def test_eda_peaks():
sampling_rate = 1000
eda = nk.eda_simulate(duration=30, sampling_rate=sampling_rate,
scr_number=6, noise=0, drift=0.01, random_state=42)
eda_phasic = nk.eda_phasic(nk.standardize(eda), method='highpass')["EDA_Phasic"].values
signals, info = nk.eda_peaks(eda_phasic, method="gamboa2008")
onsets, peaks, amplitudes = biosppy.eda.basic_scr(eda_phasic, sampling_rate=1000)
assert np.allclose((info["SCR_Peaks"] - peaks).mean(), 0, atol=1e-5)
signals, info = nk.eda_peaks(eda_phasic, method="kim2004")
onsets, peaks, amplitudes = biosppy.eda.kbk_scr(eda_phasic, sampling_rate=1000)
assert np.allclose((info["SCR_Peaks"] - peaks).mean(), 0, atol=1)
def test_eda_eventrelated():
eda = nk.eda_simulate(duration=15, scr_number=3)
eda_signals, info = nk.eda_process(eda, sampling_rate=1000)
epochs = nk.epochs_create(eda_signals,
events=[5000, 10000, 15000],
sampling_rate=1000,
epochs_start=-0.1,
epochs_end=1.9)
eda_eventrelated = nk.eda_eventrelated(epochs)
no_activation = np.where(eda_eventrelated["EDA_SCR"] == 0)[0][0]
assert int(
pd.DataFrame(eda_eventrelated.values[no_activation]).isna().sum()) == 4
assert len(eda_eventrelated["Label"]) == 3
def test_eda_eventrelated():
eda = nk.eda_simulate(duration=15, scr_number=3)
eda_signals, info = nk.eda_process(eda, sampling_rate=1000)
epochs = nk.epochs_create(
eda_signals,
events=[5000, 10000, 15000],
sampling_rate=1000,
epochs_start=-0.1,
epochs_end=1.9,
)
eda_eventrelated = nk.eda_eventrelated(epochs)
no_activation = np.where(eda_eventrelated["EDA_SCR"] == 0)[0][0]
assert int(
pd.DataFrame(eda_eventrelated.values[no_activation]).isna().sum()) == 4
assert len(eda_eventrelated["Label"]) == 3
# Test warning on missing columns
with pytest.warns(nk.misc.NeuroKitWarning,
match=r".*does not have an `EDA_Phasic`.*"):
first_epoch_key = list(epochs.keys())[0]
first_epoch_copy = epochs[first_epoch_key].copy()
del first_epoch_copy["EDA_Phasic"]
nk.eda_eventrelated({**epochs, first_epoch_key: first_epoch_copy})
with pytest.warns(nk.misc.NeuroKitWarning,
match=r".*does not have an `SCR_Amplitude`.*"):
first_epoch_key = list(epochs.keys())[0]
first_epoch_copy = epochs[first_epoch_key].copy()
del first_epoch_copy["SCR_Amplitude"]
nk.eda_eventrelated({**epochs, first_epoch_key: first_epoch_copy})
with pytest.warns(nk.misc.NeuroKitWarning,
match=r".*does not have an `SCR_RecoveryTime`.*"):
first_epoch_key = list(epochs.keys())[0]
first_epoch_copy = epochs[first_epoch_key].copy()
del first_epoch_copy["SCR_RecoveryTime"]
nk.eda_eventrelated({**epochs, first_epoch_key: first_epoch_copy})
with pytest.warns(nk.misc.NeuroKitWarning,
match=r".*does not have an `SCR_RiseTime`.*"):
first_epoch_key = list(epochs.keys())[0]
first_epoch_copy = epochs[first_epoch_key].copy()
del first_epoch_copy["SCR_RiseTime"]
nk.eda_eventrelated({**epochs, first_epoch_key: first_epoch_copy})
def test_bio_process():
sampling_rate = 1000
# Create data
ecg = nk.ecg_simulate(duration=30, sampling_rate=sampling_rate)
rsp = nk.rsp_simulate(duration=30, sampling_rate=sampling_rate)
eda = nk.eda_simulate(duration=30,
sampling_rate=sampling_rate,
scr_number=3)
emg = nk.emg_simulate(duration=30,
sampling_rate=sampling_rate,
burst_number=3)
bio_df, bio_info = nk.bio_process(ecg=ecg,
rsp=rsp,
eda=eda,
emg=emg,
sampling_rate=sampling_rate)
# SCR components
scr = [val for key, val in bio_info.items() if "SCR" in key]
assert all(len(elem) == len(scr[0]) for elem in scr)
assert all(bio_info["SCR_Onsets"] < bio_info["SCR_Peaks"])
