def window_statistics(self, start_dts, end_dts, statistics):
window = Bout.Bout(start_dts, end_dts)
bouts = self.bouts_involved(window)
output_row = []
if (len(bouts) > 0):
for stat in statistics:
if stat[0] == "generic":
for val1 in stat[1]:
if val1 == "sum":
intersection = Bout.bout_list_intersection([window],bouts)
Bout.cache_lengths(intersection)
sum_seconds = Bout.total_time(intersection).total_seconds()
output_row.append(sum_seconds)
elif val1 == "mean":
intersection = Bout.bout_list_intersection([window],bouts)
Bout.cache_lengths(intersection)
sum_seconds = Bout.total_time(intersection).total_seconds()
if sum_seconds >0 and len(bouts) > 0:
output_row.append( sum_seconds / len(bouts) )
else:
output_row.append(0)
elif val1 == "n":
output_row.append( len(bouts) )
else:
print("nooooooooo")
print(stat)
print(statistics)
output_row.append(-1)
elif stat[0] == "sdx":
# ("sdx", [10,20,30,40,50,60,70,80,90])
sdx_results = sdx(bouts, stat[1])
for r in sdx_results:
output_row.append(r)
else:
# No bouts in this Bout_Collection overlapping this window
# There was no data for the time period
# Output -1 for each missing variable
for i in range(self.expected_results(statistics)):
output_row.append(-1)
return output_row
def test_bouts():
# There are 8 bouts of 0s
zero_bouts = counts.bouts(0,0)
assert(len(zero_bouts) == 8)
# There are 8 bouts of 1s
one_bouts = counts.bouts(1,1)
assert(len(one_bouts) == 8)
# Since there are only 1s and 0s in the file, there should be 1 bout of 0 to 1
both_bouts = counts.bouts(0,1)
assert(len(both_bouts) == 1)
# The timestamps of that 1 bout should match the start and end of the channel timestamps
# But "end" of bout occurs 1 minute after end of channel
assert(both_bouts[0].start_timestamp == counts.timestamps[0])
assert(both_bouts[0].end_timestamp == counts.timestamps[-1]+timedelta(minutes=1))
# Changing the max value shouldn't change anything
bouts = counts.bouts(0,23)
assert(len(bouts) == 1)
# Same for the minimum value
bouts = counts.bouts(-340,23)
assert(len(bouts) == 1)
# Should be no bouts 2 or above
bouts = counts.bouts(2,23)
assert(len(bouts) == 0)
# Same for below 0
bouts = counts.bouts(-32323,-2)
assert(len(bouts) == 0)
# The data is in 1 minute epochs
total_zero_time = Bout.total_time(zero_bouts)
total_one_time = Bout.total_time(one_bouts)
total_both_time = Bout.total_time(both_bouts)
assert(total_zero_time == timedelta(minutes=10*30))
assert(total_one_time == timedelta(minutes=16*30))
assert(total_both_time == total_zero_time + total_one_time)
# Integer seconds spent at 0 should be 300 minutes * 60 = 18000 seconds
total_zero_time_seconds = total_zero_time.total_seconds()
assert(total_zero_time_seconds == 10*30*60)
# Inverting bouts within a period
# Since the file is 0s and 1s, the total time - the time spent @ 0 should = time spent @ 1
not_zero_bouts = Bout.time_period_minus_bouts((counts.timestamps[0],counts.timestamps[-1]+timedelta(minutes=1)), zero_bouts)
total_not_zero_time = Bout.total_time(not_zero_bouts)
assert(total_not_zero_time == total_one_time)
def test_bouts():
one_bouts = counts.bouts(1, 1)
# There are 7 bouts
assert len(one_bouts) == 7
# Their length is 1, 2, 3, .. 7
assert Bout.total_time(one_bouts) == timedelta(minutes=7 + 6 + 5 + 4 + 3 + 2 + 1)
# Keeping bouts >= i minutes means there should be 7-(i-1) left
for i in range(1, 7):
i_or_longer = Bout.limit_to_lengths(one_bouts, min_length=timedelta(minutes=i))
assert len(i_or_longer) == 7 - (i - 1)
# One manual check
three_or_longer = Bout.limit_to_lengths(one_bouts, min_length=timedelta(minutes=3))
assert len(three_or_longer) == 5
# This should exclude the 1 bout at exactly 3 minutes
three_plus_bit_or_longer = Bout.limit_to_lengths(one_bouts, min_length=timedelta(minutes=3, seconds=1))
assert len(three_plus_bit_or_longer) == 4
# No bouts should be this long
eight_or_longer = Bout.limit_to_lengths(one_bouts, min_length=timedelta(minutes=8))
assert len(eight_or_longer) == 0
# There is nothing above 1 in the file, should be 0 bouts
two_bouts = counts.bouts(2, 989)
assert len(two_bouts) == 0
def infer_valid_days(channel, wear_bouts, valid_criterion=timedelta(hours=10)):
#Generate day-long windows
start = time_utilities.start_of_day(channel.timestamps[0])
day_windows = []
while start < channel.timeframe[1]:
day_windows.append(Bout.Bout(start, start+timedelta(days=1)))
start += timedelta(days=1)
valid_windows = []
invalid_windows = []
for window in day_windows:
#how much does all of wear_bouts intersect with window?
