def test_rsp_rrv():
rsp90 = nk.rsp_simulate(duration=60,
sampling_rate=1000,
respiratory_rate=90,
random_state=42)
rsp110 = nk.rsp_simulate(duration=60,
sampling_rate=1000,
respiratory_rate=110,
random_state=42)
cleaned90 = nk.rsp_clean(rsp90, sampling_rate=1000)
_, peaks90 = nk.rsp_peaks(cleaned90)
rsp_rate90 = nk.rsp_rate(peaks90, desired_length=len(rsp90))
cleaned110 = nk.rsp_clean(rsp110, sampling_rate=1000)
_, peaks110 = nk.rsp_peaks(cleaned110)
rsp_rate110 = nk.rsp_rate(peaks110, desired_length=len(rsp110))
rsp90_rrv = nk.rsp_rrv(rsp_rate90, peaks90)
rsp110_rrv = nk.rsp_rrv(rsp_rate110, peaks110)
assert np.array(rsp90_rrv["RRV_SDBB"]) < np.array(rsp110_rrv["RRV_SDBB"])
assert np.array(rsp90_rrv["RRV_RMSSD"]) < np.array(rsp110_rrv["RRV_RMSSD"])
assert np.array(rsp90_rrv["RRV_SDSD"]) < np.array(rsp110_rrv["RRV_SDSD"])
# assert np.array(rsp90_rrv["RRV_pNN50"]) == np.array(rsp110_rrv["RRV_pNN50"]) == np.array(rsp110_rrv["RRV_pNN20"]) == np.array(rsp90_rrv["RRV_pNN20"]) == 0
# assert np.array(rsp90_rrv["RRV_TINN"]) < np.array(rsp110_rrv["RRV_TINN"])
# assert np.array(rsp90_rrv["RRV_HTI"]) > np.array(rsp110_rrv["RRV_HTI"])
assert np.array(rsp90_rrv["RRV_HF"]) < np.array(rsp110_rrv["RRV_HF"])
assert np.array(rsp90_rrv["RRV_LF"]) < np.array(rsp110_rrv["RRV_LF"])
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)
def test_signal_rate(): # since singal_rate wraps signal_period, the latter is tested as well
# Test with array.
duration = 10
sampling_rate = 1000
signal = nk.signal_simulate(duration=duration, sampling_rate=sampling_rate, frequency=1)
info = nk.signal_findpeaks(signal)
rate = nk.signal_rate(peaks=info["Peaks"], sampling_rate=1000, desired_length=len(signal))
assert rate.shape[0] == duration * sampling_rate
# Test with dictionary.produced from signal_findpeaks.
assert info[list(info.keys())[0]].shape == (info["Peaks"].shape[0],)
# Test with DataFrame.
duration = 120
sampling_rate = 1000
rsp = nk.rsp_simulate(
duration=duration, sampling_rate=sampling_rate, respiratory_rate=15, method="sinuosoidal", noise=0
)
rsp_cleaned = nk.rsp_clean(rsp, sampling_rate=sampling_rate)
signals, info = nk.rsp_peaks(rsp_cleaned)
rate = nk.signal_rate(signals, sampling_rate=sampling_rate, desired_length=duration * sampling_rate)
assert rate.shape == (signals.shape[0],)
# Test with dictionary.produced from rsp_findpeaks.
rate = nk.signal_rate(info, sampling_rate=sampling_rate, desired_length=duration * sampling_rate)
assert rate.shape == (duration * sampling_rate,)
def test_signal_rate():
# Test with array.
signal = nk.signal_simulate(duration=10, sampling_rate=1000, frequency=1)
info = nk.signal_findpeaks(signal)
rate = nk.signal_rate(peaks=info["Peaks"],
sampling_rate=1000,
desired_length=None)
assert rate.shape[0] == len(info["Peaks"])
# Test with dictionary.produced from signal_findpeaks.
assert info[list(info.keys())[0]].shape == (info["Peaks"].shape[0], )
# Test with DataFrame.
rsp = nk.rsp_simulate(duration=120,
sampling_rate=1000,
respiratory_rate=15,
method="sinuosoidal",
noise=0)
rsp_cleaned = nk.rsp_clean(rsp, sampling_rate=1000)
signals, info = nk.rsp_peaks(rsp_cleaned)
rate = nk.signal_rate(signals, sampling_rate=1000)
assert rate.shape == (signals.shape[0], )
