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 a109b60f..3f75359e 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
@@ -24,7 +24,7 @@ def format_timestamp(x):
 
 def extract_ers_from_file(esm_df, device_id): 
 
-    # pd.set_option("display.max_rows", None)
+    pd.set_option("display.max_rows", 20)
     pd.set_option("display.max_columns", None)
 
     with open('config.yaml', 'r') as stream:
@@ -37,26 +37,55 @@ def extract_ers_from_file(esm_df, device_id):
     classified = classify_sessions_by_completion_time(esm_preprocessed)
     esm_filtered_sessions = classified[classified["session_response"] == 'ema_completed'].reset_index()[['device_id', 'esm_session']]
     esm_df = esm_preprocessed.loc[(esm_preprocessed['device_id'].isin(esm_filtered_sessions['device_id'])) & (esm_preprocessed['esm_session'].isin(esm_filtered_sessions['esm_session']))]
+    
+
+
+    # Problem ne bo ekstrahiranje posameznih začetkov in trajanj stresnih dogodkov - večji problem je pridobitev ustreznega targeta, 
+    # tako da bo poravnan s tem dogodkom, saj se lahko zgodi, da je timestamp zabeležene intenzitete stresnega dogodka ne pade v okno stresnega dogodka.
+    # Edina izjema tega so, če je označen odgovor "1 - Še vedno traja" pri vprašanju appraisal_event_duration
 
     # Extract time-relevant information
     targets_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"]
-    
-    if targets_method == "thirty_before": # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
-        time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
 
-        extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # in rounded up in seconds
+
+    if targets_method in ["30_before", "90_before"]: # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
+
+        extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # is rounded up in seconds
+        extracted_ers["label"] = f"straw_event_{targets_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3) 
         extracted_ers[['event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].min().reset_index()[['timestamp', 'device_id']]
         extracted_ers = extracted_ers[extracted_ers["timestamp"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min 
-        extracted_ers["label"] = "straw_event_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
-        extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x))
-        extracted_ers["shift"] = time_before_questionnaire
-        extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: format_timestamp(x))
         extracted_ers["shift_direction"] = -1 
-    
+
+        if targets_method == "30_before":
+            time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
+
+            extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x))
+            extracted_ers["shift"] = time_before_questionnaire
+            extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: format_timestamp(x))
+        
+        elif targets_method == "90_before":
+            time_before_questionnaire = 90 * 60 # in seconds (90 minutes)
+
+            extracted_ers[['end_event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].max().reset_index()[['timestamp', 'device_id']]
+
+            extracted_ers['diffs'] = extracted_ers['event_timestamp'].astype('int64') - extracted_ers['end_event_timestamp'].shift(1, fill_value=0).astype('int64')
+            extracted_ers.loc[extracted_ers['diffs'] > time_before_questionnaire * 1000, 'diffs'] = time_before_questionnaire * 1000
+
+            # TODO: združi celotno trajanje in formatiraj v HH:MM:SS
+
+            sys.exit()
+    elif targets_method == "stress_events":
+        pass
+        # VV Testiranje različnih povpraševanj za VV
+        # print(esm_df[esm_df.questionnaire_id == 87])
+        # filter_esm = esm_df[(esm_df.esm_type == 7) & ((esm_df.questionnaire_id == 90.) | (esm_df.questionnaire_id == 91.))][['questionnaire_id', 'esm_user_answer', 'esm_session']]
+        # print(filter_esm[filter_esm.esm_user_answer == "1 - Še vedno traja"].shape)
+        # print(filter_esm.shape)
+        
     else:
         extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
 
-    # sys.exit()
+    sys.exit()
     return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]
 
 if snakemake.params["stage"] == "extract":