intersections = Bout.bout_list_intersection([window], wear_bouts)
total = Bout.total_time(intersections)
# If the amount of overlap exceeds the valid criterion, it is valid
if total >= valid_criterion:
#window.draw_properties={"lw":0, "facecolor":[1,0,0], "alpha":0.25}
valid_windows.append(window)
else:
invalid_windows.append(window)
return(invalid_windows, valid_windows)
def sdx(bouts, percentages):
total_time_minutes = Bout.total_time(bouts).total_seconds()/60
Bout.cache_lengths(bouts)
bouts.sort(key=lambda x : x.length)
highest_length_minutes = int(bouts[-1].length.total_seconds()/60)
targets_minutes = [int((total_time_minutes)/100.0 * percentage) for percentage in percentages]
results = []
#print("Number of bouts: ", len(bouts))
#print("Total time mins: ", total_time_minutes)
#print("Highest length mins", highest_length_minutes)
#print(targets_minutes)
current_target_index = 0
target_minutes = targets_minutes[current_target_index]
for length in range(1, highest_length_minutes+1):
included_bouts = [b for b in bouts if b.length.total_seconds()/60 <= length]
#print(included_bouts)
total_included_time_minutes = Bout.total_time(included_bouts).total_seconds()/60
#print(length, total_included_time_minutes)
while total_included_time_minutes >= target_minutes:
#print(">target_minutes", target_minutes)
#length is the result
results.append(length)
current_target_index += 1
if current_target_index == len(targets_minutes):
target_minutes = 999999999
else:
target_minutes = targets_minutes[current_target_index]
if current_target_index == len(targets_minutes):
break
#print(results)
return results
def test_extracted_bouts():
one_bouts = counts.bouts(1,1)
zero_bouts = counts.bouts(0,0)
# Bouts where counts == 0 and counts == 1 should be mutually excluse
# So there should be no intersections between them
intersections = Bout.bout_list_intersection(one_bouts, zero_bouts)
assert(len(intersections) == 0)
# A bout that spans the whole time period should completely intersect with bouts where counts == 1
one_big_bout = Bout.Bout(counts.timestamps[0]-timedelta(days=1), counts.timestamps[-1]+timedelta(days=1))
one_intersections = Bout.bout_list_intersection(one_bouts, [one_big_bout])
assert(Bout.total_time(one_intersections) == Bout.total_time(one_bouts))
# Same for zeros
zero_intersections = Bout.bout_list_intersection(zero_bouts, [one_big_bout])
assert(Bout.total_time(zero_intersections) == Bout.total_time(zero_bouts))
# Filling in the bout gaps of one bouts should recreate the zero bouts
inverse_of_one_bouts = Bout.time_period_minus_bouts((counts.timeframe[0], counts.timeframe[1]+timedelta(minutes=1)), one_bouts)
# They should have the same n
assert(len(inverse_of_one_bouts) == len(zero_bouts))
# Same total amount of time
assert(Bout.total_time(inverse_of_one_bouts) == Bout.total_time(zero_bouts))
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])
def test_bouts():
one_bouts = counts.bouts(1, 1)
# There are 7 bouts
assert (len(one_bouts) == 7)
# Their length is 1, 2, 3, .. 7
assert (Bout.total_time(one_bouts) == timedelta(minutes=7 + 6 + 5 + 4 + 3 +
2 + 1))
# Keeping bouts >= i minutes means there should be 7-(i-1) left
for i in range(1, 7):
i_or_longer = Bout.limit_to_lengths(one_bouts,
min_length=timedelta(minutes=i))
assert (len(i_or_longer) == 7 - (i - 1))
# One manual check
three_or_longer = Bout.limit_to_lengths(one_bouts,
min_length=timedelta(minutes=3))
assert (len(three_or_longer) == 5)
# This should exclude the 1 bout at exactly 3 minutes
three_plus_bit_or_longer = Bout.limit_to_lengths(one_bouts,
min_length=timedelta(
minutes=3, seconds=1))
assert (len(three_plus_bit_or_longer) == 4)
# No bouts should be this long
eight_or_longer = Bout.limit_to_lengths(one_bouts,
min_length=timedelta(minutes=8))
assert (len(eight_or_longer) == 0)
# There is nothing above 1 in the file, should be 0 bouts
two_bouts = counts.bouts(2, 989)
assert (len(two_bouts) == 0)
def qc_analysis(job_details):
id_num = str(job_details["pid"])
filename = job_details["filename"]
filename_short = os.path.basename(filename).split('.')[0]
battery_max = 0
if filetype == "GeneActiv":
battery_max = GA_battery_max
elif filetype == "Axivity":
battery_max = AX_battery_max
# Load the data from the hdf5 file
ts, header = data_loading.fast_load(filename, filetype)
header["QC_filename"] = os.path.basename(filename)
x, y, z, battery, temperature = ts.get_channels(["X", "Y", "Z", "Battery", "Temperature"])
# create a channel of battery percentage, based on the assumed battery maximum value
battery_pct = Channel.Channel.clone(battery)
battery_pct.data = (battery.data / battery_max) * 100
channels = [x, y, z, battery, temperature, battery_pct]
anomalies = diagnostics.diagnose_fix_anomalies(channels, discrepancy_threshold=2)
# create dictionary of anomalies types
anomalies_dict = dict()
# check whether any anomalies have been found:
if len(anomalies) > 0:
anomalies_file = os.path.join(anomalies_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)
else:
for type in anomaly_types:
anomalies_dict["QC_anomaly_{}".format(type)] = 0
# check for axis anomalies
axes_dict = diagnostics.diagnose_axes(x, y, z, noise_cutoff_mg=13)
axis_anomaly = False
for key, val in axes_dict.items():
anomalies_dict["QC_{}".format(key)] = val
if key.endswith("max"):
if val > axis_max:
axis_anomaly = True
elif key.endswith("min"):
if val < axis_min:
axis_anomaly = True
# create a "check battery" flag:
check_battery = False
# calculate first and last battery percentages
first_battery_pct = round((battery_pct.data[1]),2)
last_battery_pct = round((battery_pct.data[-1]),2)
header["QC_first_battery_pct"] = first_battery_pct
header["QC_last_battery_pct"] = last_battery_pct
# calculate lowest battery percentage
# check if battery.pct has a missing_value, exclude those values if they exist
if battery_pct.missing_value == "None":
lowest_battery_pct = min(battery_pct.data)
else:
test_array = np.delete(battery_pct.data, np.where(battery_pct.data == battery_pct.missing_value))
lowest_battery_pct = min(test_array)
header["QC_lowest_battery_pct"] = round(lowest_battery_pct,2)
header["QC_lowest_battery_threshold"] = battery_minimum
# find the maximum battery discharge in any 24hr period:
max_discharge = battery_pct.channel_max_decrease(time_period=timedelta(hours=discharge_hours))
header["QC_max_discharge"] = round(max_discharge, 2)
header["QC_discharge_time_period"] = "{} hours".format(discharge_hours)
header["QC_discharge_threshold"] = discharge_pct
# change flag if lowest battery percentage dips below battery_minimum at any point
# OR maximum discharge greater than discharge_pct over time period "hours = discharge_hours"
if lowest_battery_pct < battery_minimum or max_discharge > discharge_pct:
check_battery = True
header["QC_check_battery"] = str(check_battery)
header["QC_axis_anomaly"] = str(axis_anomaly)
# 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)
results_ts = Time_Series.Time_Series("")
# Derive some signal features
vm = channel_inference.infer_vector_magnitude(x, y, z)
enmo = channel_inference.infer_enmo(vm)
enmo.minimum = 0
enmo.maximum = enmo_max
# Infer nonwear
nonwear_bouts = channel_inference.infer_nonwear_for_qc(x, y, z, noise_cutoff_mg=noise_cutoff_mg)
# Use nonwear bouts to calculate wear bouts
wear_bouts = Bout.time_period_minus_bouts(enmo.timeframe, nonwear_bouts)
# Use wear bouts to calculate the amount of wear time in the file in hours, save to meta data
total_wear = Bout.total_time(wear_bouts)
total_seconds_wear = total_wear.total_seconds()
total_hours_wear = round(total_seconds_wear/3600)
header["QC_total_hours_wear"] = total_hours_wear
# Split the enmo channel into lists of bouts for each quadrant:
''' quadrant_0 = 00:00 -> 06: 00
quadrant_1 = 06:00 -> 12: 00
quadrant_2 = 12:00 -> 18: 00
quadrant_3 = 18:00 -> 00: 00 '''
q_0, q_1, q_2, q_3 = channel_inference.create_quadrant_bouts(enmo)
# calculate the intersection of each set of bouts with wear_bouts, then calculate the wear time in each quadrant.
sum_quadrant_wear = 0
for quadrant, name1, name2 in ([q_0, "QC_hours_wear_quadrant_0", "QC_pct_wear_quadrant_0"],
[q_1, "QC_hours_wear_quadrant_1", "QC_pct_wear_quadrant_1"],
[q_2, "QC_hours_wear_quadrant_2", "QC_pct_wear_quadrant_2"],
[q_3, "QC_hours_wear_quadrant_3", "QC_pct_wear_quadrant_3"]):
quadrant_wear = Bout.bout_list_intersection(quadrant, wear_bouts)
seconds_wear = Bout.total_time(quadrant_wear).total_seconds()
hours_wear = round(seconds_wear / 3600)
header[name1] = hours_wear
header[name2] = round(((hours_wear / total_hours_wear) * 100), 2)
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, battery_pct],["ENMO", "Battery_percentage"]):
channel.name = channel_name
results_ts.add_channel(channel)
if PLOT == "YES":
# Plot statistics as subplots in one plot file per data file
results_ts["ENMO"].add_annotations(nonwear_bouts)
results_ts.draw_qc(plotting_df, file_target=os.path.join(charts_folder,"{}_plots.png".format(filename_short)))
header["QC_script"] = version
# file of metadata from qc process
qc_output = os.path.join(results_folder, "qc_meta_{}.csv".format(filename_short))
