46 lines
1.9 KiB
Python
46 lines
1.9 KiB
Python
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import json
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import pandas as pd
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from datetime import datetime
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CALORIES_INTRADAY_COLUMNS = ("device_id",
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"level", "mets", "value",
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"local_date_time", "timestamp")
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def parseCaloriesData(calories_data):
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if calories_data.empty:
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return pd.DataFrame(), pd.DataFrame(columns=CALORIES_INTRADAY_COLUMNS)
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device_id = calories_data["device_id"].iloc[0]
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records_intraday = []
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# Parse JSON into individual records
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for record in calories_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-calories"][0]["dateTime"], "%Y-%m-%d")
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dataset = record["activities-calories-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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row_intraday = (device_id,
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data["level"], data["mets"], data["value"],
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d_datetime, 0)
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records_intraday.append(row_intraday)
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return pd.DataFrame(data=[], columns=["local_date_time"]), pd.DataFrame(data=records_intraday, columns=CALORIES_INTRADAY_COLUMNS)
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table_format = snakemake.params["table_format"]
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if table_format == "JSON":
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json_raw = pd.read_csv(snakemake.input[0])
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summary, intraday = parseCaloriesData(json_raw)
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elif table_format == "CSV":
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summary = pd.read_csv(snakemake.input[0])
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intraday = pd.read_csv(snakemake.input[1])
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summary["timestamp"] = (summary["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000
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intraday["timestamp"] = (intraday["local_date_time"] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') * 1000
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summary.to_csv(snakemake.output["summary_data"], index=False)
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intraday.to_csv(snakemake.output["intraday_data"], index=False)
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