Update fitbit_parse_steps.py: add summary data

src/data/fitbit_parse_steps.py

@@ -4,6 +4,11 @@ import numpy as np
 from datetime import datetime, timezone
 from math import trunc
 
+STEPS_SUMMARY_COLUMNS = ("device_id",
+                            "steps_rapids_intradaycountallsteps",
+                            "local_date_time",
+                            "timestamp")
+
 STEPS_INTRADAY_COLUMNS = ("device_id",
                             "steps",
                             "local_date_time",
@@ -14,12 +19,23 @@ def parseStepsData(steps_data):
     if steps_data.empty:
         return pd.DataFrame(), pd.DataFrame(columns=STEPS_INTRADAY_COLUMNS)
     device_id = steps_data["device_id"].iloc[0]
-    records_intraday = []
+    records_summary, records_intraday = [], []
     # Parse JSON into individual records
     for record in steps_data.fitbit_data:
         record = json.loads(record)  # Parse text into JSON
+
+        # Parse summary data
         curr_date = datetime.strptime(
             record["activities-steps"][0]["dateTime"], "%Y-%m-%d")
+        
+        row_summary = (device_id,
+            record["activities-steps"][0]["value"],
+            curr_date,
+            0)
+        
+        records_summary.append(row_summary)
+
+        # Parse intraday data
         dataset = record["activities-steps-intraday"]["dataset"]
         for data in dataset:
             d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
@@ -32,7 +48,7 @@ def parseStepsData(steps_data):
 
             records_intraday.append(row_intraday)
 
-    return pd.DataFrame(data=[], columns=["local_date_time", "timestamp"]), pd.DataFrame(data=records_intraday, columns=STEPS_INTRADAY_COLUMNS)
+    return pd.DataFrame(data=records_summary, columns=STEPS_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=STEPS_INTRADAY_COLUMNS)
 
 table_format = snakemake.params["table_format"]
 timezone = snakemake.params["timezone"]