frequency = pd.Timedelta(sampling_period, unit='s')
start_time = '01/01/2018 00:00:00'
N = 7 * 1440 * 2 # *sampling_period
n_array = np.geomspace(5, 1000, num=40, endpoint=True, dtype=int)
q_array = [-5, -3, -1, 0, 1, 3, 5]
# Stochastic generators
bn = BrownianNoise(t=1)
fbm = FractionalBrownianMotion(hurst=0.9, t=1)
fgn = FractionalGaussianNoise(hurst=0.6, t=1)
np.random.seed(0)
# Brownian noise: h(q) = 1+H with H=0.5
bn_sample = generate_series(bn.sample(N - 1),
start=start_time,
sampling_period=sampling_period)
# Fractional Brownian motion: h(q) = 1+H with H = 0.9 (in this example)
fbm_sample = generate_series(fbm.sample(N - 1),
start=start_time,
sampling_period=sampling_period)
# Fractional Gaussian noise: h(q) = H with H = 0.6 (in this example)
fgn_sample = generate_series(fgn.sample(N),
start=start_time,
sampling_period=sampling_period)
# Associated fluctuations
test_bn = Fractal.dfa(bn_sample, n_array, deg=2, log=False)
test_bn_parallel = Fractal.dfa_parallel(bn_sample,
import pyActigraphy
import pytest
sampling_period = 60
frequency = pd.Timedelta(sampling_period, unit='s')
start_time = '01/01/2018 00:00:00'
N = 30240*sampling_period
period = pd.Timedelta(N, unit='s')
seq_prob = 0.7
seq_max_length = 50
sequences = generate_series(
generate_sequences(
N=N-1,
p=seq_prob,
max_rand_int=seq_max_length
),
start=start_time,
sampling_period=sampling_period
)
seq = pyActigraphy.io.BaseRaw(
name='raw_sequence',
uuid='XXXXXXXX',
format='CUSTOM',
axial_mode=None,
start_time=pd.to_datetime(start_time),
period=period,
frequency=frequency,
data=sequences,
light=None
from generate_dataset import generate_series
from generate_dataset import generate_squarewave
from generate_dataset import generate_sinewave
import pandas as pd
import pyActigraphy
from pytest import approx
sampling_period = 60
frequency = pd.Timedelta(sampling_period, unit='s')
start_time = '01/01/2018 08:00:00'
N = 20160 * sampling_period
period = pd.Timedelta(N, unit='s')
gaussian_noise = generate_series(generate_gaussian_noise(N=N - 1),
start=start_time,
sampling_period=sampling_period)
square_wave = generate_series(generate_squarewave(N=N - 1),
start=start_time,
sampling_period=sampling_period)
sine_wave = generate_series(generate_sinewave(N=N - 1),
start=start_time,
sampling_period=sampling_period)
raw_gaussian = pyActigraphy.io.BaseRaw(name='raw_gaussian',
uuid='XXXXXXXX',
format='CUSTOM',
axial_mode=None,
start_time=pd.to_datetime(start_time),
period=period,
frequency=frequency,
from generate_dataset import generate_series
from generate_dataset import generate_sinewave
import pandas as pd
import pyActigraphy
sampling_period = 60
frequency = pd.Timedelta(sampling_period, unit='s')
start_time = '01/01/2018 08:00:00'
N = 20800 * sampling_period
period = pd.Timedelta(N, unit='s')
sine_wave = generate_series(generate_sinewave(A=100, offset=True, N=N - 1),
start=start_time,
sampling_period=sampling_period)
raw_sinewave = pyActigraphy.io.BaseRaw(name='raw_sinewave',
uuid='XXXXXXXX',
format='CUSTOM',
axial_mode=None,
start_time=pd.to_datetime(start_time),
period=period,
frequency=frequency,
data=sine_wave,
light=None)
def test_smp_sinewave():
assert raw_sinewave.SleepMidPoint(
freq='5min', algo='Roenneberg') == pd.Timedelta('0 days 02:00:00')
from generate_dataset import generate_series
# from generate_dataset import generate_squarewave
from generate_dataset import generate_sinewave
import pandas as pd
import pyActigraphy
from pytest import approx
sampling_period = 60
frequency = pd.Timedelta(sampling_period, unit='s')
start_time = '01/01/2018 08:00:00'
N = 20160
period = pd.Timedelta(N * sampling_period, unit='s')
sine_wave_mask = generate_series(generate_sinewave(N=N, offset=True),
start=start_time,
sampling_period=sampling_period)
sine_wave_mask.loc['2018-01-03 06:00:00':'2018-01-03 14:00:00'] = 0
raw_sinewave_mask = pyActigraphy.io.BaseRaw(
name='raw_sinewave',
uuid='XXXXXXXX',
format='CUSTOM',
axial_mode=None,
start_time=pd.to_datetime(start_time),
period=period,
frequency=frequency,
data=sine_wave_mask,
light=None)
import numpy as np
import pandas as pd
import pyActigraphy
from pytest import approx
from generate_dataset import generate_series
from generate_dataset import generate_sinewave
sampling_period = 60
frequency = pd.Timedelta(sampling_period, unit='s')
start_time = '2018/01/01 08:00:00'
N = 20160
period = pd.Timedelta(N, unit='T')
sine_wave = generate_series(generate_sinewave(N=N, offset=True),
start=start_time,
sampling_period=sampling_period)
# Sleep characteristics:
# - start_time: 8h
# - sleep onset: 22h01
# - periode awake: 14h
# - sleep offset: 6h
# - periode asleep:7h59min
n_epochs_nan_trend = int(pd.Timedelta('12h') / frequency)
n_epochs_awake_prior_sleep = int(pd.Timedelta('2h01min') / frequency)
n_epochs_awake_after_sleep = int(pd.Timedelta('14h') / frequency)
n_epochs_asleep = int(pd.Timedelta('7h59min') / frequency)
n_days = int(period / pd.Timedelta('1D')) - 1
# Create associated sleep signal (awake:0, sleep:1)
sampling_period = 60
frequency = pd.Timedelta(sampling_period, unit='s')
start_time = '01/01/2000 08:00:00'
N = 10080
period = pd.Timedelta(N * sampling_period, unit='s')
# Set seed for reproducibility
np.random.seed(0)
sine_wave = generate_series(
generate_sinewave(
N=N, # number of samples
T=1440 * 60, # period in sec: 24*60*60
Ts=60, # sampling rate (sec.)
A=100, # oscillation amplitude
add_noise=True, # add gaussian noise
noise_power=100,
offset=True # offset oscillations between 0 and +2A
),
start=start_time,
sampling_period=sampling_period)
raw_sinewave = pyActigraphy.io.BaseRaw(name='raw_sinewave',
uuid='XXXXXXXX',
format='CUSTOM',
axial_mode=None,
start_time=pd.to_datetime(start_time),
period=period,
frequency=frequency,
data=sine_wave,
light=None)
