diff --git a/examples/DynaCLR/DynaCLR-classical-sampling/create_pseudo_tracks.py b/examples/DynaCLR/DynaCLR-classical-sampling/create_pseudo_tracks.py
index b225d17c6..15a2e22cd 100644
--- a/examples/DynaCLR/DynaCLR-classical-sampling/create_pseudo_tracks.py
+++ b/examples/DynaCLR/DynaCLR-classical-sampling/create_pseudo_tracks.py
@@ -9,14 +9,12 @@
 
 # %% create training and validation dataset
 # TODO: Modify path to the input data
-input_data_path = "/training_data.zarr"
-# TODO: Modify path to the output data
-track_data_path = "/training_data_tracks.zarr"
-
+input_track_path = "/hpc/projects/intracellular_dashboard/organelle_dynamics/2025_07_24_A549_SEC61_TOMM20_G3BP1_ZIKV/1-preprocess/label-free/3-track/2025_07_24_A549_SEC61_TOMM20_G3BP1_ZIKV_cropped.zarr"
+output_track_path = "/hpc/projects/organelle_phenotyping/models/SEC61_TOMM20_G3BP1_Sensor/time_interval/dynaclr_gfp_rfp_ph_2D/classical/data/2025_07_24_A549_SEC61_TOMM20_G3BP1_ZIKV_classical_fake_tracks.zarr"
 # TODO: Modify the channel name to the one you are using for the segmentation mask
-segmentation_channel_name = "Nucl_mask"
+segmentation_channel_name = "nuclei_prediction_labels_labels"
 # TODO: Modify the z-slice to the one you are using for the segmentation mask
-Z_SLICE = 30
+Z_SLICE = 0
 # %%
 """
 Add csvs with fake tracking to tracking data. 
@@ -67,14 +65,13 @@ def save_track_df(track_df, well_id, pos_name, out_path):
 def main():
     # Load the input segmentation data
     zarr_input = open_ome_zarr(
-        input_data_path,
-        layout="hcs",
-        mode="r+",
+        input_track_path,
+        mode="r",
     )
     chan_names = zarr_input.channel_names
-    assert (
-        segmentation_channel_name in chan_names
-    ), "Channel name not found in the input data"
+    assert segmentation_channel_name in chan_names, (
+        "Channel name not found in the input data"
+    )
 
     # Create the empty store for the tracking data
     position_names = []
@@ -82,23 +79,19 @@ def main():
         position_names.append(tuple(ds.split("/")))
 
     create_empty_plate(
-        store_path=track_data_path,
+        store_path=output_track_path,
         position_keys=position_names,
-        channel_names=segmentation_channel_name,
+        channel_names=[segmentation_channel_name],
         shape=(1, 1, 1, *position.data.shape[3:]),
         chunks=position.data.chunks,
         scale=position.scale,
     )
-
+    #
     # Populate the tracking data
-    with open_ome_zarr(track_data_path, layout="hcs", mode="r+") as track_store:
+    with open_ome_zarr(output_track_path, layout="hcs", mode="r+") as track_store:
         # Create progress bar for wells and positions
         for well_id, well_data in tqdm(zarr_input.wells(), desc="Processing wells"):
-            for pos_name, pos_data in tqdm(
-                well_data.positions(),
-                desc=f"Processing positions in {well_id}",
-                leave=False,
-            ):
+            for pos_name, pos_data in well_data.positions():
                 data = pos_data.data
                 T, C, Z, Y, X = data.shape
                 track_df_all = pd.DataFrame()
@@ -110,7 +103,7 @@ def main():
                     track_pos["0"][0, 0, 0] = seg_mask
                     track_df = create_track_df(seg_mask, time)
                     track_df_all = pd.concat([track_df_all, track_df])
-                save_track_df(track_df_all, well_id, pos_name, track_data_path)
+                save_track_df(track_df_all, well_id, pos_name, output_track_path)
     zarr_input.close()