def setup_func():
global ts
global header
global counts
ts, header = data_loading.load(
os.path.abspath(__file__).replace(os.path.basename(__file__), "") +
"_data/testfile21.dat",
"Actigraph",
datetime_format="%d/%m/%Y")
counts = ts.get_channel("AG_Counts")
def setup_func():
global ts
global header
global counts
ts, header = data_loading.load(
os.path.abspath(__file__).replace(os.path.basename(__file__), "") + "_data/testfile19.dat",
"Actigraph",
datetime_format="%d/%m/%Y",
)
counts = ts.get_channel("AG_Counts")
def test_nonwear_positions():
# Case 1: Nonwear at very beginning of file
ts1, header1 = data_loading.load(os.path.abspath(__file__).replace(os.path.basename(__file__), "") + "_data/testfile23.dat", "Actigraph", datetime_format="%d/%m/%Y")
counts1 = ts1.get_channel("AG_Counts")
nonwear_bouts1, wear_bouts1 = channel_inference.infer_nonwear_actigraph(counts1)
# Case 2: Nonwear in middle of file
ts2, header2 = data_loading.load(os.path.abspath(__file__).replace(os.path.basename(__file__), "") + "_data/testfile24.dat", "Actigraph", datetime_format="%d/%m/%Y")
counts2 = ts2.get_channel("AG_Counts")
nonwear_bouts2, wear_bouts2 = channel_inference.infer_nonwear_actigraph(counts2)
# Case 3: Nonwear at very end of file
ts3, header3 = data_loading.load(os.path.abspath(__file__).replace(os.path.basename(__file__), "") + "_data/testfile25.dat", "Actigraph", datetime_format="%d/%m/%Y")
counts3 = ts3.get_channel("AG_Counts")
nonwear_bouts3, wear_bouts3 = channel_inference.infer_nonwear_actigraph(counts3)
# They should all have the same duration of wear & nonwear
assert(Bout.total_time(nonwear_bouts1) == timedelta(hours=2))
assert(Bout.total_time(nonwear_bouts1) == Bout.total_time(nonwear_bouts2))
assert(Bout.total_time(nonwear_bouts1) == Bout.total_time(nonwear_bouts3))
assert(Bout.total_time(wear_bouts1) == Bout.total_time(wear_bouts2))
assert(Bout.total_time(wear_bouts1) == Bout.total_time(wear_bouts3))
# Delete the relevant nonwear bouts from each channel
counts1.delete_windows(nonwear_bouts1)
counts2.delete_windows(nonwear_bouts2)
counts3.delete_windows(nonwear_bouts3)
# Total data should be equal
assert(sum(counts1.data) == sum(counts2.data))
assert(sum(counts1.data) == sum(counts3.data))
# Summary level mean should also be the same
s1 = counts1.summary_statistics()[0]
s2 = counts2.summary_statistics()[0]
s3 = counts3.summary_statistics()[0]
assert(s1.data[0] == s2.data[0])
assert(s1.data[0] == s3.data[0])
def test_nonwear_positions():
# Case 1: Nonwear at very beginning of file
ts1, header1 = data_loading.load(os.path.abspath(__file__).replace(os.path.basename(__file__), "") + "_data/testfile23.dat", "Actigraph", datetime_format="%d/%m/%Y")
counts1 = ts1.get_channel("AG_Counts")
nonwear_bouts1, wear_bouts1 = channel_inference.infer_nonwear_actigraph(counts1)
# Case 2: Nonwear in middle of file
ts2, header2 = data_loading.load(os.path.abspath(__file__).replace(os.path.basename(__file__), "") + "_data/testfile24.dat", "Actigraph", datetime_format="%d/%m/%Y")
counts2 = ts2.get_channel("AG_Counts")
nonwear_bouts2, wear_bouts2 = channel_inference.infer_nonwear_actigraph(counts2)
# Case 3: Nonwear at very end of file
ts3, header3 = data_loading.load(os.path.abspath(__file__).replace(os.path.basename(__file__), "") + "_data/testfile25.dat", "Actigraph", datetime_format="%d/%m/%Y")
counts3 = ts3.get_channel("AG_Counts")
nonwear_bouts3, wear_bouts3 = channel_inference.infer_nonwear_actigraph(counts3)
# They should all have the same duration of wear & nonwear
assert(Bout.total_time(nonwear_bouts1) == timedelta(hours=2))
assert(Bout.total_time(nonwear_bouts1) == Bout.total_time(nonwear_bouts2))
assert(Bout.total_time(nonwear_bouts1) == Bout.total_time(nonwear_bouts3))