# Test with dictionary.produced from rsp_findpeaks.
test_length = 30
rate = nk.signal_rate(info, sampling_rate=1000, desired_length=test_length)
assert rate.shape == (test_length, )
def test_rsp_rrv():
rsp90 = nk.rsp_simulate(duration=60,
sampling_rate=1000,
respiratory_rate=90,
random_state=42)
rsp110 = nk.rsp_simulate(duration=60,
sampling_rate=1000,
respiratory_rate=110,
random_state=42)
cleaned90 = nk.rsp_clean(rsp90, sampling_rate=1000)
_, peaks90 = nk.rsp_peaks(cleaned90)
rsp_rate90 = nk.signal_rate(peaks90, desired_length=len(rsp90))
cleaned110 = nk.rsp_clean(rsp110, sampling_rate=1000)
_, peaks110 = nk.rsp_peaks(cleaned110)
rsp_rate110 = nk.signal_rate(peaks110, desired_length=len(rsp110))
rsp90_rrv = nk.rsp_rrv(rsp_rate90, peaks90)
rsp110_rrv = nk.rsp_rrv(rsp_rate110, peaks110)
assert np.array(rsp90_rrv["RRV_SDBB"]) < np.array(rsp110_rrv["RRV_SDBB"])
assert np.array(rsp90_rrv["RRV_RMSSD"]) < np.array(rsp110_rrv["RRV_RMSSD"])
assert np.array(rsp90_rrv["RRV_SDSD"]) < np.array(rsp110_rrv["RRV_SDSD"])
# assert np.array(rsp90_rrv["RRV_pNN50"]) == np.array(rsp110_rrv["RRV_pNN50"]) == np.array(rsp110_rrv["RRV_pNN20"]) == np.array(rsp90_rrv["RRV_pNN20"]) == 0
# assert np.array(rsp90_rrv["RRV_TINN"]) < np.array(rsp110_rrv["RRV_TINN"])
# assert np.array(rsp90_rrv["RRV_HTI"]) > np.array(rsp110_rrv["RRV_HTI"])
assert np.array(rsp90_rrv["RRV_HF"]) < np.array(rsp110_rrv["RRV_HF"])
assert np.isnan(rsp90_rrv["RRV_LF"][0])
assert np.isnan(rsp110_rrv["RRV_LF"][0])
# Test warning on too short duration
with pytest.warns(nk.misc.NeuroKitWarning,
match=r"The duration of recording is too short.*"):
short_rsp90 = nk.rsp_simulate(duration=10,
sampling_rate=1000,
respiratory_rate=90,
random_state=42)
short_cleaned90 = nk.rsp_clean(short_rsp90, sampling_rate=1000)
_, short_peaks90 = nk.rsp_peaks(short_cleaned90)
short_rsp_rate90 = nk.signal_rate(short_peaks90,
desired_length=len(short_rsp90))
nk.rsp_rrv(short_rsp_rate90, short_peaks90)
def test_rsp_clean():
sampling_rate = 1000
rsp = nk.rsp_simulate(duration=120,
sampling_rate=sampling_rate,
respiratory_rate=15,
random_state=42)
khodadad2018 = nk.rsp_clean(rsp, sampling_rate=1000, method="khodadad2018")
assert len(rsp) == len(khodadad2018)
rsp_biosppy = nk.rsp_clean(rsp, sampling_rate=1000, method="biosppy")
assert len(rsp) == len(rsp_biosppy)
# Check if filter was applied.
fft_raw = np.fft.rfft(rsp)
fft_khodadad2018 = np.fft.rfft(khodadad2018)
fft_biosppy = np.fft.rfft(rsp_biosppy)
freqs = np.fft.rfftfreq(len(rsp), 1 / sampling_rate)
# assert np.sum(fft_raw[freqs > 2]) > np.sum(fft_khodadad2018[freqs > 2])
assert np.sum(fft_raw[freqs > 2]) > np.sum(fft_biosppy[freqs > 2])
assert np.sum(fft_khodadad2018[freqs > 2]) > np.sum(fft_biosppy[freqs > 2])
# Check if detrending was applied.
assert np.mean(rsp) > np.mean(khodadad2018)
# 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)
def test_rsp_findpeaks():
rsp = nk.rsp_simulate(duration=120, sampling_rate=1000,
respiratory_rate=15)
rsp_cleaned = nk.rsp_clean(rsp, sampling_rate=1000)
signals, info = nk.rsp_findpeaks(rsp_cleaned, sampling_rate=1000)
assert signals.shape == (120000, 2)
assert signals["RSP_Peaks"].sum() == 28
assert signals["RSP_Troughs"].sum() == 28
assert info["RSP_Peaks"].shape[0] == 28
assert info["RSP_Troughs"].shape[0] == 28
# assert that extrema start with a trough and end with a peak
assert info["RSP_Peaks"][0] > info["RSP_Troughs"][0]
assert info["RSP_Peaks"][-1] > info["RSP_Troughs"][-1]
def test_rsp_peaks():
rsp = nk.rsp_simulate(duration=120, sampling_rate=1000,
respiratory_rate=15, random_state=42)
rsp_cleaned = nk.rsp_clean(rsp, sampling_rate=1000)
signals, info = nk.rsp_peaks(rsp_cleaned)
assert signals.shape == (120000, 2)
assert signals["RSP_Peaks"].sum() == 28
assert signals["RSP_Troughs"].sum() == 28
assert info["RSP_Peaks"].shape[0] == 28
assert info["RSP_Troughs"].shape[0] == 28
assert np.allclose(info["RSP_Peaks"].sum(), 1643817)
assert np.allclose(info["RSP_Troughs"].sum(), 1586588)
