def test_signal_distort():
signal = nk.signal_simulate(duration=10, frequency=0.5, sampling_rate=10)
# Warning for nyquist criterion
with pytest.warns(
nk.misc.NeuroKitWarning,
match=r"Skipping requested noise frequency.*cannot be resolved.*"
):
nk.signal_distort(signal, sampling_rate=10, noise_amplitude=1, silent=False)
# Warning for period duration
with pytest.warns(
nk.misc.NeuroKitWarning,
match=r"Skipping requested noise frequency.*since its period of.*"
):
signal = nk.signal_simulate(duration=1, frequency=1, sampling_rate=10)
nk.signal_distort(signal, noise_amplitude=1, noise_frequency=0.1, silent=False)
def test_rsp_clean():
sampling_rate = 100
duration = 120
rsp = nk.rsp_simulate(duration=duration,
sampling_rate=sampling_rate,
respiratory_rate=15,
noise=0.1,
random_state=42)
# Add linear drift (to test baseline removal).
rsp += nk.signal_distort(rsp,
sampling_rate=sampling_rate,
linear_drift=True)
khodadad2018 = nk.rsp_clean(rsp,
sampling_rate=sampling_rate,
method="khodadad2018")
assert len(rsp) == len(khodadad2018)
rsp_biosppy = nk.rsp_clean(rsp,
sampling_rate=sampling_rate,
method="biosppy")
assert len(rsp) == len(rsp_biosppy)
# Check if filter was applied.
fft_raw = np.abs(np.fft.rfft(rsp))
fft_khodadad2018 = np.abs(np.fft.rfft(khodadad2018))
fft_biosppy = np.abs(np.fft.rfft(rsp_biosppy))
freqs = np.fft.rfftfreq(len(rsp), 1 / sampling_rate)
assert np.sum(fft_raw[freqs > 3]) > np.sum(fft_khodadad2018[freqs > 3])
assert np.sum(fft_raw[freqs < 0.05]) > np.sum(
fft_khodadad2018[freqs < 0.05])
assert np.sum(fft_raw[freqs > 0.35]) > np.sum(fft_biosppy[freqs > 0.35])
assert np.sum(fft_raw[freqs < 0.1]) > np.sum(fft_biosppy[freqs < 0.1])
# Comparison to biosppy (https://github.com/PIA-Group/BioSPPy/blob/master/biosppy/signals/resp.py#L62)
rsp_biosppy = nk.rsp_clean(rsp,
sampling_rate=sampling_rate,
method="biosppy")
original, _, _ = biosppy.tools.filter_signal(signal=rsp,
ftype="butter",
band="bandpass",
order=2,
frequency=[0.1, 0.35],
sampling_rate=sampling_rate)
original = nk.signal_detrend(original, order=0)
assert np.allclose((rsp_biosppy - original).mean(), 0, atol=1e-6)
plot = nk.eog_plot(signals, peaks=info, sampling_rate=100)
plot.set_size_inches(10, 6, forward=True)
plot.savefig("README_eog.png", dpi=300, h_pad=3)
# =============================================================================
# Signal Processing
# =============================================================================
# Generate original signal
original = nk.signal_simulate(duration=6, frequency=1)
# Distort the signal (add noise, linear trend, artifacts etc.)
distorted = nk.signal_distort(original,
noise_amplitude=0.1,
noise_frequency=[5, 10, 20],
powerline_amplitude=0.05,
artifacts_amplitude=0.3,
artifacts_number=3,
linear_drift=0.5)
# Clean (filter and detrend)
cleaned = nk.signal_detrend(distorted)
cleaned = nk.signal_filter(cleaned, lowcut=0.5, highcut=1.5)
# Compare the 3 signals
plot = nk.signal_plot([original, distorted, cleaned])
# Save plot
fig = plt.gcf()
fig.set_size_inches(10, 6)
fig.savefig("README_signalprocessing.png", dpi=300, h_pad=3)