266 lines
12 KiB
Python
266 lines
12 KiB
Python
import json, yaml
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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import dateutil.parser
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SLEEP_CODE2LEVEL = ["asleep", "restless", "awake"]
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SLEEP_SUMMARY_COLUMNS_V1_2 = ("device_id", "efficiency",
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"minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed",
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"is_main_sleep", "type",
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"local_start_date_time", "local_end_date_time",
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"timestamp")
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SLEEP_SUMMARY_COLUMNS_V1 = SLEEP_SUMMARY_COLUMNS_V1_2 + ("count_awake", "duration_awake", "count_awakenings", "count_restless", "duration_restless")
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SLEEP_INTRADAY_COLUMNS = ("device_id",
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# For "classic" type, original_level is one of {"awake", "restless", "asleep"}
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# For "stages" type, original_level is one of {"wake", "deep", "light", "rem"}
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"level",
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# For "classic" type, unified_level is one of {0, 1} where 0: awake {"awake" + "restless"}, 1: asleep {"asleep"}
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# For "stages" type, unified_level is one of {0, 1} where 0: awake {"wake"}, 1: asleep {"deep" + "light" + "rem"}
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"unified_level",
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# one of {0, 1} where 0: nap, 1: main sleep
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"is_main_sleep",
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# one of {"classic", "stages"}
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"type",
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"local_date_time",
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"timestamp")
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def mergeLongAndShortData(data_summary):
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longData = pd.DataFrame(columns=['dateTime', 'level', 'seconds'])
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shortData = pd.DataFrame(columns=['dateTime','level', 'seconds'])
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windowLength = 30
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for data in data_summary['data']:
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origEntry = data
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counter = 0
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numberOfSplits = origEntry['seconds']//windowLength
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for times in range(numberOfSplits):
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newRow = {'dateTime':dateutil.parser.parse(origEntry['dateTime'])+timedelta(seconds=counter*windowLength),'level':origEntry['level'],'seconds':windowLength}
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longData = longData.append(newRow, ignore_index = True)
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counter = counter + 1
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for data in data_summary['shortData']:
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origEntry = data
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counter = 0
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numberOfSplits = origEntry['seconds']//windowLength
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for times in range(numberOfSplits):
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newRow = {'dateTime':dateutil.parser.parse(origEntry['dateTime'])+timedelta(seconds=counter*windowLength),'level':origEntry['level'],'seconds':windowLength}
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shortData = shortData.append(newRow,ignore_index = True)
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counter = counter + 1
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longData.set_index('dateTime',inplace=True)
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shortData.set_index('dateTime',inplace=True)
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longData['level'] = np.where(longData.index.isin(shortData.index) == True,'wake',longData['level'])
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longData.reset_index(inplace=True)
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return longData.values.tolist()
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def classicData1min(data_summary):
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dataList = list()
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for data in data_summary['data']:
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origEntry = data
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counter = 0
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timeDuration = 60
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numberOfSplits = origEntry['seconds']//timeDuration
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for times in range(numberOfSplits):
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newRow = {'dateTime':dateutil.parser.parse(origEntry['dateTime'])+timedelta(seconds=counter*timeDuration),'level':origEntry['level'],'seconds':timeDuration}
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dataList.append(newRow)
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counter = counter + 1
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return dataList
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# Parse one record for sleep API version 1
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def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type):
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sleep_record_type = "classic"
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d_start_datetime = datetime.strptime(record["startTime"][:18], "%Y-%m-%dT%H:%M:%S")
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d_end_datetime = datetime.strptime(record["endTime"][:18], "%Y-%m-%dT%H:%M:%S")
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# Summary data
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if fitbit_data_type == "summary":
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row_summary = (device_id, record["efficiency"],
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record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"],
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d_is_main_sleep, sleep_record_type,
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d_start_datetime, d_end_datetime,
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0,
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record["awakeCount"], record["awakeDuration"], record["awakeningsCount"],
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record["restlessCount"], record["restlessDuration"])
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records_summary.append(row_summary)
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# Intraday data
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if fitbit_data_type == "intraday":
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start_date = d_start_datetime.date()
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end_date = d_end_datetime.date()
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is_before_midnight = True
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curr_date = start_date
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for data in record["minuteData"]:
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# For overnight episodes, use end_date once we are over midnight
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d_time = datetime.strptime(data["dateTime"], '%H:%M:%S').time()
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if is_before_midnight and d_time.hour == 0:
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curr_date = end_date
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d_datetime = datetime.combine(curr_date, d_time)
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# API 1.2 stores original_level as strings, so we convert original_levels of API 1 to strings too
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# (1: "asleep", 2: "restless", 3: "awake")
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d_original_level = SLEEP_CODE2LEVEL[int(data["value"])-1]
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row_intraday = (device_id,
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d_original_level, -1, d_is_main_sleep, sleep_record_type,
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d_datetime, 0)
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records_intraday.append(row_intraday)
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return records_summary, records_intraday
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# Parse one record for sleep API version 1.2
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def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type):
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sleep_record_type = record['type']
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d_start_datetime = datetime.strptime(record["startTime"][:18], "%Y-%m-%dT%H:%M:%S")
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d_end_datetime = datetime.strptime(record["endTime"][:18], "%Y-%m-%dT%H:%M:%S")
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# Summary data
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if fitbit_data_type == "summary":
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row_summary = (device_id, record["efficiency"],
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record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"],
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d_is_main_sleep, sleep_record_type,
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d_start_datetime, d_end_datetime,
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0)
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records_summary.append(row_summary)
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# Intraday data
