def load_experiment(data_path, diary_path, start_hour):
# Configure an Experiment
exp = Experiment()
# Iterates over a set of files in a directory.
# Unfortunately, we have to do it manually with RawProcessing because we are modifying the annotations
print("Running %s" % data_path)
for file in glob(data_path):
print("FILE", file)
pp = RawProcessing(file,
cols_for_activity=["stdMET_highIC_Branch"],
is_act_count=False,
col_for_datetime="REALTIME",
strftime="%d-%b-%Y %H:%M:%S",
col_for_pid="id",
col_for_hr="mean_hr",
device_location="dw")
pp.data["hyp_act_x"] = pp.data[
"hyp_act_x"] - 1.0 # adjust for the BBVA dataset
w = Wearable(pp) # Creates a wearable from a pp object
exp.add_wearable(w)
# Set frequency for every wearable in the collection
exp.set_freq_in_secs(60)
# Changing the hour the experiment starts from midnight (0) to 3pm (15)
exp.change_start_hour_for_experiment_day(start_hour)
diary = Diary().from_file(diary_path)
exp.add_diary(diary)
return exp
def setUp(self):
# Loads some file
pp1 = RawProcessing()
pp1.load_file("../data/examples_mesa/mesa-sample-day5-invalid5hours.csv",
# activitiy information
cols_for_activity=["activity"],
is_act_count=True,
# Datatime information
col_for_datatime="linetime",
device_location="dw",
start_of_week="dayofweek",
# Participant information
col_for_pid="mesaid")
self.w_5day_invalid5hours = Wearable(pp1)
def load_experiment(data_path, diary_path, start_hour):
# Configure an Experiment
exp = Experiment()
# Iterates over a set of files in a directory.
# Unfortunately, we have to do it manually with RawProcessing because we are modifying the annotations
for file in glob(data_path):
pp = RawProcessing(
file,
# HR information
col_for_hr="HR",
# Activity information
cols_for_activity=["Axis1", "Axis2", "Axis3"],
is_act_count=False,
# Datetime information
col_for_datetime="time",
strftime="%Y-%b-%d %H:%M:%S",
# Participant information
col_for_pid="pid")
w = Wearable(pp) # Creates a wearable from a pp object
exp.add_wearable(w)
# Set frequency for every wearable in the collection
# exp.set_freq_in_secs(5)
# Changing the hour the experiment starts from midnight (0) to 3pm (15)
exp.change_start_hour_for_experiment_day(start_hour)
diary = Diary().from_file(diary_path)
exp.add_diary(diary)
return exp
def load_experiment(data_path, start_hour):
# Configure an Experiment
exp = Experiment()
# Iterates over a set of files in a directory.
# Unfortunately, we have to do it manually with RawProcessing because we are modifying the annotations
for file in glob(data_path):
pp = RawProcessing(file,
# HR information
col_for_hr="hr",
# Activity information
cols_for_activity=["x", "y", "z"],
is_act_count=True,
# Datetime information
col_for_datetime="faketime",
strftime="%Y-%m-%d %H:%M:%S",
# Participant information
col_for_pid="pid")
w = Wearable(pp) # Creates a wearable from a pp object
w.data["hyp_annotation"] = (w.data["label"] > 0)
exp.add_wearable(w)
# Set frequency for every wearable in the collection
exp.set_freq_in_secs(15)
# Changing the hour the experiment starts from midnight (0) to 3pm (15)
exp.change_start_hour_for_experiment_day(start_hour)
return exp
def setup_experiment(file_path, diary_path, start_hour):
# Configure an Experiment
exp = Experiment()
# Iterates over a set of files in a directory.
# Unfortunately, we have to do it manually with RawProcessing because we are modifying the annotations
for file in glob(file_path):
pp = RawProcessing(file,
device_location="dw",
# HR information
col_for_hr="mean_hr",
# Activity information
cols_for_activity=["activity"],
is_act_count=True,
# Datetime information
col_for_datetime="linetime",
strftime="%Y-%m-%d %H:%M:%S",
# Participant information
col_for_pid="mesaid")
w = Wearable(pp) # Creates a wearable from a pp object
# Invert the two_stages flag. Now True means sleeping and False means awake
w.data["hyp_annotation"] = (w.data["stages"] > 0)
exp.add_wearable(w)
exp.set_freq_in_secs(30)
w.change_start_hour_for_experiment_day(start_hour)
diary = Diary().from_file(diary_path)
exp.add_diary(diary)
return exp
def configure_experiment(self, datapath: str, device_location: str, cols_for_activity, col_for_mets: str = None,
is_emno: bool = False, is_act_count: bool = False, col_for_datetime: str = "time",
start_of_week: int = -1, strftime: str = None, col_for_pid: str = None, pid: int = -1,
additional_data: object = None, col_for_hr: str = None):
