73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
import pandas as pd
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import pytz, json
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from datetime import datetime
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NIGHT = "night"
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MORNING = "morning"
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AFTERNOON = "afternoon"
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EVENING = "evening"
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HOUR2EPOCH = [NIGHT] * 6 + [MORNING] * 6 + [AFTERNOON] * 6 + [EVENING] * 6
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STEPS_COLUMNS = ("device_id",
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"steps",
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"local_date_time",
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"local_date",
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"local_month",
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"local_day",
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"local_day_of_week",
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"local_time",
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"local_hour",
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"local_minute",
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"local_day_segment")
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fitbit_data = pd.read_csv(snakemake.input[0])
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steps_data = fitbit_data[fitbit_data["fitbit_data_type"] == "steps"]
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local_timezone = pytz.timezone(snakemake.params["local_timezone"])
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"""
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Data is pulled in intraday manner. Since data will be duplicated until the
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last record from that day, first sort by time, then drop all but
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the last record for each day. Drop duplicates based on aware timestamp.
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"""
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local_date_col = steps_data["timestamp"].apply(lambda ts: str(datetime.fromtimestamp(ts/1000, tz=local_timezone).date()))
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steps_data = steps_data.assign(local_date=local_date_col.values)
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steps_data.sort_values(by="timestamp", ascending=True, inplace=True)
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steps_data.drop_duplicates(subset="local_date", keep="last", inplace=True)
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device_id = steps_data["device_id"].iloc[0]
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records = []
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# Parse JSON into individual records
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for record in steps_data.fitbit_data:
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record = json.loads(record) # Parse text into JSON
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curr_date = datetime.strptime(
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record["activities-steps"][0]["dateTime"], "%Y-%m-%d")
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dataset = record["activities-steps-intraday"]["dataset"]
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for data in dataset:
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d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
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d_datetime = datetime.combine(curr_date, d_time)
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# Create tuple of parsed data
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row = (device_id,
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data["value"],
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d_datetime,
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d_datetime.date(),
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d_datetime.month,
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d_datetime.day,
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d_datetime.weekday(),
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d_datetime.time(),
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d_datetime.hour,
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d_datetime.minute,
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HOUR2EPOCH[d_datetime.hour])
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# Append the data to a list
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records.append(row)
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# Create a new DataFrame from the list of tuples.
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steps_preprocessed = pd.DataFrame(data=records, columns=STEPS_COLUMNS)
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steps_preprocessed.to_csv(snakemake.output[0], index=False)
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