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 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 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 = MESAPreProcessing(file)
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["interval_sleep"] = w.data["interval"].isin(["REST-S", "REST"])
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 get_valid_data_for_experiment(self,
experiment: Experiment) -> pd.DataFrame:
pids = [
wearable.get_pid() for wearable in experiment.get_all_wearables()
]
return self.data[self.data.index.isin(pids)]
def add_to_experiment(self, experiment: Experiment) -> None:
for wearable in experiment.get_all_wearables():
self.add_to_wearable(wearable)
df_acc.append(df_res)
df_acc = pd.concat(df_acc)
df_acc["exp_id"] = exp_id
df_acc["quantile"] = quantile
df_acc["window_lengths"] = min_window_length
df_acc["time_merge_blocks"] = merge_blocks
df_acc.to_csv(os.path.join(output_path, "exp_%d.csv" % (exp_id)), index=False)
if __name__ == "__main__":
# Configure an Experiment
exp = Experiment()
data_path = "./data/collection_apple_watch/*.csv"
start_hour = 15
end_hour = start_hour - 1
quantiles = [0.025, 0.05, 0.075, 0.10, 0.125, 0.15, 0.175, 0.20, 0.225, 0.25, 0.275,
0.30, 0.325, 0.35, 0.375, 0.4, 0.425, 0.45, 0.475, 0.5, 0.525, 0.55, 0.575, 0.6, 0.625, 0.65, 0.675,
0.7, 0.725, 0.75, 0.775, 0.8, 0.825, 0.85, 0.875, 0.9, 0.925, 0.95, 0.975, 1.0]
window_lengths = [25, 27.5, 30, 32.5, 35, 37.5, 40, 42.5, 45, 47.5, 50]
time_merge_blocks = [30, 60, 120, 240, 360, 420]
hparam_list = []
exp_id = 0
for quantile in quantiles:
for merge_blocks in time_merge_blocks:
from glob import glob
from hypnospy import Wearable
from hypnospy.data import ActiwatchSleepData
from hypnospy.analysis import SleepWakeAnalysis
from hypnospy.analysis import TimeSeriesProcessing
from hypnospy.analysis import PhysicalActivity
from hypnospy import Experiment
if __name__ == "__main__":
# Configure an Experiment
exp = Experiment()
file_path = "./data/small_collection_hchs/*"
# 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 = ActiwatchSleepData(file,
col_for_datetime="time",
col_for_pid="pid")
w = Wearable(pp) # Creates a wearable from a pp object
exp.add_wearable(w)
tsp = TimeSeriesProcessing(exp)
tsp.fill_no_activity(-0.0001)
tsp.detect_non_wear(strategy="choi")
tsp.check_consecutive_days(5)
print("Valid days:", tsp.get_valid_days())