34 lines
1.6 KiB
Python
34 lines
1.6 KiB
Python
import json
|
|
import pandas as pd
|
|
from datetime import datetime
|
|
|
|
CALORIES_INTRADAY_COLUMNS = ("device_id", "level", "mets", "value", "local_date_time", "timestamp")
|
|
|
|
def parseCaloriesData(calories_data):
|
|
if calories_data.empty:
|
|
return pd.DataFrame(columns=CALORIES_INTRADAY_COLUMNS)
|
|
device_id = calories_data["device_id"].iloc[0]
|
|
records_intraday = []
|
|
|
|
# Parse JSON into individual records
|
|
for record in calories_data.json_fitbit_column:
|
|
record = json.loads(record) # Parse text into JSON
|
|
if "activities-calories" in record and "activities-calories-intraday" in record:
|
|
curr_date = datetime.strptime(record["activities-calories"][0]["dateTime"], "%Y-%m-%d")
|
|
dataset = record["activities-calories-intraday"]["dataset"]
|
|
for data in dataset:
|
|
d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
|
|
d_datetime = datetime.combine(curr_date, d_time)
|
|
row_intraday = (device_id, data["level"], data["mets"], data["value"], d_datetime, 0)
|
|
records_intraday.append(row_intraday)
|
|
|
|
return pd.DataFrame(data=records_intraday, columns=CALORIES_INTRADAY_COLUMNS)
|
|
|
|
def main(json_raw, stream_parameters):
|
|
parsed_data = parseCaloriesData(json_raw)
|
|
parsed_data["timestamp"] = 0 # this column is added at readable_datetime.R because we neeed to take into account multiple timezones
|
|
parsed_data["mets"] = parsed_data["mets"] / 10
|
|
if pd.api.types.is_datetime64_any_dtype( parsed_data['local_date_time']):
|
|
parsed_data['local_date_time'] = parsed_data['local_date_time'].dt.strftime('%Y-%m-%d %H:%M:%S')
|
|
return(parsed_data)
|