# TODO: Missing a check to see if datapath exists.
if os.path.isdir(datapath):
if not datapath.endswith("*"):
datapath = os.path.join(datapath, "*")
else:
if '*' not in datapath:
datapath = datapath + "*"
for file in glob(datapath):
pp = RawProcessing(file,
device_location=device_location,
# activitiy information
cols_for_activity=cols_for_activity,
is_act_count=is_act_count,
is_emno=is_emno,
# Datatime information
col_for_datetime=col_for_datetime,
col_for_mets=col_for_mets,
start_of_week=start_of_week,
strftime=strftime,
# Participant information
col_for_pid=col_for_pid,
pid=pid,
additional_data=additional_data,
# HR information
col_for_hr=col_for_hr, )
w = Wearable(pp)
self.wearables[w.get_pid()] = w
def test_load_file(self):
pp1 = RawProcessing()
pp1.load_file("../data/examples_mesa/mesa-sample.csv",
collection_name="mesa",
col_for_datatime="linetime",
device_location="dw",
start_of_week="dayofweek",
col_for_pid="mesaid")
nrows = pp1.data.shape[0]
self.assertEqual(nrows, 23003)
self.assertIn("linetime", pp1.data.keys())
pp2 = RawProcessing()
pp2.load_file("../data/examples_mesa/mesa-sample.csv",
collection_name="mesa",
col_for_datatime="linetime",
device_location="dw",
start_of_week=4,
pid=1)
nrows = pp2.data.shape[0]
self.assertEqual(nrows, 23003)
self.assertIn("hyp_time", pp2.data.keys())
def configure_experiment(self,
datapath: str,
device_location: str,
cols_for_activity,
col_for_mets: str = None,
is_emno: bool = False,
is_act_count: bool = False,
col_for_datetime: str = "time",
start_of_week: int = -1,
strftime: str = None,
col_for_pid: str = None,
pid: int = -1,
additional_data: object = None,
col_for_hr: str = None):
"""
Parameters
----------
datapath : str
DESCRIPTION. Path to the dat folder
device_location : str, optional
DESCRIPTION. The default is None. Where this device was located (options are: "bw", "hip", "dw", "ndw", "chest", "hp_ch", "hp_bw", "all")
# Configuration for activity
cols_for_activity : list / str
DESCRIPTION. Which columns record activity
col_for_mets : object, optional
DESCRIPTION. Column that records METs.
is_emno : bool, optional
DESCRIPTION. True if the cols_for_activity are already computed as the ENMO (Euclidean Norm Minus One)
is_act_count : bool, optional
DESCRIPTION. The default is False. True us cols_for_activity are already computed as counts
# Datetime parameters
col_for_datetime : str, optional
DESCRIPTION. The default is "time". Name of timestamp column.
start_of_week : int, optional
DESCRIPTION. The default is -1. Integer that represents the day at the start of the week
strftime : str, optional
DESCRIPTION. The default is None. Format to parse col_for_datetime
# PID parameters
col_for_pid : str, optional
DESCRIPTION. The default is None. Participant ID columns
pid : int, optional
DESCRIPTION. The default is -1.
# HR parameters
col_for_hr : str, optional
DESCRIPTION. The default is None. Column with heart rate data
# Any additional data?
additional_data : object, optional
DESCRIPTION. The default is None.
Returns
-------
None.
"""
# TODO: Missing a check to see if datapath exists.
if os.path.isdir(datapath):
if not datapath.endswith("*"):
datapath = os.path.join(datapath, "*")
else:
if '*' not in datapath:
datapath = datapath + "*"
for file in glob(datapath):
pp = RawProcessing(
file,
device_location=device_location,
# activitiy information
cols_for_activity=cols_for_activity,
is_act_count=is_act_count,
is_emno=is_emno,
# Datatime information
col_for_datetime=col_for_datetime,
col_for_mets=col_for_mets,
start_of_week=start_of_week,
strftime=strftime,
# Participant information
col_for_pid=col_for_pid,
pid=pid,
additional_data=additional_data,
# HR information
col_for_hr=col_for_hr,
)
w = Wearable(pp)
self.wearables[w.get_pid()] = w
# Shiftworker analysis
from hypnospy import Wearable
from hypnospy.data import RawProcessing
from hypnospy.analysis import Viewer, NonWearingDetector, SleepBoudaryDetector, SleepWakeAnalysis
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pp = RawProcessing(
"./data/shiftworker/Shiftworker.csv",
# HR information
col_for_hr="mean_hr",
# Activity information
cols_for_activity=["ACC"],
is_act_count=True,
# Datetime information
col_for_datetime="real_time",
strftime="%d/%m/%Y %H:%M",
# Participant information
col_for_pid="id")
w = Wearable(pp)
#Check out data columns
#print(w.data.head(10))
#Define parameters fo HR-based sleep algorithm
hr_quantile = 0.4
hr_min_window_length = 60
hr_merge_blocks = 180