            if _df.type.iloc[-1] != "Exit":
                 subject_names.append(subject_name)
 
         for subject_name in subject_names:
@@ -454,7 +477,7 @@ def make(self, key):
                         "dist_to_patch"
                     ].values
 
-            # Get closest subject to patch at each pel del timestep
+            # Get closest subject to patch at each pellet timestep
             closest_subjects_pellet_ts = dist_to_patch_pel_ts_id_df.idxmin(axis=1)
             # Get closest subject to patch at each wheel timestep
             cum_wheel_dist_subj_df = pd.DataFrame(
@@ -481,9 +504,10 @@ def make(self, key):
                 all_subj_patch_pref_dict[patch["patch_name"]][subject_name][
                     "cum_time"
                 ] = subject_in_patch_cum_time
-                subj_pellets = closest_subjects_pellet_ts[closest_subjects_pellet_ts == subject_name]
 
-                subj_patch_thresh = patch["patch_threshold"][np.searchsorted(patch["patch_threshold_timestamps"], subj_pellets.index.values) - 1]
+                closest_subj_mask = closest_subjects_pellet_ts == subject_name
+                subj_pellets = closest_subjects_pellet_ts[closest_subj_mask]
+                subj_patch_thresh = patch["patch_threshold"][closest_subj_mask]
 
                 self.Patch.insert1(
                     key