# Assert that extrema start with a trough and end with a peak.
assert info["RSP_Peaks"][0] > info["RSP_Troughs"][0]
assert info["RSP_Peaks"][-1] > info["RSP_Troughs"][-1]
def test_rsp_amplitude():
rsp = nk.rsp_simulate(duration=120, sampling_rate=1000,
respiratory_rate=15, method="sinusoidal", noise=0)
rsp_cleaned = nk.rsp_clean(rsp, sampling_rate=1000)
signals, info = nk.rsp_peaks(rsp_cleaned)
# Test with dictionary.
amplitude = nk.rsp_amplitude(rsp, signals)
assert amplitude.shape == (rsp.size, )
assert np.abs(amplitude.mean() - 1) < 0.01
# Test with DataFrame.
amplitude = nk.rsp_amplitude(rsp, info)
assert amplitude.shape == (rsp.size, )
assert np.abs(amplitude.mean() - 1) < 0.01
def respiration_clean(data, column='PZT'):
'''
# todo: does this function belong to dsu?
Parameters
----------
data
column
Returns
-------
'''
sampling_rate = estimate_rate(data)
data.loc[:, column] = nk.rsp_clean(data[column],
sampling_rate,
method='BioSPPy')
return data
def test_rsp_clean():
sampling_rate = 1000
rsp = nk.rsp_simulate(duration=120, sampling_rate=sampling_rate,
respiratory_rate=15)
signals = nk.rsp_clean(rsp, sampling_rate=1000)
assert signals.shape == (120000, 2)
# check if filter was applied
raw = signals["RSP_Raw"]
clean = signals["RSP_Filtered"]
fft_raw = np.fft.rfft(raw)
fft_clean = np.fft.rfft(clean)
freqs = np.fft.rfftfreq(len(raw), 1/sampling_rate)
assert np.sum(fft_raw[freqs > 2]) > np.sum(fft_clean[freqs > 2])
# check if detrending was applied
assert np.mean(raw) > np.mean(clean)
def test_rsp_rate():
rsp = nk.rsp_simulate(duration=120, sampling_rate=1000,
respiratory_rate=15, method="sinusoidal", noise=0)
rsp_cleaned = nk.rsp_clean(rsp, sampling_rate=1000)
signals, info = nk.rsp_peaks(rsp_cleaned)
# Test with dictionary.
test_length = 30
rate = nk.rsp_rate(peaks=info, sampling_rate=1000,
desired_length=test_length)
assert rate.shape == (test_length, )
assert np.abs(rate.mean() - 15) < 0.2
# Test with DataFrame.
rate = nk.rsp_rate(signals, sampling_rate=1000)
assert rate.shape == (signals.shape[0], )
assert np.abs(rate.mean() - 15) < 0.2
def test_rsp_rate():
rsp = nk.rsp_simulate(duration=120, sampling_rate=1000,
respiratory_rate=15)
rsp_cleaned = nk.rsp_clean(rsp, sampling_rate=1000)
signals, info = nk.rsp_findpeaks(rsp_cleaned, sampling_rate=1000)
# vary desired_lenght over tests
# test with peaks only
test_length = 30
data = nk.rsp_rate(info["RSP_Peaks"], sampling_rate=1000,
desired_length=test_length)
assert data.shape == (test_length, 2)
assert np.abs(data["RSP_Rate"].mean() - 15) < 0.2
assert np.abs(data["RSP_Period"].mean() - 4) < 0.1
# test with peaks and troughs passed in separately
test_length = 300
data = nk.rsp_rate(peaks=info["RSP_Peaks"], troughs=info["RSP_Troughs"],
sampling_rate=1000, desired_length=test_length)
assert data.shape == (test_length, 3)
assert np.abs(data["RSP_Rate"].mean() - 15) < 0.2
assert np.abs(data["RSP_Period"].mean() - 4) < 0.1
assert np.abs(data["RSP_Amplitude"].mean() - 2086) < 0.5
# test with DataFrame containing peaks and troughs
data = nk.rsp_rate(signals, sampling_rate=1000)
assert data.shape == (signals.shape[0], 3)
assert np.abs(data["RSP_Rate"].mean() - 15) < 0.2
assert np.abs(data["RSP_Period"].mean() - 4) < 0.1
assert np.abs(data["RSP_Amplitude"].mean() - 2087) < 0.5
# test with dict containing peaks and troughs
test_length = 30000
data = nk.rsp_rate(info, sampling_rate=1000, desired_length=test_length)
assert data.shape == (test_length, 3)
assert np.abs(data["RSP_Rate"].mean() - 15) < 0.2
assert np.abs(data["RSP_Period"].mean() - 4) < 0.1
assert np.abs(data["RSP_Amplitude"].mean() - 2087) < 0.5
