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Add calories module for fitbit_readable_datetime.py

pull/95/head
Meng Li 2020-04-15 16:01:02 -04:00
parent 8671f8db68
commit 695984586f
2 changed files with 44 additions and 1 deletions
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2
config.yaml
View File

@ -2,7 +2,7 @@
SENSORS: [applications_crashes, applications_foreground, applications_notifications, battery, bluetooth, calls, locations, messages, plugin_ambient_noise, plugin_device_usage, plugin_google_activity_recognition, screen]
FITBIT_TABLE: [fitbit_data]
FITBIT_SENSORS: [heartrate, steps, sleep]
FITBIT_SENSORS: [heartrate, steps, sleep, calories]
# Participants to include in the analysis
# You must create a file for each participant

43
src/data/fitbit_readable_datetime.py
View File

@ -28,6 +28,12 @@ STEPS_COLUMNS = ("device_id",
"local_day_of_week", "local_time", "local_hour", "local_minute",
"local_day_segment")
CALORIES_COLUMNS = ("device_id",
"level", "mets", "value",
"local_date_time", "local_date", "local_month", "local_day",
"local_day_of_week", "local_time", "local_hour", "local_minute",
"local_day_segment")
def drop_duplicates(data, local_timezone):
"""
Data is pulled in intraday manner. Since data will be duplicated until the
@ -163,6 +169,39 @@ def parse_heartrate_data(heartrate_data):
return pd.DataFrame(data=records, columns=HR_COLUMNS)
def parse_calories_data(calories_data):
if calories_data.empty:
return pd.DataFrame(columns=CALORIES_COLUMNS)
device_id = calories_data["device_id"].iloc[0]
records = []
# Parse JSON into individual records
for record in calories_data.fitbit_data:
record = json.loads(record) # Parse text into JSON
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 = (device_id,
data["level"],
data["mets"],
data["value"],
d_datetime,
d_datetime.date(),
d_datetime.month,
d_datetime.day,
d_datetime.weekday(),
d_datetime.time(),
d_datetime.hour,
d_datetime.minute,
HOUR2EPOCH[d_datetime.hour])
records.append(row)
return pd.DataFrame(data=records, columns=CALORIES_COLUMNS)
fitbit_data = pd.read_csv(snakemake.input[0])
local_timezone = pytz.timezone(snakemake.params["local_timezone"])
@ -177,5 +216,9 @@ elif sensor == "sleep":
data_preprocesed = parse_sleep_data(data)
elif sensor == "steps":
data_preprocesed = parse_steps_data(data)
elif sensor == "calories":
data_preprocesed = parse_calories_data(data)
else:
raise ValueError("Please check the FITBIT_SENSORS list in config.yaml file.")
data_preprocesed.to_csv(snakemake.output[0], index=False)