Get, join and start processing required ERS stress event data.

src/features/phone_esm/straw/process_user_event_related_segments.py

@@ -22,6 +22,7 @@ def format_timestamp(x):
     
     return tstring
 
+
 def extract_ers_from_file(esm_df, device_id): 
 
     pd.set_option("display.max_rows", 20)
@@ -31,7 +32,7 @@ def extract_ers_from_file(esm_df, device_id):
         config = yaml.load(stream, Loader=yaml.FullLoader)
 
     
-    pd.DataFrame().to_csv(snakemake.output[1]) # Create an empty stress event file either way
+    pd.DataFrame().to_csv(snakemake.output[1]) # Create an empty stress event file either way TODO
 
 
     esm_preprocessed = clean_up_esm(preprocess_esm(esm_df))
@@ -45,7 +46,7 @@ def extract_ers_from_file(esm_df, device_id):
     targets_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"]
     if targets_method in ["30_before", "90_before"]: # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
         # 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() # is rounded up in seconds
+        extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # questionnaire length
         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 
@@ -72,19 +73,56 @@ def extract_ers_from_file(esm_df, device_id):
             extracted_ers["shift"] = extracted_ers["diffs"].apply(lambda x: format_timestamp(x))
 
     elif targets_method == "stress_event":
-        
-        pd.DataFrame().to_csv(snakemake.output[1])
-        
         # TODO: generiranje ERS datoteke za stress_events
+
+        # Get and join required data
+        extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index().rename(columns={'timestamp': 'session_length'}) # questionnaire end timestamp
+        session_end_timestamp = esm_df.groupby(['device_id', 'esm_session'])['timestamp'].max().to_frame().rename(columns={'timestamp': 'session_end_timestamp'}) # questionnaire end timestamp
+        se_time = esm_df[esm_df.questionnaire_id == 90.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_time'})
+        se_duration = esm_df[esm_df.questionnaire_id == 91.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_duration'})
+        se_intensity = esm_df[esm_df.questionnaire_id == 87.].set_index(['device_id', 'esm_session'])['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'se_intensity'})
+
+        extracted_ers = extracted_ers.join(session_end_timestamp, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_time, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_duration, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_intensity, on=['device_id', 'esm_session'], how='inner')
+                            
+        # Filter sessions that are not useful
+        extracted_ers = extracted_ers[(extracted_ers.se_time != "0 - Ne spomnim se")]
+
+        # Transform data into its final form, ready for the extraction
+        extracted_ers.reset_index(inplace=True)
+        extracted_ers["label"] = f"straw_event_{targets_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
+        
+        # Convert to unix timestamp
+        
+        time_before_event = 90 * 60 # in seconds (10 minutes)
+        extracted_ers['event_timestamp'] = pd.to_datetime(extracted_ers['se_time']).apply(lambda x: x.timestamp() * 1000).astype('int64')
+        extracted_ers['shift'] = time_before_event
+        extracted_ers['shift_direction'] = -1
+
+        print(extracted_ers[['session_end_timestamp', 'event_timestamp']])
+
+        extracted_ers['se_duration'] = \
+            np.where(extracted_ers['se_duration'] == "1 - Še vedno traja", 
+            extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'], 
+            extracted_ers['se_duration'])
+
+        extracted_ers['se_duration'] = \
+            extracted_ers['se_duration'].apply(lambda x: math.ceil(x / 1000) if isinstance(x, int) else (pd.to_datetime(x).hour * 60 + pd.to_datetime(x).minute) * 60)
+
+        sys.exit()
         # 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']]
-        # 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[filter_esm.esm_user_answer == "1 - Še vedno traja"].shape)
+        print(filter_esm.shape)
-        # print(filter_esm.shape)
         
         # TODO: generiranje stress_events_targets datoteke (dodaj tudi stolpec s pid) + dodati moraš merge metodo, ki bo združila te datoteke
         # TODO: na koncu se mora v čistilni skripti ustrezno odstraniti vse targete in prilepiti nove targete zraven ustreznih segmentov (zna se zgoditi, da bodo overlap)
 
+        pd.DataFrame().to_csv(snakemake.output[1])
+        sys.exit()
+        
     else:
         raise Exception("Please select correct target method for the event-related segments.")
         extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])