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Add dates filter while parsing fitbit data

pull/103/head
Meng Li 2020-11-30 12:34:14 -05:00
parent 5178be585d
commit 70991d6667
4 changed files with 53 additions and 21 deletions
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9
rules/preprocessing.smk
View File

@ -176,7 +176,8 @@ rule phone_application_categories:
rule fitbit_parse_heartrate:
input:
"data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv"
participant_file = "data/external/participant_files/{pid}.yaml",
raw_data = "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv"
params:
timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
table = lambda wildcards: config["FITBIT_HEARTRATE_"+str(wildcards.fitbit_data_type).upper()]["TABLE"],
@ -189,7 +190,8 @@ rule fitbit_parse_heartrate:
rule fitbit_parse_steps:
input:
"data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv"
participant_file = "data/external/participant_files/{pid}.yaml",
raw_data = "data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv"
params:
timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
table = lambda wildcards: config["FITBIT_STEPS_"+str(wildcards.fitbit_data_type).upper()]["TABLE"],
@ -202,7 +204,8 @@ rule fitbit_parse_steps:
rule fitbit_parse_sleep:
input:
"data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv"
participant_file = "data/external/participant_files/{pid}.yaml",
raw_data = "data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv"
params:
timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
table = lambda wildcards: config["FITBIT_SLEEP_"+str(wildcards.fitbit_data_type).upper()]["TABLE"],

19
src/data/fitbit_parse_heartrate.py
View File

@ -1,4 +1,4 @@
import json, sys
import yaml, json, sys
import pandas as pd
import numpy as np
from datetime import datetime, timezone
@ -93,7 +93,6 @@ def parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date,
records_intraday.append(row_intraday)
return records_intraday
# def append_timestamp(data):
def parseHeartrateData(heartrate_data, fitbit_data_type):
@ -132,13 +131,23 @@ timezone = snakemake.params["timezone"]
column_format = snakemake.params["column_format"]
fitbit_data_type = snakemake.params["fitbit_data_type"]
with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f:
participant_file = yaml.safe_load(f)
local_start_date = pd.Timestamp(participant_file["FITBIT"]["START_DATE"])
local_end_date = pd.Timestamp(participant_file["FITBIT"]["END_DATE"]) + pd.DateOffset(1)
if column_format == "JSON":
json_raw = pd.read_csv(snakemake.input[0])
json_raw = pd.read_csv(snakemake.input["raw_data"])
parsed_data = parseHeartrateData(json_raw, fitbit_data_type)
elif column_format == "PLAIN_TEXT":
parsed_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
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))
else:
raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].")
# Only keep dates in the range of [local_start_date, local_end_date)
parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)]
if parsed_data.shape[0] > 0:
parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6
parsed_data.to_csv(snakemake.output[0], index=False)
parsed_data.to_csv(snakemake.output[0], index=False)

32
src/data/fitbit_parse_sleep.py
View File

@ -1,4 +1,4 @@
import json
import json, yaml
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
@ -222,32 +222,44 @@ column_format = snakemake.params["column_format"]
fitbit_data_type = snakemake.params["fitbit_data_type"]
sleep_episode_timestamp = snakemake.params["sleep_episode_timestamp"]
with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f:
participant_file = yaml.safe_load(f)
local_start_date = pd.Timestamp(participant_file["FITBIT"]["START_DATE"])
local_end_date = pd.Timestamp(participant_file["FITBIT"]["END_DATE"]) + pd.DateOffset(1)
if column_format == "JSON":
json_raw = pd.read_csv(snakemake.input[0])
json_raw = pd.read_csv(snakemake.input["raw_data"])
parsed_data = parseSleepData(json_raw, fitbit_data_type)
elif column_format == "PLAIN_TEXT":
if fitbit_data_type == "summary":
parsed_data = pd.read_csv(snakemake.input[0], parse_dates=["local_start_date_time", "local_end_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
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))
elif fitbit_data_type == "intraday":
parsed_data = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
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))
else:
raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].")
else:
raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].")
if parsed_data.shape[0] > 0 and fitbit_data_type == "summary":
if sleep_episode_timestamp == "start":
parsed_data["timestamp"] = parsed_data["local_start_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6
elif sleep_episode_timestamp == "end":
parsed_data["timestamp"] = parsed_data["local_end_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6
else:
if sleep_episode_timestamp != "start" and sleep_episode_timestamp != "end":
raise ValueError("SLEEP_EPISODE_TIMESTAMP can only be one of ['start', 'end'].")
# Column name to be considered as the event datetime
datetime_column = "local_" + sleep_episode_timestamp + "_date_time"
# Only keep dates in the range of [local_start_date, local_end_date)
parsed_data = parsed_data.loc[(parsed_data[datetime_column] >= local_start_date) & (parsed_data[datetime_column] < local_end_date)]
# Convert datetime to timestamp
parsed_data["timestamp"] = parsed_data[datetime_column].dt.tz_localize(timezone).astype(np.int64) // 10**6
# Drop useless columns: local_start_date_time and local_end_date_time
parsed_data.drop(["local_start_date_time", "local_end_date_time"], axis = 1, inplace=True)
if parsed_data.shape[0] > 0 and fitbit_data_type == "intraday":
# Only keep dates in the range of [local_start_date, local_end_date)
parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)]
# Convert datetime to timestamp
parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6
# Unifying level
parsed_data["unified_level"] = np.where(parsed_data["level"].isin(["awake", "wake", "restless"]), 0, 1)
parsed_data.to_csv(snakemake.output[0], index=False)
parsed_data.to_csv(snakemake.output[0], index=False)

14
src/data/fitbit_parse_steps.py
View File

@ -1,4 +1,4 @@
import json
import json, yaml
import pandas as pd
import numpy as np
from datetime import datetime, timezone
@ -57,14 +57,22 @@ timezone = snakemake.params["timezone"]
column_format = snakemake.params["column_format"]
fitbit_data_type = snakemake.params["fitbit_data_type"]
with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f:
participant_file = yaml.safe_load(f)
local_start_date = pd.Timestamp(participant_file["FITBIT"]["START_DATE"])
local_end_date = pd.Timestamp(participant_file["FITBIT"]["END_DATE"]) + pd.DateOffset(1)
if column_format == "JSON":
json_raw = pd.read_csv(snakemake.input[0])
json_raw = pd.read_csv(snakemake.input["raw_data"])
parsed_data = parseStepsData(json_raw, fitbit_data_type)
elif column_format == "PLAIN_TEXT":
parsed_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
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))
else:
raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].")
# Only keep dates in the range of [local_start_date, local_end_date)
parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)]
if parsed_data.shape[0] > 0:
parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6