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if fitbit_data_type == "intraday":
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if sleep_record_type == 'classic':
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start_date = d_start_datetime.date()
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end_date = d_end_datetime.date()
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is_before_midnight = True
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curr_date = start_date
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data_summary = record['levels']
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dataSplitted = classicData1min(data_summary) ##Calling the function to split the data in regular 60 seconds interval
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for data in dataSplitted:
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# For overnight episodes, use end_date once we are over midnight
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d_time = data["dateTime"].time()
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if is_before_midnight and d_time.hour == 0:
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curr_date = end_date
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d_datetime = datetime.combine(curr_date, d_time)
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d_original_level = data["level"]
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row_intraday = (device_id,
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d_original_level, -1, d_is_main_sleep, sleep_record_type,
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d_datetime, 0)
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records_intraday.append(row_intraday)
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else:
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# For sleep type "stages"
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start_date = d_start_datetime.date()
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end_date = d_end_datetime.date()
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is_before_midnight = True
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curr_date = start_date
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data_summary = record['levels']
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dataList = mergeLongAndShortData(data_summary)
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for data in dataList:
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d_time = data[0].time()
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if is_before_midnight and d_time.hour == 0:
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curr_date = end_date
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d_datetime = datetime.combine(curr_date, d_time)
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d_original_level = data[1]
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row_intraday = (device_id,
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d_original_level, -1, d_is_main_sleep, sleep_record_type,
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d_datetime, 0)
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records_intraday.append(row_intraday)
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return records_summary, records_intraday
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def parseSleepData(sleep_data, fitbit_data_type):
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SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2
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if sleep_data.empty:
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return pd.DataFrame(columns=SLEEP_SUMMARY_COLUMNS), pd.DataFrame(columns=SLEEP_INTRADAY_COLUMNS)
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device_id = sleep_data["device_id"].iloc[0]
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records_summary, records_intraday = [], []
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# Parse JSON into individual records
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for multi_record in sleep_data.fitbit_data:
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for record in json.loads(multi_record)["sleep"]:
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# Whether the sleep episode is nap (0) or main sleep (1)
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d_is_main_sleep = 1 if record["isMainSleep"] else 0
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# For sleep API version 1
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if "awakeCount" in record:
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SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1
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records_summary, records_intraday = parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type)
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# For sleep API version 1.2
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else:
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SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2
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records_summary, records_intraday = parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type)
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if fitbit_data_type == "summary":
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parsed_data = pd.DataFrame(data=records_summary, columns=SLEEP_SUMMARY_COLUMNS)
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elif fitbit_data_type == "intraday":
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parsed_data = pd.DataFrame(data=records_intraday, columns=SLEEP_INTRADAY_COLUMNS)
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else:
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raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].")
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return parsed_data
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timezone = snakemake.params["timezone"]
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column_format = snakemake.params["column_format"]
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fitbit_data_type = snakemake.params["fitbit_data_type"]
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sleep_episode_timestamp = snakemake.params["sleep_episode_timestamp"]
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with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f:
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participant_file = yaml.safe_load(f)
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local_start_date = pd.Timestamp(participant_file["FITBIT"]["START_DATE"])
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local_end_date = pd.Timestamp(participant_file["FITBIT"]["END_DATE"]) + pd.DateOffset(1)
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if column_format == "JSON":
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json_raw = pd.read_csv(snakemake.input["raw_data"])
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parsed_data = parseSleepData(json_raw, fitbit_data_type)
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elif column_format == "PLAIN_TEXT":
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if fitbit_data_type == "summary":
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parsed_data = pd.read_csv(snakemake.input["raw_data"], parse_dates=["local_start_date_time", "local_end_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
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elif fitbit_data_type == "intraday":
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parsed_data = pd.read_csv(snakemake.input["raw_data"], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
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else:
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raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].")
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else:
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raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].")
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if parsed_data.shape[0] > 0 and fitbit_data_type == "summary":
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if sleep_episode_timestamp != "start" and sleep_episode_timestamp != "end":
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raise ValueError("SLEEP_EPISODE_TIMESTAMP can only be one of ['start', 'end'].")
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# Column name to be considered as the event datetime
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datetime_column = "local_" + sleep_episode_timestamp + "_date_time"
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# Only keep dates in the range of [local_start_date, local_end_date)
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parsed_data = parsed_data.loc[(parsed_data[datetime_column] >= local_start_date) & (parsed_data[datetime_column] < local_end_date)]
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# Convert datetime to timestamp
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parsed_data["timestamp"] = parsed_data[datetime_column].dt.tz_localize(timezone).astype(np.int64) // 10**6
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# Drop useless columns: local_start_date_time and local_end_date_time
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parsed_data.drop(["local_start_date_time", "local_end_date_time"], axis = 1, inplace=True)
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if parsed_data.shape[0] > 0 and fitbit_data_type == "intraday":
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# Only keep dates in the range of [local_start_date, local_end_date)
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parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)]
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# Convert datetime to timestamp
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parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6
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# Unifying level
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parsed_data["unified_level"] = np.where(parsed_data["level"].isin(["awake", "wake", "restless"]), 0, 1)
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parsed_data.to_csv(snakemake.output[0], index=False)
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