from datetime import datetime, date, time, timedelta
from pampro import Time_Series, Channel, channel_inference, triaxial_calibration
# Change the filenames as appropriate
# Load sample activPAL data
x, y, z = Channel.load_channels(
"/pa/data/STVS/_data/activpal_data/714952C-AP1335893 18Nov13 10-00am for 7d 23h 14m.datx",
"activPAL")
# Autocalibrate the raw acceleration data
x, y, z, (cal_params), (results), (misc) = triaxial_calibration.calibrate(
x, y, z)
# Infer some sample level info from the three channels - VM, ENMO, Pitch & Roll
vm = channel_inference.infer_vector_magnitude(x, y, z)
enmo = channel_inference.infer_enmo(vm)
pitch, roll = channel_inference.infer_pitch_roll(x, y, z)
# Create a time series object and add all signals to it
ts = Time_Series.Time_Series("activPAL")
ts.add_channels([x, y, z, vm, enmo, pitch, roll])
# Request some stats about the time series
# In this case: mean ENMO, pitch and roll, and 10 degree cutpoints of pitch and roll
angle_levels = [[-90, -80], [-80, -70], [-70, -60], [-60, -50], [-50, -40],
[-40, -30], [-30, -20], [-20, -10], [-10, 0], [0, 10],
[10, 20], [20, 30], [30, 40], [40, 50], [50, 60], [60, 70],
[70, 80], [80, 90]]
stat_dict = {
"Pitch": angle_levels + ["mean"],
from datetime import datetime, date, time, timedelta
from pampro import Time_Series, Channel, channel_inference, triaxial_calibration
# Change the filenames as appropriate
# Load sample activPAL data
x, y, z = Channel.load_channels("/pa/data/STVS/_data/activpal_data/714952C-AP1335893 18Nov13 10-00am for 7d 23h 14m.datx", "activPAL")
# Autocalibrate the raw acceleration data
x, y, z, (cal_params), (results), (misc) = triaxial_calibration.calibrate(x, y, z)
# Infer some sample level info from the three channels - VM, ENMO, Pitch & Roll
vm = channel_inference.infer_vector_magnitude(x, y, z)
enmo = channel_inference.infer_enmo(vm)
pitch, roll = channel_inference.infer_pitch_roll(x, y, z)
# Create a time series object and add all signals to it
ts = Time_Series.Time_Series("activPAL")
ts.add_channels([x,y,z,vm,enmo,pitch,roll])
# Request some stats about the time series
# In this case: mean ENMO, pitch and roll, and 10 degree cutpoints of pitch and roll
angle_levels = [[-90,-80],[-80,-70],[-70,-60],[-60,-50],[-50,-40],[-40,-30],[-30,-20],[-20,-10],[-10,0],[0,10],[10,20],[20,30],[30,40],[40,50],[50,60],[60,70],[70,80],[80,90]]
stat_dict = {"Pitch":angle_levels+["mean"], "Roll":angle_levels+["mean"], "ENMO":["mean"]}
# Get the output at 15 minute level
quarter_hourly_results = ts.piecewise_statistics(timedelta(minutes=15), stat_dict)
# Create a time series object to put the results in
from datetime import datetime, date, time, timedelta
from pampro import Time_Series, Channel, channel_inference, Bout
# 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)
import random
import copy
from pampro import Time_Series, Channel, channel_inference, Bout
execution_start = datetime.now()
ts = Time_Series.Time_Series("Actiheart")
# Load sample Actiheart data
filename = os.path.join(os.path.dirname(__file__), '..', 'data\ARBOTW.txt')
chans = Channel.load_channels(filename, "Actiheart")
#ts.add_channels(chans)
activity = chans[0]
ecg = chans[1]
# Calculate moving averages of the channels
ecg_ma = ecg.moving_average(15)
activity_ma = activity.moving_average(15)
ts.add_channel(ecg_ma)
ts.add_channel(activity_ma)
blah = activity.time_derivative()
blah = blah.moving_average(121)
#ts.add_channel(blah)
from datetime import datetime, date, time, timedelta
from pampro import Time_Series, Channel, channel_inference, Bout
# 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
from matplotlib.dates import DayLocator, HourLocator, DateFormatter, drange
from datetime import datetime, date, time, timedelta
from scipy import stats
import random
import copy
from pampro import Time_Series, Channel, channel_inference, Bout
execution_start = datetime.now()
ts = Time_Series.Time_Series("Actiheart")
# Load sample Actiheart data
filename = os.path.join(os.path.dirname(__file__), '..', 'data\ARBOTW.txt')
chans = Channel.load_channels(filename, "Actiheart")
#ts.add_channels(chans)
activity = chans[0]
ecg = chans[1]
# Calculate moving averages of the channels
ecg_ma = ecg.moving_average(15)
activity_ma = activity.moving_average(15)
ts.add_channel(ecg_ma)
ts.add_channel(activity_ma)
blah = activity.time_derivative()
blah = blah.moving_average(121)
#ts.add_channel(blah)
# Infer sleep from Actiheart channels