assert(Bout.total_time(wear_bouts1) == Bout.total_time(wear_bouts2))
assert(Bout.total_time(wear_bouts1) == Bout.total_time(wear_bouts3))
# Delete the relevant nonwear bouts from each channel
counts1.delete_windows(nonwear_bouts1)
counts2.delete_windows(nonwear_bouts2)
counts3.delete_windows(nonwear_bouts3)
# Total data should be equal
assert(sum(counts1.data) == sum(counts2.data))
assert(sum(counts1.data) == sum(counts3.data))
# Summary level mean should also be the same
s1 = counts1.summary_statistics()[0]
s2 = counts2.summary_statistics()[0]
s3 = counts3.summary_statistics()[0]
assert(s1.data[0] == s2.data[0])
assert(s1.data[0] == s3.data[0])
from datetime import datetime, date, time, timedelta
from pampro import data_loading, Time_Series, Channel, channel_inference, triaxial_calibration
# Change filenames as appropriate
# Read the data - yields 1 channel per axis
ts, header = data_loading.load("/pa/data/BIOBANK/example.cwa", "Axivity")
x, y, z = ts.get_channels(["X", "Y", "Z"])
# Autocalibrate the raw acceleration data
x, y, z, calibration_diagnostics = triaxial_calibration.calibrate(x, y, z)
# Infer some sample level information - Vector Magnitude (VM), Euclidean Norm Minus One (ENMO)
vm = channel_inference.infer_vector_magnitude(x, y, z)
enmo = channel_inference.infer_enmo(vm)
# Create a time series object and add channels to it
ts.add_channels([vm, enmo])
# Uncomment this line to write the raw data as CSV
#ts.write_channels_to_file("C:/Data/3.csv")
# Request some interesting statistics - mean of ENMO
stats = {"ENMO":[("generic", ["mean"])]}
# Get the above statistics on an hourly level - returned as channels
hourly_results = ts.piecewise_statistics(timedelta(hours=1), statistics=stats)
# Write the hourly analysis to a file
def process_file(job_details):
id_num = str(job_details["pid"])
filename = job_details["filename"]
filename_short = os.path.basename(filename).split('.')[0]
meta = os.path.join(results_folder,
"metadata_{}.csv".format(filename_short))
# check if analysis_meta already exists...
if os.path.isfile(meta):
os.remove(meta)
battery_max = 0
if monitor_type == "GeneActiv":
battery_max = GA_battery_max
elif monitor_type == "Axivity":
battery_max = AX_battery_max
epochs = [timedelta(minutes=n) for n in epoch_minutes]
# Use 'epochs_minutes' variable to create the corresponding names to the epochs defined
names = []
plots_list = []
for n in epoch_minutes:
name = ""
if n % 60 == 0: # If the epoch is a multiple of 60, it will be named in hours, e.g. '1h'
name = "{}h".format(int(n / 60))
elif n % 60 != 0: # If the epoch is NOT a multiple of 60, it will be named in seconds, e.g. '15m'
name = "{}m".format(n)
names.append(name)
if n in epoch_plot:
plots_list.append(name)
# fast-load the data to identify any anomalies:
qc_ts, qc_header = data_loading.fast_load(filename, monitor_type)
qc_channels = qc_ts.get_channels(["X", "Y", "Z"])
anomalies = diagnostics.diagnose_fix_anomalies(qc_channels,
discrepancy_threshold=2)
# Load the data
ts, header = data_loading.load(filename, monitor_type, compress=False)
header["processed_file"] = os.path.basename(filename)
# some monitors have manufacturers parameters applied to them, let's preserve these but rename them:
var_list = [
"x_gain", "x_offset", "y_gain", "y_offset", "z_gain", "z_offset",
"calibration_date"
]
for var in var_list:
if var in header.keys():
header[("manufacturers_%s" % var)] = header[var]
header.pop(var)
x, y, z, battery, temperature, integrity = ts.get_channels(
["X", "Y", "Z", "Battery", "Temperature", "Integrity"])
initial_channels = [x, y, z, battery, temperature, integrity]
# create dictionary of anomalies total and types
anomalies_dict = {"QC_anomalies_total": len(anomalies)}
