def test_signal_resample():
signal = np.cos(np.linspace(start=0, stop=20, num=50))
downsampled_interpolation = nk.signal_resample(signal,
method="interpolation",
sampling_rate=1000,
desired_sampling_rate=500)
downsampled_numpy = nk.signal_resample(signal,
method="numpy",
sampling_rate=1000,
desired_sampling_rate=500)
downsampled_pandas = nk.signal_resample(signal,
method="pandas",
sampling_rate=1000,
desired_sampling_rate=500)
downsampled_fft = nk.signal_resample(signal,
method="FFT",
sampling_rate=1000,
desired_sampling_rate=500)
downsampled_poly = nk.signal_resample(signal,
method="poly",
sampling_rate=1000,
desired_sampling_rate=500)
# Upsample
upsampled_interpolation = nk.signal_resample(downsampled_interpolation,
method="interpolation",
sampling_rate=500,
desired_sampling_rate=1000)
upsampled_numpy = nk.signal_resample(downsampled_numpy,
method="numpy",
sampling_rate=500,
desired_sampling_rate=1000)
upsampled_pandas = nk.signal_resample(downsampled_pandas,
method="pandas",
sampling_rate=500,
desired_sampling_rate=1000)
upsampled_fft = nk.signal_resample(downsampled_fft,
method="FFT",
sampling_rate=500,
desired_sampling_rate=1000)
upsampled_poly = nk.signal_resample(downsampled_poly,
method="poly",
sampling_rate=500,
desired_sampling_rate=1000)
# Check
rez = pd.DataFrame({
"Interpolation": upsampled_interpolation - signal,
"Numpy": upsampled_numpy - signal,
"Pandas": upsampled_pandas - signal,
"FFT": upsampled_fft - signal,
"Poly": upsampled_poly - signal
})
assert np.allclose(np.mean(rez.mean()), 0.0001, atol=0.0001)
def test_hrv_frequency():
# Test frequency domain
ecg1 = nk.ecg_simulate(duration=60,
sampling_rate=2000,
heart_rate=70,
random_state=42)
_, peaks1 = nk.ecg_process(ecg1, sampling_rate=2000)
hrv1 = nk.hrv_frequency(peaks1, sampling_rate=2000)
ecg2 = nk.signal_resample(ecg1,
sampling_rate=2000,
desired_sampling_rate=500)
_, peaks2 = nk.ecg_process(ecg2, sampling_rate=500)
hrv2 = nk.hrv_frequency(peaks2, sampling_rate=500)
assert np.allclose(hrv1["HRV_HF"] - hrv2["HRV_HF"], 0, atol=1.5)
assert np.isnan(hrv1["HRV_LF"][0])
assert np.isnan(hrv2["HRV_LF"][0])
assert np.isnan(hrv1["HRV_VLF"][0])
assert np.isnan(hrv2["HRV_LF"][0])
# Test warning on too short duration
with pytest.warns(nk.misc.NeuroKitWarning,
match=r"The duration of recording is too short.*"):
ecg3 = nk.ecg_simulate(duration=10,
sampling_rate=2000,
heart_rate=70,
random_state=42)
_, peaks3 = nk.ecg_process(ecg3, sampling_rate=2000)
nk.hrv_frequency(peaks3, sampling_rate=2000, silent=False)
def resampling_data_signal(data: Dict[str, Dict[str, np.array]], sampling_rate: int, desired_sampling_rate: int, method: str = 'interpolation') -> Dict[str, Dict[str, object]]:
output = defaultdict(dict)
for participant_id, dataset in data.items():
for task_id, signal_data in dataset.items():
resampled_signal = nk.signal_resample(signal_data, method = method, sampling_rate = sampling_rate, desired_sampling_rate = desired_sampling_rate)
output[participant_id][task_id] = resampled_signal
return output
def process_eda(eda, show_fig=False):
"""
Resample EDA signal from 4 Hz to 64 Hz.
Compute EDA signal features (more info: https://neurokit2.readthedocs.io/en/latest/functions.html#module-neurokit2.eda).
Parameters
----------
eda : dict [timestamp : value]
EDA signal.
show_fig : bool
set if plot specific features.
Returns
-------
eda_signals : DataFrame
eda_info : dict
"""
eda_signal = nk.signal_resample(eda['value'],
sampling_rate=4,
desired_sampling_rate=64)
eda_signals, eda_info = nk.eda_process(eda_signal, sampling_rate=64)
if show_fig:
plt.plot(eda_signals['EDA_Phasic'], label='fázická složka')
plt.plot(eda_signals['EDA_Tonic'], label='tónická složka')
plt.plot(eda_signals['EDA_Raw'], label='původní')
plt.xlabel('Vzorek [n]')
plt.ylabel('EDA [uS]')
plt.legend()
plt.show()
return eda_signals, eda_info
def test_ecg_hrv():
ecg_slow = nk.ecg_simulate(duration=60,
sampling_rate=1000,
heart_rate=70,
random_state=42)
ecg_fast = nk.ecg_simulate(duration=60,
sampling_rate=1000,
heart_rate=110,
random_state=42)
ecg_slow, _ = nk.ecg_process(ecg_slow, sampling_rate=1000)
ecg_fast, _ = nk.ecg_process(ecg_fast, sampling_rate=1000)
ecg_slow_hrv = nk.ecg_hrv(ecg_slow, sampling_rate=1000)
ecg_fast_hrv = nk.ecg_hrv(ecg_fast, sampling_rate=1000)
assert ecg_fast_hrv["HRV_RMSSD"][0] < ecg_slow_hrv["HRV_RMSSD"][0]
assert ecg_fast_hrv["HRV_MeanNN"][0] < ecg_slow_hrv["HRV_MeanNN"][0]
assert ecg_fast_hrv["HRV_SDNN"][0] < ecg_slow_hrv["HRV_SDNN"][0]
assert ecg_fast_hrv["HRV_CVNN"][0] < ecg_slow_hrv["HRV_CVNN"][0]
assert ecg_fast_hrv["HRV_CVSD"][0] < ecg_slow_hrv["HRV_CVSD"][0]
assert ecg_fast_hrv["HRV_MedianNN"][0] < ecg_slow_hrv["HRV_MedianNN"][0]
assert ecg_fast_hrv["HRV_MadNN"][0] < ecg_slow_hrv["HRV_MadNN"][0]
assert ecg_fast_hrv["HRV_MCVNN"][0] < ecg_slow_hrv["HRV_MCVNN"][0]
assert ecg_fast_hrv["HRV_pNN50"][0] == ecg_slow_hrv["HRV_pNN50"][0]
assert ecg_fast_hrv["HRV_pNN20"][0] < ecg_slow_hrv["HRV_pNN20"][0]
assert ecg_fast_hrv["HRV_TINN"][0] < ecg_slow_hrv["HRV_TINN"][0]
assert ecg_fast_hrv["HRV_HTI"][0] > ecg_slow_hrv["HRV_HTI"][0]
assert ecg_fast_hrv["HRV_ULF"][0] == ecg_slow_hrv["HRV_ULF"][0] == 0
assert all(elem in [
'HRV_RMSSD', 'HRV_MeanNN', 'HRV_SDNN', 'HRV_SDSD', 'HRV_CVNN',
'HRV_CVSD', 'HRV_MedianNN', 'HRV_MadNN', 'HRV_MCVNN', 'HRV_pNN50',
'HRV_pNN20', 'HRV_TINN', 'HRV_HTI', 'HRV_ULF', 'HRV_VLF', 'HRV_LF',
'HRV_HF', 'HRV_VHF', 'HRV_LFHF', 'HRV_LFn', 'HRV_HFn', 'HRV_LnHF',
'HRV_SD1', 'HRV_SD2', 'HRV_SD2SD1', 'HRV_CSI', 'HRV_CVI',
'HRV_CSI_Modified', 'HRV_SampEn'
] for elem in np.array(ecg_fast_hrv.columns.values, dtype=str))
# Test frequency domain
ecg1 = nk.ecg_simulate(duration=60,
sampling_rate=2000,
heart_rate=70,
random_state=42)
hrv1 = nk.ecg_hrv(nk.ecg_process(ecg1, sampling_rate=2000)[0],
sampling_rate=2000)
ecg2 = nk.signal_resample(ecg1,
sampling_rate=2000,
desired_sampling_rate=500)
hrv2 = nk.ecg_hrv(nk.ecg_process(ecg2, sampling_rate=500)[0],
sampling_rate=500)
assert np.allclose(np.mean(hrv1[["HRV_HF", "HRV_LF", "HRV_VLF"]].iloc[0] -
hrv2[["HRV_HF", "HRV_LF", "HRV_VLF"]].iloc[0]),
0,
atol=1)
def test_hrv_frequency():
# Test frequency domain
ecg1 = nk.ecg_simulate(duration=60,
sampling_rate=2000,
heart_rate=70,
random_state=42)
_, peaks1 = nk.ecg_process(ecg1, sampling_rate=2000)
hrv1 = nk.hrv_frequency(peaks1, sampling_rate=2000)
ecg2 = nk.signal_resample(ecg1,
sampling_rate=2000,
desired_sampling_rate=500)
_, peaks2 = nk.ecg_process(ecg2, sampling_rate=500)
hrv2 = nk.hrv_frequency(peaks2, sampling_rate=500)
assert np.allclose(hrv1["HRV_HF"] - hrv2["HRV_HF"], 0, atol=1.5)
assert np.isnan(hrv1["HRV_LF"][0])
assert np.isnan(hrv2["HRV_LF"][0])
assert np.isnan(hrv1["HRV_VLF"][0])
assert np.isnan(hrv2["HRV_LF"][0])