def test_ppg_simulate():
ppg1 = nk.ppg_simulate(duration=20, sampling_rate=500, heart_rate=70,
frequency_modulation=.3, ibi_randomness=.25,
drift=1, motion_amplitude=.5,
powerline_amplitude=.1, burst_amplitude=1,
burst_number=5, random_state=42, show=False)
assert ppg1.size == 20 * 500
ppg2 = nk.ppg_simulate(duration=200, sampling_rate=1000, heart_rate=70,
frequency_modulation=.3, ibi_randomness=.25,
drift=1, motion_amplitude=.5,
powerline_amplitude=.1, burst_amplitude=1,
burst_number=5, random_state=42, show=False)
assert ppg2.size == 200 * 1000
# Ensure that frequency_modulation does not affect other signal properties.
ppg3 = nk.ppg_simulate(duration=200, sampling_rate=1000, heart_rate=70,
frequency_modulation=1, ibi_randomness=.25,
drift=1, motion_amplitude=.5,
powerline_amplitude=.1, burst_amplitude=1,
burst_number=5, random_state=42, show=False)
assert np.allclose((ppg2.mean() - ppg3.mean()), 0, atol=1e-2)
assert np.allclose((ppg2.std() - ppg3.std()), 0, atol=1e-2)
# Ensure that ibi_randomness does not affect other signal properties.
ppg4 = nk.ppg_simulate(duration=200, sampling_rate=1000, heart_rate=70,
frequency_modulation=1, ibi_randomness=1,
drift=1, motion_amplitude=.5,
powerline_amplitude=.1, burst_amplitude=1,
burst_number=5, random_state=42, show=False)
assert np.allclose((ppg3.mean() - ppg4.mean()), 0, atol=1e-1)
assert np.allclose((ppg3.std() - ppg4.std()), 0, atol=1e-1)
def test_ppg_clean():
sampling_rate = 500
ppg = nk.ppg_simulate(
duration=30,
sampling_rate=sampling_rate,
heart_rate=180,
frequency_modulation=0.01,
ibi_randomness=0.1,
drift=1,
motion_amplitude=0.5,
powerline_amplitude=0.1,
burst_amplitude=1,
burst_number=5,
random_state=42,
show=False,
)
ppg_cleaned_elgendi = nk.ppg_clean(ppg,
sampling_rate=sampling_rate,
method="elgendi")
assert ppg.size == ppg_cleaned_elgendi.size
# Assert that bandpass filter with .5 Hz lowcut and 8 Hz highcut was applied.
fft_raw = np.abs(np.fft.rfft(ppg))
fft_elgendi = np.abs(np.fft.rfft(ppg_cleaned_elgendi))
freqs = np.fft.rfftfreq(ppg.size, 1 / sampling_rate)
assert np.sum(fft_raw[freqs < 0.5]) > np.sum(fft_elgendi[freqs < 0.5])
assert np.sum(fft_raw[freqs > 8]) > np.sum(fft_elgendi[freqs > 8])
def test_ppg_simulate(duration, sampling_rate, heart_rate, freq_modulation):
ppg = nk.ppg_simulate(
duration=duration,
sampling_rate=sampling_rate,
heart_rate=heart_rate,
frequency_modulation=freq_modulation,
ibi_randomness=0,
drift=0,
motion_amplitude=0,
powerline_amplitude=0,
burst_amplitude=0,
burst_number=0,
random_state=42,
show=False,
)
assert ppg.size == duration * sampling_rate
signals, _ = nk.ppg_process(ppg, sampling_rate=sampling_rate)
assert np.allclose(signals["PPG_Rate"].mean(), heart_rate, atol=1)
# Ensure that the heart rate fluctuates in the requested range.
groundtruth_range = freq_modulation * heart_rate
observed_range = np.percentile(signals['PPG_Rate'], 90) - np.percentile(
signals['PPG_Rate'], 10)
assert np.allclose(groundtruth_range,
observed_range,
atol=groundtruth_range * .15)
def test_ppg_findpeaks():
sampling_rate = 500
ppg = nk.ppg_simulate(
duration=30,
sampling_rate=sampling_rate,
heart_rate=60,
frequency_modulation=0.01,
ibi_randomness=0.1,
drift=1,
motion_amplitude=0.5,
powerline_amplitude=0.1,
burst_amplitude=1,
burst_number=5,
random_state=42,
show=True,
)
ppg_cleaned_elgendi = nk.ppg_clean(ppg,
sampling_rate=sampling_rate,
method="elgendi")
info_elgendi = nk.ppg_findpeaks(ppg_cleaned_elgendi,
sampling_rate=sampling_rate,
show=True)
peaks = info_elgendi["PPG_Peaks"]
assert peaks.size == 29
assert peaks.sum() == 219763
def test_ppg_simulate_ibi(ibi_randomness, std_heart_rate):
ppg = nk.ppg_simulate(
duration=20,
sampling_rate=50,
heart_rate=70,
frequency_modulation=0,
ibi_randomness=ibi_randomness,
drift=0,
motion_amplitude=0,
powerline_amplitude=0,
burst_amplitude=0,
burst_number=0,
random_state=42,
show=False,
)
assert ppg.size == 20 * 50
signals, _ = nk.ppg_process(ppg, sampling_rate=50)
assert np.allclose(signals["PPG_Rate"].mean(), 70, atol=1.5)
# Ensure that standard deviation of heart rate
assert np.allclose(signals["PPG_Rate"].std(), std_heart_rate, atol=1)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import neurokit2 as nk
# =============================================================================
# Simulate physiological signals
# =============================================================================
# Generate synthetic signals
ecg = nk.ecg_simulate(duration=10, heart_rate=70)
ppg = nk.ppg_simulate(duration=10, heart_rate=70)
rsp = nk.rsp_simulate(duration=10, respiratory_rate=15)
eda = nk.eda_simulate(duration=10, scr_number=3)
emg = nk.emg_simulate(duration=10, burst_number=2)
# Visualise biosignals
data = pd.DataFrame({
"ECG": ecg,
"PPG": ppg,
"RSP": rsp,
"EDA": eda,
"EMG": emg
})
nk.signal_plot(data, subplots=True)
# Save it
data = pd.DataFrame({
"ECG":
nk.ecg_simulate(duration=10, heart_rate=70, noise=0),
"PPG":