# check whether any anomalies have been found:
if len(anomalies) > 0:
anomalies_file = os.path.join(
results_folder, "{}_anomalies.csv".format(filename_short))
df = pd.DataFrame(anomalies)
for type in anomaly_types:
anomalies_dict["QC_anomaly_{}".format(type)] = (
df.anomaly_type.values == type).sum()
df = df.set_index("anomaly_type")
# print record of anomalies to anomalies_file
df.to_csv(anomalies_file)
# if anomalies have been found, fix these anomalies
channels = diagnostics.fix_anomalies(anomalies, initial_channels)
else:
for type in anomaly_types:
anomalies_dict["QC_anomaly_{}".format(type)] = 0
# if no anomalies
channels = initial_channels
first_channel = channels[0]
# Convert timestamps to offsets from the first timestamp
start, offsets = Channel.timestamps_to_offsets(first_channel.timestamps)
# As timestamps are sparse, expand them to 1 per observation
offsets = Channel.interpolate_offsets(offsets, len(first_channel.data))
# For each channel, convert to offset timestamps
for c in channels:
c.start = start
c.set_contents(c.data, offsets, timestamp_policy="offset")
# find approximate first and last battery percentage values
first_battery_pct = round((battery.data[1] / battery_max) * 100, 2)
last_battery_pct = round((battery.data[-1] / battery_max) * 100, 2)
# Calculate the time frame to use
start = time_utilities.start_of_day(x.timeframe[0])
end = time_utilities.end_of_day(x.timeframe[-1])
tp = (start, end)
# if the sampling frequency is greater than 40Hz
if x.frequency > 40:
# apply a low pass filter
x = pampro_fourier.low_pass_filter(x,
20,
frequency=x.frequency,
order=4)
x.name = "X" # because LPF^ changes the name, we want to override that
y = pampro_fourier.low_pass_filter(y,
20,
frequency=y.frequency,
order=4)
y.name = "Y"
z = pampro_fourier.low_pass_filter(z,
20,
frequency=z.frequency,
order=4)
z.name = "Z"
# find any bouts where data is "missing" BEFORE calibration
missing_bouts = []
if -111 in x.data:
# extract the bouts of the data channels where the data == -111 (the missing value)
missing = x.bouts(-111, -111)
# add a buffer of 2 minutes (120 seconds) to the beginning and end of each bout
for item in missing:
bout_start = max(item.start_timestamp - timedelta(seconds=120),
x.timeframe[0])
bout_end = min(item.end_timestamp + timedelta(seconds=120),
x.timeframe[1])
new_bout = Bout.Bout(start_timestamp=bout_start,
end_timestamp=bout_end)
missing_bouts.append(new_bout)
else:
pass
x.delete_windows(missing_bouts)
y.delete_windows(missing_bouts)
z.delete_windows(missing_bouts)
integrity.fill_windows(missing_bouts, fill_value=1)
################ CALIBRATION #######################
# extract still bouts
calibration_ts, calibration_header = triaxial_calibration.calibrate_stepone(
x, y, z, noise_cutoff_mg=noise_cutoff_mg)
# Calibrate the acceleration to local gravity
cal_diagnostics = triaxial_calibration.calibrate_steptwo(
calibration_ts, calibration_header, calibration_statistics=False)
# calibrate data
triaxial_calibration.do_calibration(x,
y,
z,
temperature=None,
cp=cal_diagnostics)
x.delete_windows(missing_bouts)
y.delete_windows(missing_bouts)
z.delete_windows(missing_bouts)
temperature.delete_windows(missing_bouts)
battery.delete_windows(missing_bouts)
# Derive some signal features
vm = channel_inference.infer_vector_magnitude(x, y, z)
vm.delete_windows(missing_bouts)
if "HPFVM" in stats:
vm_hpf = channel_inference.infer_vm_hpf(vm)
else:
vm_hpf = None
if "ENMO" in stats:
enmo = channel_inference.infer_enmo(vm)
else:
enmo = None
if "PITCH" and "ROLL" in stats:
pitch, roll = channel_inference.infer_pitch_roll(x, y, z)
else:
pitch = roll = None
