54 lines
2.4 KiB
Python
54 lines
2.4 KiB
Python
import json
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import numpy as np
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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", "timestamp"]), pd.DataFrame(data=records_intraday, columns=CALORIES_INTRADAY_COLUMNS)
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table_format = snakemake.params["table_format"]
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timezone = snakemake.params["timezone"]
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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], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
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intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
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# if not pd.isnull(local_start_date) and not pd.isnull(local_end_date):
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if summary.shape[0] > 0:
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summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone, ambiguous=False, nonexistent="NaT").dropna().astype(np.int64) // 10**6
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summary.dropna(subset=['timestamp'], inplace=True)
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if intraday.shape[0] > 0:
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intraday["timestamp"] = intraday["local_date_time"].dt.tz_localize(timezone, ambiguous=False, nonexistent="NaT").dropna().astype(np.int64) // 10**6
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intraday.dropna(subset=['timestamp'], inplace=True)
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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) |