assert all(bio_info["SCR_Peaks"] < bio_info["SCR_Recovery"])
# RSP
assert all(bio_info["RSP_Peaks"] > bio_info["RSP_Troughs"])
assert len(bio_info["RSP_Peaks"]) == len(bio_info["RSP_Troughs"])
# EMG
assert all(bio_info["EMG_Offsets"] > bio_info["EMG_Onsets"])
assert len(bio_info["EMG_Offsets"] == len(bio_info["EMG_Onsets"]))
assert all(elem in [
'ECG_Raw', 'ECG_Clean', 'ECG_Rate', 'ECG_Quality', 'ECG_R_Peaks',
"ECG_P_Peaks", "ECG_Q_Peaks", "ECG_S_Peaks", "ECG_T_Peaks",
"ECG_P_Onsets", "ECG_T_Offsets", "ECG_Atrial_Phase",
"ECG_Ventricular_Phase", "ECG_Atrial_PhaseCompletion",
"ECG_Ventricular_PhaseCompletion", 'RSP_Raw', 'RSP_Clean',
'RSP_Amplitude', 'RSP_Rate', 'RSP_Phase', 'RSP_PhaseCompletion',
'RSP_Peaks', 'RSP_Troughs', 'EDA_Raw', 'EDA_Clean', 'EDA_Tonic',
'EDA_Phasic', 'SCR_Onsets', 'SCR_Peaks', 'SCR_Height', 'SCR_Amplitude',
'SCR_RiseTime', 'SCR_Recovery', 'SCR_RecoveryTime', 'EMG_Raw',
'EMG_Clean', 'EMG_Amplitude', 'EMG_Activity', 'EMG_Onsets',
'EMG_Offsets', 'RSA_P2T'
] for elem in np.array(bio_df.columns.values, dtype=str))
def test_eda_phasic():
sampling_rate = 1000
eda = nk.eda_simulate(duration=30, sampling_rate=sampling_rate,
scr_number=6, noise=0.01, drift=0.01, random_state=42)
cvxEDA = nk.eda_phasic(nk.standardize(eda), method='cvxeda')
assert len(cvxEDA) == len(eda)
smoothMedian = nk.eda_phasic(nk.standardize(eda), method='smoothmedian')
assert len(smoothMedian) == len(eda)
highpass = nk.eda_phasic(nk.standardize(eda), method='highpass')
assert len(highpass) == len(eda)
def test_eda_phasic():
sampling_rate = 1000
eda = nk.eda_simulate(duration=30,
sampling_rate=sampling_rate,
scr_number=6,
noise=0.01,
drift=0.01,
random_state=42)
if platform.system() == "Linux":
cvxEDA = nk.eda_phasic(nk.standardize(eda), method="cvxeda")
assert len(cvxEDA) == len(eda)
smoothMedian = nk.eda_phasic(nk.standardize(eda), method="smoothmedian")
assert len(smoothMedian) == len(eda)
highpass = nk.eda_phasic(nk.standardize(eda), method="highpass")
assert len(highpass) == len(eda)
def test_eda_process():
eda = nk.eda_simulate(duration=30,
scr_number=5,
drift=0.1,
noise=0,
sampling_rate=250)
signals, info = nk.eda_process(eda, sampling_rate=250)
assert signals.shape == (7500, 11)
assert np.array([
"EDA_Raw", "EDA_Clean", "EDA_Tonic", "EDA_Phasic", "SCR_Onsets",
"SCR_Peaks", "SCR_Height", "SCR_Amplitude", "SCR_RiseTime",
"SCR_Recovery", "SCR_RecoveryTime"
]) in signals.columns.values
# Check equal number of markers
peaks = np.where(signals["SCR_Peaks"] == 1)[0]
onsets = np.where(signals["SCR_Onsets"] == 1)[0]
recovery = np.where(signals["SCR_Recovery"] == 1)[0]
assert peaks.shape == onsets.shape == recovery.shape == (5, )
def test_eda_eventrelated():
eda = nk.eda_simulate(duration=15, scr_number=3)
eda_signals, info = nk.eda_process(eda, sampling_rate=1000)
epochs = nk.epochs_create(eda_signals,
events=[5000, 10000, 15000],
sampling_rate=1000,
epochs_start=-0.1,
epochs_end=1.9)
eda_eventrelated = nk.eda_eventrelated(epochs)
no_activation = np.where(eda_eventrelated["EDA_Activation"] == 0)[0][0]
assert int(
pd.DataFrame(eda_eventrelated.values[no_activation]).isna().sum()) == 4
assert len(eda_eventrelated["Label"]) == 3
assert len(eda_eventrelated.columns) == 6
assert all(elem in [
"EDA_Activation", "EDA_Peak_Amplitude", "EDA_Peak_Amplitude_Time",
"EDA_RiseTime", "EDA_RecoveryTime", "Label"
] for elem in np.array(eda_eventrelated.columns.values, dtype=str))
def test_eda_clean():
sampling_rate = 1000
eda = nk.eda_simulate(
duration=30,
sampling_rate=sampling_rate,
scr_number=6,
noise=0.01,
drift=0.01,
random_state=42,
)
clean = nk.eda_clean(eda, sampling_rate=sampling_rate)
assert len(clean) == len(eda)
# Comparison to biosppy (https://github.com/PIA-Group/BioSPPy/blob/master/biosppy/signals/eda.py)
eda_biosppy = nk.eda_clean(eda,
sampling_rate=sampling_rate,
method="biosppy")
original, _, _ = biosppy.tools.filter_signal(
signal=eda,
ftype="butter",
band="lowpass",
order=4,
frequency=5,
sampling_rate=sampling_rate,
)
original, _ = biosppy.tools.smoother(signal=original,
kernel="boxzen",
size=int(0.75 * sampling_rate),
mirror=True)
# pd.DataFrame({"our":eda_biosppy, "biosppy":original}).plot()
assert np.allclose((eda_biosppy - original).mean(), 0, atol=1e-5)
def test_eda_plot():
sampling_rate = 1000
eda = nk.eda_simulate(
duration=30,
sampling_rate=sampling_rate,
scr_number=6,
noise=0,
drift=0.01,
random_state=42,
)
eda_summary, _ = nk.eda_process(eda, sampling_rate=sampling_rate)
# Plot data over samples.
nk.eda_plot(eda_summary)
# This will identify the latest figure.
fig = plt.gcf()
assert len(fig.axes) == 3
titles = [
"Raw and Cleaned Signal",
"Skin Conductance Response (SCR)",
"Skin Conductance Level (SCL)",
]
for (ax, title) in zip(fig.get_axes(), titles):
assert ax.get_title() == title
assert fig.get_axes()[2].get_xlabel() == "Samples"
np.testing.assert_array_equal(fig.axes[0].get_xticks(),
fig.axes[1].get_xticks(),
fig.axes[2].get_xticks())
plt.close(fig)
# Plot data over seconds.
nk.eda_plot(eda_summary, sampling_rate=sampling_rate)
# This will identify the latest figure.
fig = plt.gcf()
assert fig.get_axes()[2].get_xlabel() == "Seconds"
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":
import numpy as np
import pandas as pd
import neurokit2 as nk
# =============================================================================
# Simulate physiological signals
# =============================================================================
# Generate synthetic signals
ecg = nk.ecg_simulate(duration=10, heart_rate=70)
rsp = nk.rsp_simulate(duration=10, respiratory_rate=15)
eda = nk.eda_simulate(duration=10, n_scr=3)
emg = nk.emg_simulate(duration=10, n_bursts=2)
# Visualise biosignals
data = pd.DataFrame({"ECG": ecg, "RSP": rsp, "EDA": eda, "EMG": emg})
data.plot(subplots=True, layout=(4, 1))
# Save it
plot = data.plot(subplots=True, layout=(4, 1))
plot[0][0].get_figure().savefig("README_simulation.png", dpi=300)
# =============================================================================
# Respiration (RSP) processing
# =============================================================================
# Generate one minute of respiratory signal
rsp = nk.rsp_simulate(duration=60, respiratory_rate=15)
# Process it
signals, info = nk.rsp_process(rsp)
import numpy as np
import pandas as pd
import neurokit2 as nk
# =============================================================================
# Example 1
# =============================================================================
# Generate synthetic signals
ecg = nk.ecg_simulate(duration=10, heart_rate=70)
rsp = nk.rsp_simulate(duration=10, respiratory_rate=15)
eda = nk.eda_simulate(duration=10, n_peaks=3)
emg = nk.emg_simulate(duration=10, n_bursts=2)
# Visualise biosignals
data = pd.DataFrame({"ECG": ecg,
"RSP": rsp,
"EDA": eda,
"EMG": emg})
data.plot(subplots=True, layout=(4, 1))
# Save it
plot = data.plot(subplots=True, layout=(4, 1))
plot[0][0].get_figure().savefig("README_simulation.png", dpi=300)
