diff --git a/src/features/all_cleaning_individual/straw/main.py b/src/features/all_cleaning_individual/straw/main.py
index 387637f7..31a51367 100644
--- a/src/features/all_cleaning_individual/straw/main.py
+++ b/src/features/all_cleaning_individual/straw/main.py
@@ -14,7 +14,6 @@ from src.features import empatica_data_yield as edy
 pd.set_option('display.max_columns', 20)
 
 def straw_cleaning(sensor_data_files, provider):
-    # TODO (maybe): reorganize the script based on the overall 
     
     features = pd.read_csv(sensor_data_files["sensor_data"][0])
     
diff --git a/src/features/phone_esm/straw/process_user_event_related_segments.py b/src/features/phone_esm/straw/process_user_event_related_segments.py
index 679dc062..d9574c89 100644
--- a/src/features/phone_esm/straw/process_user_event_related_segments.py
+++ b/src/features/phone_esm/straw/process_user_event_related_segments.py
@@ -23,7 +23,7 @@ def format_timestamp(x):
     return tstring
 
 
-def extract_ers_from_file(esm_df, device_id): # TODO: session_id groupby -> spremeni naziv segmenta
+def extract_ers_from_file(esm_df, device_id): 
 
     pd.set_option("display.max_rows", None)
     pd.set_option("display.max_columns", None)
@@ -60,9 +60,7 @@ def extract_ers_from_file(esm_df, device_id): # TODO: session_id groupby -> spre
 
     return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]
 
-# TODO: potrebno preveriti kako se izvaja iskanje prek device_id -> na tem temelji tudi proces ekstrahiranja ERS
-
-if snakemake.params["stage"] == "extract": # TODO: najprej preveri ustreznost umeščenosti v RAPIDS pipelineu
+if snakemake.params["stage"] == "extract": 
     esm_df = pd.read_csv(input_data_files['esm_raw_input'])
 
     with open(input_data_files['pid_file'], 'r') as stream: