def test_invalid_days():
# File contains 24 hours of 1s, then 15 hours of 0s, then 24 hours of 1s
nonwear_bouts, wear_bouts = channel_inference.infer_nonwear_actigraph(counts)
# Get invalid/valid days where valid criterion is default (10 hours)
invalid_windows, valid_windows = channel_inference.infer_valid_days(counts, wear_bouts)
# One of the days should be invalid
assert(len(invalid_windows) == 1)
# The invalid bout should be exactly 1 day long
assert(invalid_windows[0].length == timedelta(days=1))
# Two valid days
assert(len(valid_windows) == 2)
# Now get invalid/valid windows where need only 9 hours
invalid_windows, valid_windows = channel_inference.infer_valid_days(counts, wear_bouts, valid_criterion=timedelta(hours=9))
# None of the days should be invalid
assert(len(invalid_windows) == 0)
# Three valid days
assert(len(valid_windows) == 3)
def test_invalid_days():
# File contains 24 hours of 1s, then 15 hours of 0s, then 24 hours of 1s
nonwear_bouts, wear_bouts = channel_inference.infer_nonwear_actigraph(counts)
# Get invalid/valid days where valid criterion is default (10 hours)
invalid_windows, valid_windows = channel_inference.infer_valid_days(counts, wear_bouts)
# One of the days should be invalid
assert(len(invalid_windows) == 1)
# The invalid bout should be exactly 1 day long
assert(invalid_windows[0].length == timedelta(days=1))
# Two valid days
assert(len(valid_windows) == 2)
# Now get invalid/valid windows where need only 9 hours
invalid_windows, valid_windows = channel_inference.infer_valid_days(counts, wear_bouts, valid_criterion=timedelta(hours=9))
# None of the days should be invalid
assert(len(invalid_windows) == 0)
# Three valid days
assert(len(valid_windows) == 3)
def test_nonwear_amount():
# File contains 24 hours of 1s, then 15 hours of 0s, then 9 hours of 1s, then 24 hours of 1s
nonwear_bouts, wear_bouts = channel_inference.infer_nonwear_actigraph(counts)
# There is 1 nonwear bout and 2 wear bouts surrounding it
assert(len(nonwear_bouts) == 1)
assert(len(wear_bouts) == 2)
Bout.cache_lengths(nonwear_bouts)
Bout.cache_lengths(wear_bouts)
nw_bout = nonwear_bouts[0]
# The nonwear bout is 15 hours long
assert(nw_bout.length == timedelta(hours=15))
# Summarise the data before deleting the nonwear
summary_before = Time_Series.Time_Series("")
summary_before.add_channels(counts.summary_statistics(statistics=[("generic", ["sum", "n", "missing"]),("cutpoints", [[0,0],[0,1],[1,1]])]))
# Number of 1s = 24 hours then 9 hours then 24 hours
assert(summary_before.get_channel("AG_Counts_sum").data[0] == (24+9+24)*60)
# 15 hours of 0s
assert(summary_before.get_channel("AG_Counts_0_0").data[0] == 15*60)
# Sum should = number of 1s
assert(summary_before.get_channel("AG_Counts_1_1").data[0] == (24+9+24)*60)
# n should be 3 days = 1440*3 = 24*3*60
assert(summary_before.get_channel("AG_Counts_n").data[0] == 24*3*60)
# Missing should be 0
assert(summary_before.get_channel("AG_Counts_missing").data[0] == 0)
counts.delete_windows(nonwear_bouts)
# Summarise the data after deleting the nonwear
summary_after = Time_Series.Time_Series("")
summary_after.add_channels(counts.summary_statistics(statistics=[("generic", ["sum", "n", "missing"]),("cutpoints", [[0,0],[0,1],[1,1]])]))
# Sum shouldn't have changed
assert(summary_after.get_channel("AG_Counts_sum").data[0] == (24+9+24)*60)
# All the 0s were nonwear, so there should now be no 0s
assert(summary_after.get_channel("AG_Counts_0_0").data[0] == 0)
# And the number of 1s shouldn't have changed
assert(summary_after.get_channel("AG_Counts_1_1").data[0] == (24+9+24)*60)
# n should have reduced by 15 hours = 15*60
assert(summary_after.get_channel("AG_Counts_n").data[0] == (24+9+24)*60)
# missing should have gone up by 15 hours = 15*60
assert(summary_after.get_channel("AG_Counts_missing").data[0] == 15*60)
def test_nonwear_amount():
# File contains 24 hours of 1s, then 15 hours of 0s, then 9 hours of 1s, then 24 hours of 1s
nonwear_bouts, wear_bouts = channel_inference.infer_nonwear_actigraph(counts)
# There is 1 nonwear bout and 2 wear bouts surrounding it
assert(len(nonwear_bouts) == 1)
assert(len(wear_bouts) == 2)
Bout.cache_lengths(nonwear_bouts)
Bout.cache_lengths(wear_bouts)
nw_bout = nonwear_bouts[0]
# The nonwear bout is 15 hours long
assert(nw_bout.length == timedelta(hours=15))
# Summarise the data before deleting the nonwear
summary_before = Time_Series.Time_Series("")
summary_before.add_channels(counts.summary_statistics(statistics=[("generic", ["sum", "n", "missing"]),("cutpoints", [[0,0],[0,1],[1,1]])]))
# Number of 1s = 24 hours then 9 hours then 24 hours
assert(summary_before.get_channel("AG_Counts_sum").data[0] == (24+9+24)*60)
# 15 hours of 0s
assert(summary_before.get_channel("AG_Counts_0_0").data[0] == 15*60)
# Sum should = number of 1s
assert(summary_before.get_channel("AG_Counts_1_1").data[0] == (24+9+24)*60)
# n should be 3 days = 1440*3 = 24*3*60
assert(summary_before.get_channel("AG_Counts_n").data[0] == 24*3*60)
# Missing should be 0
assert(summary_before.get_channel("AG_Counts_missing").data[0] == 0)
counts.delete_windows(nonwear_bouts)
# Summarise the data after deleting the nonwear
summary_after = Time_Series.Time_Series("")
summary_after.add_channels(counts.summary_statistics(statistics=[("generic", ["sum", "n", "missing"]),("cutpoints", [[0,0],[0,1],[1,1]])]))
# Sum shouldn't have changed
assert(summary_after.get_channel("AG_Counts_sum").data[0] == (24+9+24)*60)
# All the 0s were nonwear, so there should now be no 0s
assert(summary_after.get_channel("AG_Counts_0_0").data[0] == 0)
# And the number of 1s shouldn't have changed
assert(summary_after.get_channel("AG_Counts_1_1").data[0] == (24+9+24)*60)
# n should have reduced by 15 hours = 15*60
assert(summary_after.get_channel("AG_Counts_n").data[0] == (24+9+24)*60)
# missing should have gone up by 15 hours = 15*60
assert(summary_after.get_channel("AG_Counts_missing").data[0] == 15*60)
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])
# Change filenames as appropriate
# Request some interesting statistics - mean, min and max of the counts signal
# ...plus basic cutpoints for Sedentary, Light, and Moderate to Vigorous
stats = {"AG_Counts": [("generic", ["mean", "min", "max"]), ("cutpoints", [[0,99],[100,2999],[3000,99999]])]}
# Load Actigraph data
counts, header = Channel.load_channels("/pa/data/Tom/pampro/data/example_actigraph.DAT", "Actigraph", datetime_format="%m/%d/%Y")
ts = Time_Series.Time_Series("Actigraph")
ts.add_channel(counts)
# Get a list of bouts where the monitor was & wasn't worn
nonwear_bouts = channel_inference.infer_nonwear_actigraph(counts, zero_minutes=timedelta(minutes=90))
# Use that list to get a list of days of valid & invalid time
invalid_bouts = channel_inference.infer_valid_days(counts, wear_bouts)
# Since the cutpoints defined above only count positive data, negative values will be ignored
# Where the monitor wasn't worn, set the count value to -1
# Where the monitor wasn't valid, set the count value to -2
counts.fill_windows(nonwear_bouts, fill_value=-1)
counts.fill_windows(nonwear_bouts, fill_value=-2)
# Get the summary level results
summary_results = ts.summary_statistics(statistics=stats)
# Create a time series object, put the summary level results in it, write them to a file
ts_output = Time_Series.Time_Series("Output")
stats = {
"AG_Counts": [("generic", ["mean", "min", "max"]),
("cutpoints", [[0, 99], [100, 2999], [3000, 99999]])]
}
# Load Actigraph data
counts, header = Channel.load_channels(
"/pa/data/Tom/pampro/data/example_actigraph.DAT",
"Actigraph",
datetime_format="%m/%d/%Y")
ts = Time_Series.Time_Series("Actigraph")
ts.add_channel(counts)
# Get a list of bouts where the monitor was & wasn't worn
nonwear_bouts = channel_inference.infer_nonwear_actigraph(
counts, zero_minutes=timedelta(minutes=90))
# Use that list to get a list of days of valid & invalid time
invalid_bouts = channel_inference.infer_valid_days(counts, wear_bouts)
# Since the cutpoints defined above only count positive data, negative values will be ignored
# Where the monitor wasn't worn, set the count value to -1
# Where the monitor wasn't valid, set the count value to -2
counts.fill_windows(nonwear_bouts, fill_value=-1)
counts.fill_windows(nonwear_bouts, fill_value=-2)
# Get the summary level results
summary_results = ts.summary_statistics(statistics=stats)
# Create a time series object, put the summary level results in it, write them to a file
ts_output = Time_Series.Time_Series("Output")
