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, 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_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