# check if qc_output already exists...
if os.path.isfile(qc_output):
os.remove(qc_output)
metadata = {**header, **anomalies_dict}
# write metadata to file
pampro_utilities.dict_write(qc_output, id_num, metadata)
for c in ts:
del c.data
del c.timestamps
del c.indices
del c.cached_indices
def calibrate(x,y,z, allow_overwrite=True, budget=1000, noise_cutoff_mg=13):
""" Use still bouts in the given triaxial data to calibrate it and return the calibrated channels """
calibration_diagnostics = OrderedDict()
vm = channel_inference.infer_vector_magnitude(x,y,z)
# Get a list of bouts where standard deviation in each axis is below given threshold ("still")
still_bouts = channel_inference.infer_still_bouts_triaxial(x,y,z, noise_cutoff_mg=noise_cutoff_mg, minimum_length=timedelta(minutes=1))
num_still_bouts = len(still_bouts)
num_still_seconds = Bout.total_time(still_bouts).total_seconds()
# Summarise VM in 10s intervals
vm_windows = vm.piecewise_statistics(timedelta(seconds=10), [("generic", ["mean"])], time_period=vm.timeframe)[0]
# Get a list where VM was between 0.5 and 1.5g ("reasonable")
reasonable_bouts = vm_windows.bouts(0.5, 1.5)
num_reasonable_bouts = len(reasonable_bouts)
num_reasonable_seconds = Bout.total_time(reasonable_bouts).total_seconds()
# We only want still bouts where the VM level was within 0.5g of 1g
# Therefore insersect "still" time with "reasonable" time
still_bouts = Bout.bout_list_intersection(reasonable_bouts, still_bouts)
# And we only want bouts where it was still and reasonable for 10s or longer
still_bouts = Bout.limit_to_lengths(still_bouts, min_length = timedelta(seconds=10))
num_final_bouts = len(still_bouts)
num_final_seconds = Bout.total_time(still_bouts).total_seconds()
# Get the average X,Y,Z for each still bout (inside which, by definition, XYZ should not change)
still_x, num_samples = x.build_statistics_channels(still_bouts, [("generic", ["mean", "n"])])
still_y = y.build_statistics_channels(still_bouts, [("generic", ["mean"])])[0]
still_z = z.build_statistics_channels(still_bouts, [("generic", ["mean"])])[0]
# Get the octant positions of the points to calibrate on
occupancy = octant_occupancy(still_x.data, still_y.data, still_z.data)
# Are they fairly distributed?
comparisons = {"x<0":[0,1,2,3], "x>0":[4,5,6,7], "y<0":[0,1,4,5], "y>0":[2,3,6,7], "z<0":[0,2,4,6], "z>0":[1,3,5,7]}
for axis in ["x", "y", "z"]:
mt = sum(occupancy[comparisons[axis + ">0"]])
lt = sum(occupancy[comparisons[axis + "<0"]])
calibration_diagnostics[axis + "_inequality"] = abs(mt-lt)/sum(occupancy)
# Calculate the initial error without doing any calibration
start_error = evaluate_solution(still_x, still_y, still_z, num_samples, [0,1,0,1,0,1])
# Do offset and scale calibration by default
offset_only_calibration = False
calibration_diagnostics["calibration_method"] = "offset and scale"
# If we have less than 500 points to calibrate with, or if more than 2 octants are empty
if len(still_x.data) < 500 or sum(occupancy == 0) > 2:
offset_only_calibration = True
calibration_diagnostics["calibration_method"] = "offset only"
# Search for the correct way to calibrate the data
calibration_parameters = find_calibration_parameters(still_x.data, still_y.data, still_z.data, offset_only=offset_only_calibration)
for param,value in zip("x_offset,x_scale,y_offset,y_scale,z_offset,z_scale".split(","), calibration_parameters):
calibration_diagnostics[param] = value
for i,occ in enumerate(occupancy):
calibration_diagnostics["octant_"+str(i)] = occ
# Calculate the final error after calibration
end_error = evaluate_solution(still_x, still_y, still_z, num_samples, calibration_parameters)
calibration_diagnostics["start_error"] = start_error
calibration_diagnostics["end_error"] = end_error
calibration_diagnostics["num_final_bouts"] = num_final_bouts
calibration_diagnostics["num_final_seconds"] = num_final_seconds
calibration_diagnostics["num_still_bouts"] = num_still_bouts
calibration_diagnostics["num_still_seconds"] = num_still_seconds
calibration_diagnostics["num_reasonable_bouts"] = num_reasonable_bouts
calibration_diagnostics["num_reasonable_seconds"] = num_reasonable_seconds
if allow_overwrite:
# If we do not need to preserve the original x,y,z values, we can just calibrate that data
# Apply the best calibration factors to the data
do_calibration(x, y, z, calibration_parameters)
return (x, y, z, calibration_diagnostics)
else:
# Else we create an independent copy of the raw data and calibrate that instead
cal_x = copy.deepcopy(x)
cal_y = copy.deepcopy(y)
cal_z = copy.deepcopy(z)
# Apply the best calibration factors to the data
do_calibration(cal_x, cal_y, cal_z, calibration_parameters)
return (cal_x, cal_y, cal_z, calibration_diagnostics)
