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_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_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 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 respiration_sequence_features(data,
events,
column='PZT',
known_sequences=None):
# nk.rsp_peaks(pzt_signal['PZT'].values, sampling_rate=sampling_rate, method="BioSPPy")
sampling_rate = estimate_rate(data)
# Extract peak using neurokit BioSPPy method
_index = data.index
_, info = nk.rsp_peaks(data[column],
sampling_rate=sampling_rate,
method="BioSPPy")
# Truncate so that first and last events are troughs
RSP_Troughs = info['RSP_Troughs']
RSP_Peaks = info['RSP_Peaks']
# at this point, check that there are some peaks/trough
if RSP_Troughs.size == 0 or RSP_Peaks.size == 0:
raise NoRespirationPeaks(
'No peaks/trough could be detected in PZT signal. ')
RSP_Peaks = RSP_Peaks[(RSP_Peaks > RSP_Troughs[0])
& (RSP_Peaks <= RSP_Troughs[-1])]
# Estimate Inspiration and Expiration durations, cycle (I+E) durations and amplitude
I_duration = (RSP_Peaks - RSP_Troughs[:-1]) / sampling_rate
E_duration = (RSP_Troughs[1:] - RSP_Peaks) / sampling_rate
IE_duration = np.ediff1d(RSP_Troughs) / sampling_rate
amplitude = data.iloc[RSP_Peaks].values - data.iloc[
RSP_Troughs[:-1]].values
IE_ratio = I_duration / E_duration
# Back to Dataframe format to allow extracting feature between events
I_duration_df = pd.DataFrame(index=_index[RSP_Peaks],
columns=['I_duration'],
data=I_duration)
E_duration_df = pd.DataFrame(index=_index[RSP_Troughs[1:]],
columns=['E_duration'],
data=E_duration)
IE_duration_df = pd.DataFrame(index=_index[RSP_Troughs[1:]],
columns=['IE_duration'],
data=IE_duration)
amplitude_df = pd.DataFrame(index=_index[RSP_Peaks],
columns=['IE_amplitude'],
data=amplitude)
IE_ratio_df = pd.DataFrame(index=_index[RSP_Peaks],
columns=['IE_ratio'],
data=IE_ratio)
cycles_df = pd.concat([
I_duration_df, E_duration_df, IE_duration_df, amplitude_df, IE_ratio_df
],
axis=1)
known_sequences = known_sequences or VALID_SEQUENCE_KEYS
features = []
# for name, row in events.T.iterrows(): # transpose due to https://github.com/OpenMindInnovation/iguazu/issues/54
for index, row in events.iterrows():
logger.debug('Processing sequence %s at %s', row.id, index)
if row.id not in known_sequences:
continue
begin = row.begin
end = row.end
cycles_sequence = cycles_df.loc[begin:end].copy()
# extract features on sequence
sequence_features = dataclass_to_dataframe(
respiration_features(cycles_sequence)).rename_axis(
index='id').reset_index()
sequence_features.insert(0, 'reference', row.id)
features.append(sequence_features)
if len(features) > 0:
features = pd.concat(features,
axis='index',
ignore_index=True,
sort=False)
logger.info('Generated a feature dataframe of shape %s',
features.shape)
else:
logger.info('No features were generated')
features = pd.DataFrame(columns=['id', 'reference', 'value'])
return features
