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tests_rsp.py
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tests_rsp.py
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# -*- coding: utf-8 -*-
import biosppy
import matplotlib.pyplot as plt
import numpy as np
import pytest
import neurokit2 as nk
def test_rsp_simulate():
rsp1 = nk.rsp_simulate(duration=20, length=3000, random_state=42)
assert len(rsp1) == 3000
rsp2 = nk.rsp_simulate(duration=20, length=3000, respiratory_rate=80, random_state=42)
# pd.DataFrame({"RSP1":rsp1, "RSP2":rsp2}).plot()
# pd.DataFrame({"RSP1":rsp1, "RSP2":rsp2}).hist()
assert len(nk.signal_findpeaks(rsp1, height_min=0.2)["Peaks"]) < len(
nk.signal_findpeaks(rsp2, height_min=0.2)["Peaks"]
)
rsp3 = nk.rsp_simulate(duration=20, length=3000, method="sinusoidal", random_state=42)
rsp4 = nk.rsp_simulate(duration=20, length=3000, method="breathmetrics", random_state=42)
# pd.DataFrame({"RSP3":rsp3, "RSP4":rsp4}).plot()
assert len(nk.signal_findpeaks(rsp3, height_min=0.2)["Peaks"]) > len(
nk.signal_findpeaks(rsp4, height_min=0.2)["Peaks"]
)
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_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_process():
rsp = nk.rsp_simulate(duration=120, sampling_rate=1000, respiratory_rate=15)
signals, info = nk.rsp_process(rsp, sampling_rate=1000)
# Only check array dimensions since functions called by rsp_process have
# already been unit tested.
assert signals.shape == (120000, 8)
assert (
np.array(
[
"RSP_Raw",
"RSP_Clean",
"RSP_Amplitude",
"RSP_Rate",
"RSP_Phase",
"RSP_PhaseCompletion",
"RSP_Peaks",
"RSP_Troughs",
]
)
in signals.columns.values
)
def test_rsp_plot():
rsp = nk.rsp_simulate(duration=120, sampling_rate=1000, respiratory_rate=15)
rsp_summary, _ = nk.rsp_process(rsp, sampling_rate=1000)
nk.rsp_plot(rsp_summary)
# This will identify the latest figure.
fig = plt.gcf()
assert len(fig.axes) == 3
titles = ["Raw and Cleaned Signal", "Breathing Rate", "Breathing Amplitude"]
for (ax, title) in zip(fig.get_axes(), titles):
assert ax.get_title() == title
plt.close(fig)
def test_rsp_eventrelated():
rsp, info = nk.rsp_process(nk.rsp_simulate(duration=30, random_state=42))
epochs = nk.epochs_create(rsp, events=[5000, 10000, 15000], epochs_start=-0.1, epochs_end=1.9)
rsp_eventrelated = nk.rsp_eventrelated(epochs)
# Test rate features
assert np.alltrue(np.array(rsp_eventrelated["RSP_Rate_Min"]) < np.array(rsp_eventrelated["RSP_Rate_Mean"]))
assert np.alltrue(np.array(rsp_eventrelated["RSP_Rate_Mean"]) < np.array(rsp_eventrelated["RSP_Rate_Max"]))
# Test amplitude features
assert np.alltrue(
np.array(rsp_eventrelated["RSP_Amplitude_Min"]) < np.array(rsp_eventrelated["RSP_Amplitude_Mean"])
)
assert np.alltrue(
np.array(rsp_eventrelated["RSP_Amplitude_Mean"]) < np.array(rsp_eventrelated["RSP_Amplitude_Max"])
)
assert len(rsp_eventrelated["Label"]) == 3
# Test warning on missing columns
with pytest.warns(nk.misc.NeuroKitWarning, match=r".*does not have an `RSP_Amplitude`.*"):
first_epoch_key = list(epochs.keys())[0]
first_epoch_copy = epochs[first_epoch_key].copy()
del first_epoch_copy["RSP_Amplitude"]
nk.rsp_eventrelated({**epochs, first_epoch_key: first_epoch_copy})
with pytest.warns(nk.misc.NeuroKitWarning, match=r".*does not have an `RSP_Phase`.*"):
first_epoch_key = list(epochs.keys())[0]
first_epoch_copy = epochs[first_epoch_key].copy()
del first_epoch_copy["RSP_Phase"]
nk.rsp_eventrelated({**epochs, first_epoch_key: first_epoch_copy})
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)
# assert all(elem in ['RRV_SDBB','RRV_RMSSD', 'RRV_SDSD'
# 'RRV_VLF', 'RRV_LF', 'RRV_HF', 'RRV_LFHF',
# 'RRV_LFn', 'RRV_HFn',
# 'RRV_SD1', 'RRV_SD2', 'RRV_SD2SD1','RRV_ApEn', 'RRV_SampEn', 'RRV_DFA']
# for elem in np.array(rsp110_rrv.columns.values, dtype=str))
def test_rsp_intervalrelated():
data = nk.data("bio_resting_5min_100hz")
df, info = nk.rsp_process(data["RSP"], sampling_rate=100)
# Test with signal dataframe
features_df = nk.rsp_intervalrelated(df)
assert features_df.shape[0] == 1 # Number of rows
# Test with dict
epochs = nk.epochs_create(df, events=[0, 15000], sampling_rate=100, epochs_end=150)
features_dict = nk.rsp_intervalrelated(epochs)
assert features_dict.shape[0] == 2 # Number of rows