src/features/phone_esm/straw/process_user_event_related_segments.py

@@ -46,15 +46,13 @@ def extract_ers_from_file(esm_df, device_id): # TODO: session_id groupby -> spre
 
     # Extract time-relevant information
     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 seconds
-    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
-    # TODO: Rename "timestamp" column meaningfully.
+    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 
 
     time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
 
     extracted_ers["label"] = "straw_event_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
     extracted_ers["event_timestamp"] = esm_df.groupby("esm_session")['timestamp'].min().reset_index()['timestamp']
     extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x))
-    # TODO: Think about adding questionnaire duration.
     extracted_ers["shift"] = time_before_questionnaire
     extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: format_timestamp(x))
     extracted_ers["shift_direction"] = -1