# Infer nonwear and mask those data points in the signal
nonwear_bouts = channel_inference.infer_nonwear_triaxial(
x, y, z, noise_cutoff_mg=noise_cutoff_mg)
for bout in nonwear_bouts:
# Show non-wear bouts in purple
bout.draw_properties = {'lw': 0, 'alpha': 0.75, 'facecolor': '#764af9'}
for channel, channel_name in zip(
[enmo, vm_hpf, pitch, roll, temperature, battery],
["ENMO", "HPFVM", "PITCH", "ROLL", "Temperature", "Battery"]):
if channel_name in stats:
# Collapse the sample data to a processing epoch (in seconds) so data is summarised
epoch_level_channel = channel.piecewise_statistics(
timedelta(seconds=processing_epoch), time_period=tp)[0]
epoch_level_channel.name = channel_name
if channel_name in ["Temperature", "Battery"]:
pass
else:
epoch_level_channel.delete_windows(nonwear_bouts)
epoch_level_channel.delete_windows(missing_bouts)
ts.add_channel(epoch_level_channel)
# collapse binary integrity channel
epoch_level_channel = integrity.piecewise_statistics(
timedelta(seconds=int(processing_epoch)),
statistics=[("binary", ["flag"])],
time_period=tp)[0]
epoch_level_channel.name = "Integrity"
epoch_level_channel.fill_windows(missing_bouts, fill_value=1)
ts.add_channel(epoch_level_channel)
# create and open results files
results_files = [
os.path.join(results_folder, "{}_{}.csv".format(name, filename_short))
for name in names
]
files = [open(file, "w") for file in results_files]
# Write the column headers to the created files
for f in files:
f.write(pampro_utilities.design_file_header(stats) + "\n")
# writing out and plotting results
for epoch, name, f in zip(epochs, names, files):
results_ts = ts.piecewise_statistics(epoch,
statistics=stats,
time_period=tp,
name=id_num)
results_ts.write_channels_to_file(file_target=f)
f.flush()
if name in plots_list:
# for each statistic in the plotting dictionary, produce a plot in the results folder
for stat, plot in plotting_dict.items():
try:
results_ts[stat].add_annotations(nonwear_bouts)
results_ts.draw([[stat]],
file_target=os.path.join(
results_folder,
plot.format(filename_short, name)))
except KeyError:
pass
header["processing_script"] = version
header["analysis_resolutions"] = names
header["noise_cutoff_mg"] = noise_cutoff_mg
header["processing_epoch"] = processing_epoch
header["QC_first_battery_pct"] = first_battery_pct
header["QC_last_battery_pct"] = last_battery_pct
metadata = {**header, **anomalies_dict, **cal_diagnostics}
# write metadata to file
pampro_utilities.dict_write(meta, id_num, metadata)
for c in ts:
del c.data
del c.timestamps
del c.indices
del c.cached_indices
from datetime import datetime, date, time, timedelta
from pampro import data_loading, Time_Series, Channel, channel_inference, triaxial_calibration
# Change filenames as appropriate
# Read the data - yields 1 channel per axis
ts, header = data_loading.load("/pa/data/BIOBANK/example.cwa", "Axivity")
x, y, z = ts.get_channels(["X", "Y", "Z"])
# Autocalibrate the raw acceleration data
x, y, z, calibration_diagnostics = triaxial_calibration.calibrate(x, y, z)
# Infer some sample level information - Vector Magnitude (VM), Euclidean Norm Minus One (ENMO)
vm = channel_inference.infer_vector_magnitude(x, y, z)
enmo = channel_inference.infer_enmo(vm)
# Create a time series object and add channels to it
ts.add_channels([vm, enmo])
# Uncomment this line to write the raw data as CSV
#ts.write_channels_to_file("C:/Data/3.csv")
# Request some interesting statistics - mean of ENMO
stats = {"ENMO": [("generic", ["mean"])]}
# Get the above statistics on an hourly level - returned as channels
hourly_results = ts.piecewise_statistics(timedelta(hours=1), statistics=stats)
# Write the hourly analysis to a file
hourly_results.write_channels_to_file("/pa/data/BIOBANK/example_output.csv")
